






"""simple docstring"""


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowercase_				     =     logging.get_logger(__name__)

lowercase_				     =     '▁'

lowercase_				     =     {'vocab_file': 'sentencepiece.bpe.model'}

lowercase_				     =     {
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

lowercase_				     =     {
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
lowercase_				     =     ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']


class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:	Tuple						 = VOCAB_FILES_NAMES
   A_						:	List[str]						 = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_						:	Tuple						 = PRETRAINED_VOCAB_FILES_MAP
   A_						:	Dict						 = ["input_ids", "attention_mask"]

   A_						:	List[int]						 = []
   A_						:	List[int]						 = []


   def __init__(	self :     Dict,       _lowerCamelCase :     Any,       _lowerCamelCase :     List[Any]=None,       _lowerCamelCase :     List[Any]=None,       _lowerCamelCase :     Optional[Any]="</s>",       _lowerCamelCase :     Tuple="</s>",       _lowerCamelCase :     Dict="<s>",       _lowerCamelCase :     Optional[Any]="<unk>",       _lowerCamelCase :     Optional[int]="<pad>",       _lowerCamelCase :     Optional[int]="<mask>",       _lowerCamelCase :     Optional[Dict[str, Any]] = None,       **_lowerCamelCase :     Dict,       ):

     '''simple docstring'''
     # Mask token behave like a normal word, i.e. include the space before it
     __A									= AddedToken(_lowerCamelCase,       lstrip=_lowerCamelCase,       rstrip=_lowerCamelCase  ) if isinstance(_lowerCamelCase,       _lowerCamelCase  ) else mask_token

     __A									= {} if sp_model_kwargs is None else sp_model_kwargs

     __A									= kwargs.get('''additional_special_tokens''',       []  )
     kwargs["additional_special_tokens"] += [
         code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
     ]

     super().__init__(
         src_lang=_lowerCamelCase,       tgt_lang=_lowerCamelCase,       eos_token=_lowerCamelCase,       unk_token=_lowerCamelCase,       sep_token=_lowerCamelCase,       cls_token=_lowerCamelCase,       pad_token=_lowerCamelCase,       mask_token=_lowerCamelCase,       sp_model_kwargs=self.sp_model_kwargs,       **_lowerCamelCase,       )

     __A									= spm.SentencePieceProcessor(**self.sp_model_kwargs  )
     self.sp_model.Load(str(_lowerCamelCase  )  )
     __A									= vocab_file

     # Original fairseq vocab and spm vocab must be "aligned":
     # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
     # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
     # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
     # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

     # Mimic fairseq token-to-id alignment for the first 4 token
     __A									= {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

     # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
     __A									= 1

     __A									= len(self.sp_model  )
     __A									= {
         code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_lowerCamelCase  )
     }
     __A									= {v: k for k, v in self.lang_code_to_id.items()}
     __A									= len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset

     self.fairseq_tokens_to_ids.update(self.lang_code_to_id  )
     __A									= {v: k for k, v in self.fairseq_tokens_to_ids.items()}

     __A									= src_lang if src_lang is not None else '''en_XX'''
     __A									= self.lang_code_to_id[self._src_lang]
     __A									= tgt_lang
     self.set_src_lang_special_tokens(self._src_lang  )


   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     return len(self.sp_model  ) + len(self.lang_code_to_id  ) + self.fairseq_offset + 1  # Plus 1 for the mask token


   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     return self._src_lang


   @src_lang.setter
   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any],       _lowerCamelCase :     str  ):

     '''simple docstring'''
     __A									= new_src_lang
     self.set_src_lang_special_tokens(self._src_lang  )


   def __getstate__(	self :     Dict  ):

     '''simple docstring'''
     __A									= self.__dict__.copy()
     __A									= None
     return state


   def __setstate__(	self :     Union[str, Any],       _lowerCamelCase :     Dict  ):

     '''simple docstring'''
     __A									= d

     # for backward compatibility
     if not hasattr(self,       '''sp_model_kwargs'''  ):
       __A									= {}

     __A									= spm.SentencePieceProcessor(**self.sp_model_kwargs  )
     self.sp_model.Load(self.vocab_file  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= {self.convert_ids_to_tokens(_lowerCamelCase  ): i for i in range(self.vocab_size  )}
     vocab.update(self.added_tokens_encoder  )
     return vocab


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any,       _lowerCamelCase :     str  ):

     '''simple docstring'''
     return self.sp_model.encode(_lowerCamelCase,       out_type=_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any,       _lowerCamelCase :     str  ):

     '''simple docstring'''
     if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
     __A									= self.sp_model.PieceToId(_lowerCamelCase  )

     # Need to return unknown token if the SP model returned 0
     return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     int  ):

     '''simple docstring'''
     if index in self.fairseq_ids_to_tokens:
       return self.fairseq_ids_to_tokens[index]
     return self.sp_model.IdToPiece(index - self.fairseq_offset  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any],       _lowerCamelCase :     Optional[Any]  ):

     '''simple docstring'''
     __A									= []
     __A									= ''''''
     __A									= False
     for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
         if not prev_is_special:
           out_string += " "
         out_string += self.sp_model.decode(_lowerCamelCase  ) + token
         __A									= True
         __A									= []
       else:
         current_sub_tokens.append(_lowerCamelCase  )
         __A									= False
     out_string += self.sp_model.decode(_lowerCamelCase  )
     return out_string.strip()


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int],       _lowerCamelCase :     str,       _lowerCamelCase :     Optional[str] = None  ):

     '''simple docstring'''
     if not os.path.isdir(_lowerCamelCase  ):
       logger.error(f'Vocabulary path ({save_directory}) should be a directory'  )
       return
     __A									= os.path.join(
         _lowerCamelCase,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

     if os.path.abspath(self.vocab_file  ) != os.path.abspath(_lowerCamelCase  ) and os.path.isfile(self.vocab_file  ):
       copyfile(self.vocab_file,       _lowerCamelCase  )
     elif not os.path.isfile(self.vocab_file  ):
       with open(_lowerCamelCase,       '''wb'''  ) as fi:
         __A									= self.sp_model.serialized_model_proto()
         fi.write(_lowerCamelCase  )

     return (out_vocab_file,)


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     List[int],       _lowerCamelCase :     Optional[List[int]] = None,       _lowerCamelCase :     bool = False  ):

     '''simple docstring'''

     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=_lowerCamelCase,       token_ids_a=_lowerCamelCase,       already_has_special_tokens=_lowerCamelCase  )

     __A									= [1] * len(self.prefix_tokens  )
     __A									= [1] * len(self.suffix_tokens  )
     if token_ids_a is None:
       return prefix_ones + ([0] * len(_lowerCamelCase  )) + suffix_ones
     return prefix_ones + ([0] * len(_lowerCamelCase  )) + ([0] * len(_lowerCamelCase  )) + suffix_ones


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     List[int],       _lowerCamelCase :     Optional[List[int]] = None  ):

     '''simple docstring'''
     if token_ids_a is None:
       return self.prefix_tokens + token_ids_a + self.suffix_tokens
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any],       _lowerCamelCase :     List[Any],       _lowerCamelCase :     str,       _lowerCamelCase :     Optional[str],       _lowerCamelCase :     Optional[str],       **_lowerCamelCase :     Dict  ):

     '''simple docstring'''
     if src_lang is None or tgt_lang is None:
       raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''  )
     __A									= src_lang
     __A									= self(_lowerCamelCase,       add_special_tokens=_lowerCamelCase,       return_tensors=_lowerCamelCase,       **_lowerCamelCase  )
     __A									= self.convert_tokens_to_ids(_lowerCamelCase  )
     __A									= tgt_lang_id
     return inputs


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any],       _lowerCamelCase :     List[str],       _lowerCamelCase :     str = "en_XX",       _lowerCamelCase :     Optional[List[str]] = None,       _lowerCamelCase :     str = "ro_RO",       **_lowerCamelCase :     Dict,       ):

     '''simple docstring'''
     __A									= src_lang
     __A									= tgt_lang
     return super().prepare_seqaseq_batch(_lowerCamelCase,       _lowerCamelCase,       **_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     return self.set_src_lang_special_tokens(self.src_lang  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     return self.set_tgt_lang_special_tokens(self.tgt_lang  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int],       _lowerCamelCase :     str  ):

     '''simple docstring'''
     __A									= self.lang_code_to_id[src_lang]
     __A									= [self.cur_lang_code_id]
     __A									= [self.eos_token_id]






   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any],       _lowerCamelCase :     str  ):

     '''simple docstring'''
     __A									= self.lang_code_to_id[tgt_lang]
     __A									= [self.cur_lang_code_id]
     __A									= [self.eos_token_id]












"""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 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(	self :     List[Any],       _lowerCamelCase :     int,       _lowerCamelCase :     List[Any]=7,       _lowerCamelCase :     int=3,       _lowerCamelCase :     Optional[Any]=18,       _lowerCamelCase :     Any=30,       _lowerCamelCase :     str=4_00,       _lowerCamelCase :     int=True,       _lowerCamelCase :     Union[str, Any]=None,       _lowerCamelCase :     str=True,       ):

     '''simple docstring'''
     __A									= size if size is not None else {'''height''': 18, '''width''': 18}
     __A									= parent
     __A									= batch_size
     __A									= num_channels
     __A									= image_size
     __A									= min_resolution
     __A									= max_resolution
     __A									= do_resize
     __A									= size
     __A									= apply_ocr






   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}





@require_torch
@require_pytesseract
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	Optional[int]						 = LayoutLMvaImageProcessor if is_pytesseract_available() else None


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= LayoutLMvaImageProcessingTester(self  )


   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     return self.image_processor_tester.prepare_image_processor_dict()


   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     __A									= 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 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= self.image_processing_class.from_dict(self.image_processor_dict  )
     self.assertEqual(image_processor.size,       {'''height''': 18, '''width''': 18}  )

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


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     pass


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     # Initialize image_processing
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A									= prepare_image_inputs(self.image_processor_tester,       equal_resolution=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       Image.Image  )

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

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

     # Test batched
     __A									= 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 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     # Initialize image_processing
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random numpy tensors
     __A									= 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
     __A									= image_processing(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processing(_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 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     # Initialize image_processing
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PyTorch tensors
     __A									= 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
     __A									= image_processing(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['''height'''],
             self.image_processor_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processing(_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 				_SCREAMING_SNAKE_CASE				(	self :     List[str]  ):

     '''simple docstring'''
     # with apply_OCR = True
     __A									= LayoutLMvaImageProcessor()

     from datasets import load_dataset

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

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

     __A									= image_processing(_lowerCamelCase,       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
     __A									= [['''11:14''', '''to''', '''11:39''', '''a.m''', '''11:39''', '''to''', '''11:44''', '''a.m.''', '''11:44''', '''a.m.''', '''to''', '''12:25''', '''p.m.''', '''12:25''', '''to''', '''12:58''', '''p.m.''', '''12:58''', '''to''', '''4:00''', '''p.m.''', '''2:00''', '''to''', '''5:00''', '''p.m.''', '''Coffee''', '''Break''', '''Coffee''', '''will''', '''be''', '''served''', '''for''', '''men''', '''and''', '''women''', '''in''', '''the''', '''lobby''', '''adjacent''', '''to''', '''exhibit''', '''area.''', '''Please''', '''move''', '''into''', '''exhibit''', '''area.''', '''(Exhibits''', '''Open)''', '''TRRF''', '''GENERAL''', '''SESSION''', '''(PART''', '''|)''', '''Presiding:''', '''Lee''', '''A.''', '''Waller''', '''TRRF''', '''Vice''', '''President''', '''“Introductory''', '''Remarks”''', '''Lee''', '''A.''', '''Waller,''', '''TRRF''', '''Vice''', '''Presi-''', '''dent''', '''Individual''', '''Interviews''', '''with''', '''TRRF''', '''Public''', '''Board''', '''Members''', '''and''', '''Sci-''', '''entific''', '''Advisory''', '''Council''', '''Mem-''', '''bers''', '''Conducted''', '''by''', '''TRRF''', '''Treasurer''', '''Philip''', '''G.''', '''Kuehn''', '''to''', '''get''', '''answers''', '''which''', '''the''', '''public''', '''refrigerated''', '''warehousing''', '''industry''', '''is''', '''looking''', '''for.''', '''Plus''', '''questions''', '''from''', '''the''', '''floor.''', '''Dr.''', '''Emil''', '''M.''', '''Mrak,''', '''University''', '''of''', '''Cal-''', '''ifornia,''', '''Chairman,''', '''TRRF''', '''Board;''', '''Sam''', '''R.''', '''Cecil,''', '''University''', '''of''', '''Georgia''', '''College''', '''of''', '''Agriculture;''', '''Dr.''', '''Stanley''', '''Charm,''', '''Tufts''', '''University''', '''School''', '''of''', '''Medicine;''', '''Dr.''', '''Robert''', '''H.''', '''Cotton,''', '''ITT''', '''Continental''', '''Baking''', '''Company;''', '''Dr.''', '''Owen''', '''Fennema,''', '''University''', '''of''', '''Wis-''', '''consin;''', '''Dr.''', '''Robert''', '''E.''', '''Hardenburg,''', '''USDA.''', '''Questions''', '''and''', '''Answers''', '''Exhibits''', '''Open''', '''Capt.''', '''Jack''', '''Stoney''', '''Room''', '''TRRF''', '''Scientific''', '''Advisory''', '''Council''', '''Meeting''', '''Ballroom''', '''Foyer''']]  # noqa: E231
     __A									= [[[1_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,       _lowerCamelCase  )
     self.assertListEqual(encoding.boxes,       _lowerCamelCase  )

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

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

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




