import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


A__     =				get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
			from transformers.models.mbart.modeling_mbart import shift_tokens_right

A__     =				250_004
A__     =				250_020
@require_sentencepiece
@require_tokenizers
class 	_lowerCAmelCase		(  _SCREAMING_SNAKE_CASE   , unittest.TestCase					):
							_UpperCAmelCase         =  MBartTokenizer
							_UpperCAmelCase         =  MBartTokenizerFast
							_UpperCAmelCase         =  True
							_UpperCAmelCase         =  True







							def 					snake_case	(					self  :     Optional[int]		):
										super().setUp()

										# We have a SentencePiece fixture for testing
										lowerCamelCase    :str    	=  MBartTokenizer(__snake_case						,       keep_accents=__snake_case		)
										tokenizer.save_pretrained(self.tmpdirname		)







							def 					snake_case	(					self  :     str		):
										lowerCamelCase    :int    	=  MBartTokenizer(__snake_case						,       keep_accents=__snake_case		)

										lowerCamelCase    :str    	=  tokenizer.tokenize('''This is a test'''		)
										self.assertListEqual(__snake_case						,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']		)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(__snake_case		)						,       [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]						,       )

										lowerCamelCase    :Any    	=  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''		)
										self.assertListEqual(
										    __snake_case						,       [
										        SPIECE_UNDERLINE + '''I''',
										        SPIECE_UNDERLINE + '''was''',
										        SPIECE_UNDERLINE + '''b''',
										        '''or''',
										        '''n''',
										        SPIECE_UNDERLINE + '''in''',
										        SPIECE_UNDERLINE + '''''',
										        '''9''',
										        '''2''',
										        '''0''',
										        '''0''',
										        '''0''',
										        ''',''',
										        SPIECE_UNDERLINE + '''and''',
										        SPIECE_UNDERLINE + '''this''',
										        SPIECE_UNDERLINE + '''is''',
										        SPIECE_UNDERLINE + '''f''',
										        '''al''',
										        '''s''',
										        '''é''',
										        '''.''',
										    ]						,       )
										lowerCamelCase    :List[str]    	=  tokenizer.convert_tokens_to_ids(__snake_case		)
										self.assertListEqual(
										    __snake_case						,       [
										        value + tokenizer.fairseq_offset
										        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
										        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
										    ]						,       )

										lowerCamelCase    :Optional[Any]    	=  tokenizer.convert_ids_to_tokens(__snake_case		)
										self.assertListEqual(
										    __snake_case						,       [
										        SPIECE_UNDERLINE + '''I''',
										        SPIECE_UNDERLINE + '''was''',
										        SPIECE_UNDERLINE + '''b''',
										        '''or''',
										        '''n''',
										        SPIECE_UNDERLINE + '''in''',
										        SPIECE_UNDERLINE + '''''',
										        '''<unk>''',
										        '''2''',
										        '''0''',
										        '''0''',
										        '''0''',
										        ''',''',
										        SPIECE_UNDERLINE + '''and''',
										        SPIECE_UNDERLINE + '''this''',
										        SPIECE_UNDERLINE + '''is''',
										        SPIECE_UNDERLINE + '''f''',
										        '''al''',
										        '''s''',
										        '''<unk>''',
										        '''.''',
										    ]						,       )







							def 					snake_case	(					self  :     Optional[int]		):
										if not self.test_slow_tokenizer:
													# as we don't have a slow version, we can't compare the outputs between slow and fast versions
													return

										lowerCamelCase    :Optional[int]    	=  (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart""", {})
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"		):
																lowerCamelCase    :Tuple    	=  self.rust_tokenizer_class.from_pretrained(__snake_case						,       **__snake_case		)
																lowerCamelCase    :Optional[Any]    	=  self.tokenizer_class.from_pretrained(__snake_case						,       **__snake_case		)

																lowerCamelCase    :Any    	=  tempfile.mkdtemp()

																lowerCamelCase    :str    	=  tokenizer_r.save_pretrained(__snake_case		)
																lowerCamelCase    :Optional[int]    	=  tokenizer_p.save_pretrained(__snake_case		)

																# Checks it save with the same files + the tokenizer.json file for the fast one
																self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files		)		)
																lowerCamelCase    :int    	=  tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f		)
																self.assertSequenceEqual(__snake_case						,       __snake_case		)

																# Checks everything loads correctly in the same way
																lowerCamelCase    :Dict    	=  tokenizer_r.from_pretrained(__snake_case		)
																lowerCamelCase    :Optional[Any]    	=  tokenizer_p.from_pretrained(__snake_case		)

																# Check special tokens are set accordingly on Rust and Python
																for key in tokenizer_pp.special_tokens_map:
																			self.assertTrue(hasattr(__snake_case						,       __snake_case		)		)
																			# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																			# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																shutil.rmtree(__snake_case		)

																# Save tokenizer rust, legacy_format=True
																lowerCamelCase    :Union[str, Any]    	=  tempfile.mkdtemp()

																lowerCamelCase    :Optional[int]    	=  tokenizer_r.save_pretrained(__snake_case						,       legacy_format=__snake_case		)
																lowerCamelCase    :List[str]    	=  tokenizer_p.save_pretrained(__snake_case		)

																# Checks it save with the same files
																self.assertSequenceEqual(__snake_case						,       __snake_case		)

																# Checks everything loads correctly in the same way
																lowerCamelCase    :int    	=  tokenizer_r.from_pretrained(__snake_case		)
																lowerCamelCase    :Any    	=  tokenizer_p.from_pretrained(__snake_case		)

																# Check special tokens are set accordingly on Rust and Python
																for key in tokenizer_pp.special_tokens_map:
																			self.assertTrue(hasattr(__snake_case						,       __snake_case		)		)

																shutil.rmtree(__snake_case		)

																# Save tokenizer rust, legacy_format=False
																lowerCamelCase    :Any    	=  tempfile.mkdtemp()

																lowerCamelCase    :Tuple    	=  tokenizer_r.save_pretrained(__snake_case						,       legacy_format=__snake_case		)
																lowerCamelCase    :int    	=  tokenizer_p.save_pretrained(__snake_case		)

																# Checks it saved the tokenizer.json file
																self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files		)		)

																# Checks everything loads correctly in the same way
																lowerCamelCase    :Optional[Any]    	=  tokenizer_r.from_pretrained(__snake_case		)
																lowerCamelCase    :Any    	=  tokenizer_p.from_pretrained(__snake_case		)

																# Check special tokens are set accordingly on Rust and Python
																for key in tokenizer_pp.special_tokens_map:
																			self.assertTrue(hasattr(__snake_case						,       __snake_case		)		)

																shutil.rmtree(__snake_case		)


@require_torch
@require_sentencepiece
@require_tokenizers
class 	_lowerCAmelCase		(  unittest.TestCase					):
							_UpperCAmelCase         =  'facebook/mbart-large-en-ro'
							_UpperCAmelCase         =  [
							    ' UN Chief Says There Is No Military Solution in Syria',
							    ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
							]
							_UpperCAmelCase         =  [
							    'Şeful ONU declară că nu există o soluţie militară în Siria',
							    'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
							    ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
							    ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
							]
							_UpperCAmelCase         =  [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]







							@classmethod
							def 					snake_case	(					cls  :     List[Any]		):
										lowerCamelCase    :MBartTokenizer    	=  MBartTokenizer.from_pretrained(
										    cls.checkpoint_name						,       src_lang='''en_XX'''						,       tgt_lang='''ro_RO'''		)
										lowerCamelCase    :List[Any]    	=  1
										return cls







							def 					snake_case	(					self  :     Union[str, Any]		):
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']						,       250001		)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']						,       250004		)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']						,       250020		)







							def 					snake_case	(					self  :     Optional[Any]		):
										lowerCamelCase    :Dict    	=  self.tokenizer.batch_encode_plus(self.src_text		).input_ids[0]
										self.assertListEqual(self.expected_src_tokens						,       __snake_case		)







							def 					snake_case	(					self  :     Any		):
										self.assertIn(__snake_case						,       self.tokenizer.all_special_ids		)
										lowerCamelCase    :List[str]    	=  [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
										lowerCamelCase    :Tuple    	=  self.tokenizer.decode(__snake_case						,       skip_special_tokens=__snake_case		)
										lowerCamelCase    :Any    	=  self.tokenizer.decode(generated_ids[1:]						,       skip_special_tokens=__snake_case		)
										self.assertEqual(__snake_case						,       __snake_case		)
										self.assertNotIn(self.tokenizer.eos_token						,       __snake_case		)







							def 					snake_case	(					self  :     str		):
										lowerCamelCase    :List[Any]    	=  ["""this is gunna be a long sentence """ * 20]
										assert isinstance(src_text[0]						,       __snake_case		)
										lowerCamelCase    :str    	=  10
										lowerCamelCase    :Optional[int]    	=  self.tokenizer(__snake_case						,       max_length=__snake_case						,       truncation=__snake_case		).input_ids[0]
										self.assertEqual(ids[-2]						,       2		)
										self.assertEqual(ids[-1]						,       __snake_case		)
										self.assertEqual(len(__snake_case		)						,       __snake_case		)







							def 					snake_case	(					self  :     Optional[Any]		):
										self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']		)						,       [250026, 250001]		)







							def 					snake_case	(					self  :     Optional[int]		):
										lowerCamelCase    :List[str]    	=  tempfile.mkdtemp()
										lowerCamelCase    :int    	=  self.tokenizer.fairseq_tokens_to_ids
										self.tokenizer.save_pretrained(__snake_case		)
										lowerCamelCase    :List[str]    	=  MBartTokenizer.from_pretrained(__snake_case		)
										self.assertDictEqual(new_tok.fairseq_tokens_to_ids						,       __snake_case		)







							@require_torch
							def 					snake_case	(					self  :     Union[str, Any]		):
										lowerCamelCase    :Optional[Any]    	=  self.tokenizer(self.src_text						,       text_target=self.tgt_text						,       padding=__snake_case						,       return_tensors='''pt'''		)
										lowerCamelCase    :Any    	=  shift_tokens_right(batch['''labels''']						,       self.tokenizer.pad_token_id		)

										# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
										assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
										assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
										assert batch.decoder_input_ids[1][-1] == 2
										assert batch.labels[1][-2:].tolist() == [2, RO_CODE]







							@require_torch
							def 					snake_case	(					self  :     Optional[Any]		):
										lowerCamelCase    :str    	=  self.tokenizer(
										    self.src_text						,       text_target=self.tgt_text						,       padding=__snake_case						,       truncation=__snake_case						,       max_length=len(self.expected_src_tokens		)						,       return_tensors='''pt'''						,       )

										lowerCamelCase    :List[str]    	=  shift_tokens_right(batch['''labels''']						,       self.tokenizer.pad_token_id		)

										self.assertIsInstance(__snake_case						,       __snake_case		)

										self.assertEqual((2, 14)						,       batch.input_ids.shape		)
										self.assertEqual((2, 14)						,       batch.attention_mask.shape		)
										lowerCamelCase    :str    	=  batch.input_ids.tolist()[0]
										self.assertListEqual(self.expected_src_tokens						,       __snake_case		)
										self.assertEqual(2						,       batch.decoder_input_ids[0, -1]		)  # EOS
										# Test that special tokens are reset
										self.assertEqual(self.tokenizer.prefix_tokens						,       []		)
										self.assertEqual(self.tokenizer.suffix_tokens						,       [self.tokenizer.eos_token_id, EN_CODE]		)







							def 					snake_case	(					self  :     int		):
										lowerCamelCase    :Optional[Any]    	=  self.tokenizer(self.src_text						,       padding=__snake_case						,       truncation=__snake_case						,       max_length=3						,       return_tensors='''pt'''		)
										lowerCamelCase    :List[Any]    	=  self.tokenizer(
										    text_target=self.tgt_text						,       padding=__snake_case						,       truncation=__snake_case						,       max_length=10						,       return_tensors='''pt'''		)
										lowerCamelCase    :Dict    	=  targets["""input_ids"""]
										lowerCamelCase    :int    	=  shift_tokens_right(__snake_case						,       self.tokenizer.pad_token_id		)

										self.assertEqual(batch.input_ids.shape[1]						,       3		)
										self.assertEqual(batch.decoder_input_ids.shape[1]						,       10		)







							@require_torch
							def 					snake_case	(					self  :     str		):
										lowerCamelCase    :int    	=  self.tokenizer._build_translation_inputs(
										    '''A test'''						,       return_tensors='''pt'''						,       src_lang='''en_XX'''						,       tgt_lang='''ar_AR'''		)

										self.assertEqual(
										    nested_simplify(__snake_case		)						,       {
										        # A, test, EOS, en_XX
										        '''input_ids''': [[62, 3034, 2, 250004]],
										        '''attention_mask''': [[1, 1, 1, 1]],
										        # ar_AR
										        '''forced_bos_token_id''': 250001,
										    }						,       )







'''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       :    Union[str, Any]       ,		lowerCamelCase       :    str       ,		lowerCamelCase       :    Union[str, Any]=7       ,		lowerCamelCase       :    Any=3       ,		lowerCamelCase       :    Tuple=18       ,		lowerCamelCase       :    List[Any]=30       ,		lowerCamelCase       :    str=4_00       ,		lowerCamelCase       :    List[str]=True       ,		lowerCamelCase       :    List[Any]=None       ,		lowerCamelCase       :    Any=True       ,		)  ->    Any:
      lowerCAmelCase_     : Optional[int]     =  size if size is not None else {"""height""": 18, """width""": 18}
      lowerCAmelCase_     : Dict     =  parent
      lowerCAmelCase_     : Tuple     =  batch_size
      lowerCAmelCase_     : int     =  num_channels
      lowerCAmelCase_     : List[str]     =  image_size
      lowerCAmelCase_     : Optional[int]     =  min_resolution
      lowerCAmelCase_     : List[str]     =  max_resolution
      lowerCAmelCase_     : List[str]     =  do_resize
      lowerCAmelCase_     : str     =  size
      lowerCAmelCase_     : int     =  apply_ocr





    def __lowercase  (  self       :    Optional[Any]						)  ->    List[Any]:
      return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}







@require_torch
@require_pytesseract
class   __snake_case			(  _SCREAMING_SNAKE_CASE      ,unittest.TestCase):







    """simple docstring"""



    lowercase  					=   LayoutLMvaImageProcessor if is_pytesseract_available() else None

    def __lowercase  (  self       :    List[Any]						)  ->    Dict:
      lowerCAmelCase_     : Union[str, Any]     =  LayoutLMvaImageProcessingTester(self						)

    @property
    def __lowercase  (  self       :    Optional[Any]						)  ->    Optional[Any]:
      return self.image_processor_tester.prepare_image_processor_dict()

    def __lowercase  (  self       :    Optional[Any]						)  ->    Dict:
      lowerCAmelCase_     : Dict     =  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 __lowercase  (  self       :    List[str]						)  ->    int:
      lowerCAmelCase_     : Dict     =  self.image_processing_class.from_dict(self.image_processor_dict						)
      self.assertEqual(image_processor.size       ,		{"""height""": 18, """width""": 18}						)

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

    def __lowercase  (  self       :    str						)  ->    Dict:
      pass

    def __lowercase  (  self       :    str						)  ->    Union[str, Any]:
      # Initialize image_processing
      lowerCAmelCase_     : Union[str, Any]     =  self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      lowerCAmelCase_     : Tuple     =  prepare_image_inputs(self.image_processor_tester       ,		equal_resolution=lowerCamelCase						)
      for image in image_inputs:
        self.assertIsInstance(lowerCamelCase       ,		Image.Image						)

      # Test not batched input
      lowerCAmelCase_     : List[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       ,		lowerCamelCase						)
      self.assertIsInstance(encoding.boxes       ,		lowerCamelCase						)

      # Test batched
      lowerCAmelCase_     : Union[str, Any]     =  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 __lowercase  (  self       :    Any						)  ->    Any:
      # Initialize image_processing
      lowerCAmelCase_     : Any     =  self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      lowerCAmelCase_     : List[Any]     =  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
      lowerCAmelCase_     : List[Any]     =  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_     : Optional[int]     =  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 __lowercase  (  self       :    List[str]						)  ->    int:
      # Initialize image_processing
      lowerCAmelCase_     : Dict     =  self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      lowerCAmelCase_     : Union[str, Any]     =  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
      lowerCAmelCase_     : 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
      lowerCAmelCase_     : Tuple     =  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 __lowercase  (  self       :    int						)  ->    Optional[Any]:
      # with apply_OCR = True
      lowerCAmelCase_     : Optional[int]     =  LayoutLMvaImageProcessor()

      from datasets import load_dataset

      lowerCAmelCase_     : int     =  load_dataset("""hf-internal-testing/fixtures_docvqa"""       ,		split="""test"""						)

      lowerCAmelCase_     : Any     =  Image.open(ds[0]["""file"""]						).convert("""RGB"""						)

      lowerCAmelCase_     : Dict     =  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
      lowerCAmelCase_     : Union[str, 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
      lowerCAmelCase_     : Any     =  [[[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
      lowerCAmelCase_     : Dict     =  LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase						)

      lowerCAmelCase_     : Dict     =  image_processing(lowerCamelCase       ,		return_tensors="""pt"""						)

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



