



'''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 __A			(  unittest.TestCase						):



 '''simple docstring'''







 def __init__(self ,   A ,   A=7 ,   A=3 ,   A=18 ,   A=30 ,   A=400 ,   A=True ,   A=None ,   A=True ,   )    ->     Dict:


      """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 					a__  (self       )    ->     str:


      """simple docstring"""



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






@require_torch
@require_pytesseract
class __A			(  A   ,   unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase    :       Dict          =       LayoutLMvaImageProcessor if is_pytesseract_available() else None



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


      """simple docstring"""



      _a      =  LayoutLMvaImageProcessingTester(self       )



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


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def 					a__  (self       )    ->     str:


      """simple docstring"""



      _a      =  self.image_processing_class(**self.image_processor_dict       )
      self.assertTrue(hasattr(A ,   '''do_resize'''       )       )
      self.assertTrue(hasattr(A ,   '''size'''       )       )
      self.assertTrue(hasattr(A ,   '''apply_ocr'''       )       )



 def 					a__  (self       )    ->     Tuple:


      """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 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      pass



 def 					a__  (self       )    ->     Tuple:


      """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=A       )
      for image in image_inputs:
           self.assertIsInstance(A ,   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 ,   A       )
      self.assertIsInstance(encoding.boxes ,   A       )

      # Test batched
      _a      =  image_processing(A ,   return_tensors='''pt'''       ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          ) ,   )



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


      """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=A ,   numpify=A       )
      for image in image_inputs:
           self.assertIsInstance(A ,   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(A ,   return_tensors='''pt'''       ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          ) ,   )



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


      """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=A ,   torchify=A       )
      for image in image_inputs:
           self.assertIsInstance(A ,   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(A ,   return_tensors='''pt'''       ).pixel_values
      self.assertEqual(
          encoded_images.shape ,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          ) ,   )



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


      """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(A ,   return_tensors='''pt'''       )

      self.assertEqual(encoding.pixel_values.shape ,   (1, 3, 224, 224)       )
      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      =  [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
      # fmt: on

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

      # with apply_OCR = False
      _a      =  LayoutLMvaImageProcessor(apply_ocr=A       )

      _a      =  image_processing(A ,   return_tensors='''pt'''       )

      self.assertEqual(encoding.pixel_values.shape ,   (1, 3, 224, 224)       )








'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowercase_		       =  {
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

lowercase_		       =  {
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       List[Any]          =       VOCAB_FILES_NAMES
 __lowerCamelCase    :       Tuple          =       PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase    :       Any          =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase    :       str          =       ['input_ids', 'attention_mask']
 __lowerCamelCase    :       Union[str, Any]          =       RobertaTokenizer



 def __init__(self ,   A=None ,   A=None ,   A=None ,   A="replace" ,   A="<s>" ,   A="</s>" ,   A="</s>" ,   A="<s>" ,   A="<unk>" ,   A="<pad>" ,   A="<mask>" ,   A=False ,   A=True ,   **A ,   )    ->     List[str]:


      """simple docstring"""



      super().__init__(
          A ,   A ,   tokenizer_file=A ,   errors=A ,   bos_token=A ,   eos_token=A ,   sep_token=A ,   cls_token=A ,   unk_token=A ,   pad_token=A ,   mask_token=A ,   add_prefix_space=A ,   trim_offsets=A ,   **A ,   )

      _a      =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
      if pre_tok_state.get('''add_prefix_space''' ,   A       ) != add_prefix_space:
           _a      =  getattr(A ,   pre_tok_state.pop('''type'''       )       )
           _a      =  add_prefix_space
           _a      =  pre_tok_class(**A       )

      _a      =  add_prefix_space

      _a      =  '''post_processor'''
      _a      =  getattr(self.backend_tokenizer ,   A ,   A       )
      if tokenizer_component_instance:
           _a      =  json.loads(tokenizer_component_instance.__getstate__()       )

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
                _a      =  tuple(state['''sep''']       )
           if "cls" in state:
                _a      =  tuple(state['''cls''']       )

           _a      =  False

           if state.get('''add_prefix_space''' ,   A       ) != add_prefix_space:
                _a      =  add_prefix_space
                _a      =  True

           if state.get('''trim_offsets''' ,   A       ) != trim_offsets:
                _a      =  trim_offsets
                _a      =  True

           if changes_to_apply:
                _a      =  getattr(A ,   state.pop('''type'''       )       )
                _a      =  component_class(**A       )
                setattr(self.backend_tokenizer ,   A ,   A       )



 @property
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      if self._mask_token is None:
           if self.verbose:
                logger.error('''Using mask_token, but it is not set yet.'''       )
           return None
      return str(self._mask_token       )



 @mask_token.setter
 def 					a__  (self ,   A       )    ->     Optional[Any]:


      """simple docstring"""



      _a      =  AddedToken(A ,   lstrip=A ,   rstrip=A       ) if isinstance(A ,   A       ) else value
      _a      =  value



 def 					a__  (self ,   *A ,   **A       )    ->     BatchEncoding:


      """simple docstring"""



      _a      =  kwargs.get('''is_split_into_words''' ,   A       )
      assert self.add_prefix_space or not is_split_into_words, (
          f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*A ,   **A       )



 def 					a__  (self ,   *A ,   **A       )    ->     BatchEncoding:


      """simple docstring"""



      _a      =  kwargs.get('''is_split_into_words''' ,   A       )

      assert self.add_prefix_space or not is_split_into_words, (
          f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*A ,   **A       )



 def 					a__  (self ,   A ,   A = None       )    ->     Tuple[str]:


      """simple docstring"""



      _a      =  self._tokenizer.model.save(A ,   name=A       )
      return tuple(A       )



 def 					a__  (self ,   A ,   A=None       )    ->     List[str]:


      """simple docstring"""



      _a      =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
           return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



 def 					a__  (self ,   A ,   A = None       )    ->     List[int]:


      """simple docstring"""



      _a      =  [self.sep_token_id]
      _a      =  [self.cls_token_id]

      if token_ids_a is None:
           return len(cls + token_ids_a + sep       ) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]



