




import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


lowerCamelCase							:						Optional[int]					       =  logging.get_logger(__name__)

lowerCamelCase							:						Union[str, Any]					       =  {"vocab_file": "vocab.txt", "emoji_file": "emoji.json"}

lowerCamelCase							:						Tuple					       =  {
    "vocab_file": {
        "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt",
    },
    "emoji_file": {
        "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json",
    },
}

lowerCamelCase							:						Any					       =  {
    "abeja/gpt-neox-japanese-2.7b": 2_048,
}

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Tuple      ,				lowercase      :			Any	):


      '''simple docstring'''



      with open(lowercase      ,				'r'      ,				encoding='utf-8'	) as f:
            lowerCamelCase_							=       json.loads(f.read()	)

      lowerCamelCase_							=       collections.OrderedDict()
      lowerCamelCase_							=       collections.OrderedDict()
      lowerCamelCase_							=       collections.OrderedDict()
      with open(lowercase      ,				'r'      ,				encoding='utf-8'	) as f:
            lowerCamelCase_							=       f.readlines()
      lowerCamelCase_							=       [[t.rstrip('\n'	)] if (t == ',' or ',' not in t) else t.rstrip('\n'	).split(','	) for t in token]
      for idx, b in enumerate(lowercase	):
            lowerCamelCase_							=       b
            lowerCamelCase_							=       idx
            for wd in b:
                  lowerCamelCase_							=       idx

      return vocab, raw_vocab, ids_to_tokens, emoji







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		VOCAB_FILES_NAMES
 UpperCamelCase		  =		PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase		  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase		  =		['''input_ids''', '''attention_mask''']

 def __init__(							self   :    str		,				A_   :    Any		,				A_   :    Any		,				A_   :    Optional[Any]="<|endoftext|>"		,				A_   :    Any="<|endoftext|>"		,				A_   :    Optional[int]="<|startoftext|>"		,				A_   :    Union[str, Any]="<|endoftext|>"		,				A_   :    Any=False		,				**A_   :    Tuple		,				)  ->      Dict:

       """simple docstring"""






       super().__init__(
           unk_token=A_		,				pad_token=A_		,				bos_token=A_		,				eos_token=A_		,				do_clean_text=A_		,				**A_		,				)
       if not os.path.isfile(A_    ):
             raise ValueError(
                 f"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
                 ' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'    )
       if not os.path.isfile(A_    ):
             raise ValueError(
                 f"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
                 ' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'    )
       lowerCamelCase_							=       do_clean_text
       lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_							=       load_vocab_and_emoji(A_		,				A_    )
       lowerCamelCase_							=       SubWordJapaneseTokenizer(
           vocab=self.vocab		,				ids_to_tokens=self.ids_to_tokens		,				emoji=self.emoji    )

 @property
 def 							a__					(							self   :    List[str]    )  ->      Dict:

       """simple docstring"""






       return len(self.raw_vocab    )

 def 							a__					(							self   :    Tuple    )  ->      Optional[Any]:

       """simple docstring"""






       return dict(self.raw_vocab		,				**self.added_tokens_encoder    )

 def 							a__					(							self   :    Optional[Any]		,				A_   :    str    )  ->      Tuple:

       """simple docstring"""






       return self.subword_tokenizer.tokenize(A_		,				clean=self.do_clean_text    )

 def 							a__					(							self   :    Optional[int]		,				A_   :    Dict    )  ->      List[Any]:

       """simple docstring"""






       return self.vocab.get(A_		,				self.vocab.get(self.unk_token    )    )

 def 							a__					(							self   :    Union[str, Any]		,				A_   :    Union[str, Any]    )  ->      int:

       """simple docstring"""






       return self.subword_tokenizer.convert_id_to_token(A_    )

 def 							a__					(							self   :    Optional[int]		,				A_   :    Optional[int]    )  ->      int:

       """simple docstring"""






       lowerCamelCase_							=       ''.join(A_    ).strip()
       return out_string

 def 							a__					(							self   :    Optional[Any]		,				A_   :    "Conversation"    )  ->      List[int]:

       """simple docstring"""






       lowerCamelCase_							=       []
       for is_user, text in conversation.iter_texts():
             input_ids.extend(self.encode(A_		,				add_special_tokens=A_    ) + [self.eos_token_id]    )

       if len(A_    ) > self.model_max_length:
             lowerCamelCase_							=       input_ids[-self.model_max_length :]
       return input_ids





 def 							a__					(							self   :    List[Any]		,				A_   :    str		,				A_   :    Optional[str] = None    )  ->      Tuple[str]:

       """simple docstring"""






       lowerCamelCase_							=       0
       if os.path.isdir(A_    ):
             lowerCamelCase_							=       os.path.join(
                 A_		,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
             lowerCamelCase_							=       os.path.join(
                 A_		,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']    )
       else:
             lowerCamelCase_							=       (
                 (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
             )
             lowerCamelCase_							=       (
                 (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
             )
       with open(A_		,				'w'		,				encoding='utf-8'    ) as writer:
             for token_index, token in self.ids_to_tokens.items():
                   if index != token_index:
                         logger.warning(
                             f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
                             ' Please check that the vocabulary is not corrupted!'    )
                         lowerCamelCase_							=       token_index
                   writer.write(','.join(A_    ) + '\n'    )
                   index += 1
       with open(A_		,				'w'		,				encoding='utf-8'    ) as writer:
             json.dump(self.emoji		,				A_    )
       return vocab_file, emoji_file







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 def __init__(							self   :    Any		,				A_   :    Union[str, Any]		,				A_   :    int		,				A_   :    Tuple    )  ->      Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=       vocab  # same as swe
       lowerCamelCase_							=       ids_to_tokens  # same as bpe
       lowerCamelCase_							=       emoji
       lowerCamelCase_							=       np.max([len(A_    ) for w in self.vocab.keys()]    )
       lowerCamelCase_							=       re.compile(r'(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)'    )
       lowerCamelCase_							=       re.compile(r'[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*'    )
       lowerCamelCase_							=       re.compile(r'[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}'    )
       lowerCamelCase_							=       re.compile(
           r'([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'    )
       lowerCamelCase_							=       re.compile(
           r'(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'    )
       lowerCamelCase_							=       re.compile(
           r'((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*'    )
       lowerCamelCase_							=       '─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'
       lowerCamelCase_							=       '▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'
       lowerCamelCase_							=       str.maketrans({k: '<BLOCK>' for k in keisen + blocks}    )

 def __len__(							self   :    str    )  ->      Optional[int]:

       """simple docstring"""






       return len(self.ids_to_tokens    )

 def 							a__					(							self   :    Union[str, Any]		,				A_   :    Dict    )  ->      Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=       self.content_repattera.sub('<URL>'		,				A_    )
       lowerCamelCase_							=       self.content_repattera.sub('<EMAIL>'		,				A_    )
       lowerCamelCase_							=       self.content_repattera.sub('<TEL>'		,				A_    )
       lowerCamelCase_							=       self.content_repattera.sub('<DATE>'		,				A_    )
       lowerCamelCase_							=       self.content_repattera.sub('<DATE>'		,				A_    )
       lowerCamelCase_							=       self.content_repattera.sub('<PRICE>'		,				A_    )
       lowerCamelCase_							=       content.translate(self.content_transa    )
       while "<BLOCK><BLOCK>" in content:
             lowerCamelCase_							=       content.replace('<BLOCK><BLOCK>'		,				'<BLOCK>'    )
       return content

 def 							a__					(							self   :    int		,				A_   :    Optional[Any]		,				A_   :    Tuple=False    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       text.replace(' '		,				'<SP>'    )
       lowerCamelCase_							=       text.replace('　'		,				'<SP>'    )
       lowerCamelCase_							=       text.replace('\r\n'		,				'<BR>'    )
       lowerCamelCase_							=       text.replace('\n'		,				'<BR>'    )
       lowerCamelCase_							=       text.replace('\r'		,				'<BR>'    )
       lowerCamelCase_							=       text.replace('\t'		,				'<TAB>'    )
       lowerCamelCase_							=       text.replace('—'		,				'ー'    )
       lowerCamelCase_							=       text.replace('−'		,				'ー'    )
       for k, v in self.emoji["emoji"].items():
             if k in text:
                   lowerCamelCase_							=       text.replace(A_		,				A_    )
       if clean:
             lowerCamelCase_							=       self.clean_text(A_    )

       def check_simbol(A_   :    Union[str, Any]    ):
             lowerCamelCase_							=       x.encode()
             if len(A_    ) == 1 and len(A_    ) == 2:
                   lowerCamelCase_							=       (int(e[0]    ) << 8) + int(e[1]    )
                   if (
                       (c >= 0XC2_A1 and c <= 0XC2_BF)
                       or (c >= 0XC7_80 and c <= 0XC7_83)
                       or (c >= 0XCA_B9 and c <= 0XCB_BF)
                       or (c >= 0XCC_80 and c <= 0XCD_A2)
                   ):
                         return True
             return False

       def checkuae(A_   :    Tuple    ):
             lowerCamelCase_							=       x.encode()
             if len(A_    ) == 1 and len(A_    ) == 3:
                   lowerCamelCase_							=       (int(e[0]    ) << 16) + (int(e[1]    ) << 8) + int(e[2]    )
                   if c >= 0XE2_80_80 and c <= 0XE2_B0_7F:
                         return True
             return False

       lowerCamelCase_							=       0
       lowerCamelCase_							=       []
       while pos < len(A_    ):
             lowerCamelCase_							=       min(len(A_    )		,				pos + self.maxlen + 1    ) if text[pos] == '<' else pos + 3
             lowerCamelCase_							=       []  # (token_id, token, pos)
             for e in range(A_		,				A_		,				-1    ):
                   lowerCamelCase_							=       text[pos:e]
                   if wd in self.vocab:
                         if wd[0] == "<" and len(A_    ) > 2:
                               lowerCamelCase_							=       [(self.vocab[wd], wd, e)]
                               break
                         else:
                               candidates.append((self.vocab[wd], wd, e)    )
             if len(A_    ) > 0:
                   # the smallest token_id is adopted
                   lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_							=       sorted(A_		,				key=lambda A_    : x[0]    )[0]
                   result.append(A_    )
                   lowerCamelCase_							=       e
             else:
                   lowerCamelCase_							=       pos + 1
                   lowerCamelCase_							=       text[pos:end]
                   if check_simbol(A_    ):
                         result.append('<KIGOU>'    )
                   elif checkuae(A_    ):
                         result.append('<U2000U2BFF>'    )
                   else:
                         for i in wd.encode('utf-8'    ):
                               result.append('<|byte%d|>' % i    )
                   lowerCamelCase_							=       end
       return result





 def 							a__					(							self   :    List[Any]		,				A_   :    Tuple		,				A_   :    List[str]="\n"    )  ->      List[str]:

       """simple docstring"""






       lowerCamelCase_							=       []
       lowerCamelCase_							=       []
       lowerCamelCase_							=       self.ids_to_tokens[index][0]
       if word[:6] == "<|byte" and word[-2:] == "|>":
             byte_tokens.append(int(word[6:-2]    )    )
       else:
             if len(A_    ) > 0:
                   words.append(bytearray(A_    ).decode('utf-8'		,				errors='replace'    )    )
                   lowerCamelCase_							=       []
             if word[:7] == "<|emoji" and word[-2:] == "|>":
                   words.append(self.emoji['emoji_inv'][word]    )
             elif word == "<SP>":
                   words.append(' '    )
             elif word == "<BR>":
                   words.append(A_    )
             elif word == "<TAB>":
                   words.append('\t'    )
             elif word == "<BLOCK>":
                   words.append('▀'    )
             elif word == "<KIGOU>":
                   words.append('ǀ'    )
             elif word == "<U2000U2BFF>":
                   words.append('‖'    )
             else:
                   words.append(A_    )
       if len(A_    ) > 0:
             words.append(bytearray(A_    ).decode('utf-8'		,				errors='replace'    )    )
       lowerCamelCase_							=       ''.join(A_    )
       return text






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 ,   lowercase__ ,   lowercase__=7 ,   lowercase__=3 ,   lowercase__=1_8 ,   lowercase__=3_0 ,   lowercase__=4_0_0 ,   lowercase__=True ,   lowercase__=None ,   lowercase__=True ,   ):







             '''simple docstring'''



             __A    =size if size is not None else {'''height''': 1_8, '''width''': 1_8}
             __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     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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





@require_torch
@require_pytesseract
class        lowerCAmelCase__  (					__magic_name__				,							unittest.TestCase			):



       '''simple docstring'''







       lowercase_						=    LayoutLMvaImageProcessor if is_pytesseract_available() else None

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =LayoutLMvaImageProcessingTester(self     )

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self.image_processor_tester.prepare_image_processor_dict()

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.image_processing_class(**self.image_processor_dict     )
             self.assertTrue(hasattr(lowercase__ ,   '''do_resize'''     )     )
             self.assertTrue(hasattr(lowercase__ ,   '''size'''     )     )
             self.assertTrue(hasattr(lowercase__ ,   '''apply_ocr'''     )     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.image_processing_class.from_dict(self.image_processor_dict     )
             self.assertEqual(image_processor.size ,   {'''height''': 1_8, '''width''': 1_8}     )

             __A    =self.image_processing_class.from_dict(self.image_processor_dict ,   size=4_2     )
             self.assertEqual(image_processor.size ,   {'''height''': 4_2, '''width''': 4_2}     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             pass

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.image_processing_class(**self.image_processor_dict     )
             # create random PIL images
             __A    =prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowercase__     )
             for image in image_inputs:
                   self.assertIsInstance(lowercase__ ,   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 ,   lowercase__     )
             self.assertIsInstance(encoding.boxes ,   lowercase__     )

             # Test batched
             __A    =image_processing(lowercase__ ,   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     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.image_processing_class(**self.image_processor_dict     )
             # create random numpy tensors
             __A    =prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowercase__ ,   numpify=lowercase__     )
             for image in image_inputs:
                   self.assertIsInstance(lowercase__ ,   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(lowercase__ ,   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     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.image_processing_class(**self.image_processor_dict     )
             # create random PyTorch tensors
             __A    =prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowercase__ ,   torchify=lowercase__     )
             for image in image_inputs:
                   self.assertIsInstance(lowercase__ ,   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(lowercase__ ,   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     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __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(lowercase__ ,   return_tensors='''pt'''     )

             self.assertEqual(encoding.pixel_values.shape ,   (1, 3, 2_2_4, 2_2_4)     )
             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_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]]  # noqa: E231
             # fmt: on

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

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

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

             self.assertEqual(encoding.pixel_values.shape ,   (1, 3, 2_2_4, 2_2_4)     )


