'''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 lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



  def __init__(      self			:       Tuple  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       List[str]=7  ,							UpperCAmelCase__			:       str=3  ,							UpperCAmelCase__			:       List[Any]=18  ,							UpperCAmelCase__			:       Optional[Any]=30  ,							UpperCAmelCase__			:       List[str]=400  ,							UpperCAmelCase__			:       List[Any]=True  ,							UpperCAmelCase__			:       List[str]=None  ,							UpperCAmelCase__			:       Union[str, Any]=True  ,							)				->Any:
        UpperCAmelCase_            =  size if size is not None else {"""height""": 18, """width""": 18}
        UpperCAmelCase_            =  parent
        UpperCAmelCase_            =  batch_size
        UpperCAmelCase_            =  num_channels
        UpperCAmelCase_            =  image_size
        UpperCAmelCase_            =  min_resolution
        UpperCAmelCase_            =  max_resolution
        UpperCAmelCase_            =  do_resize
        UpperCAmelCase_            =  size
        UpperCAmelCase_            =  apply_ocr






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







@require_torch
@require_pytesseract
class lowerCamelCase							(	SCREAMING_SNAKE_CASE__     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	LayoutLMvaImageProcessor if is_pytesseract_available() else None






  def  lowerCAmelCase__ (      self			:       Optional[Any]  )				->List[Any]:
        UpperCAmelCase_            =  LayoutLMvaImageProcessingTester(self  )






  @property
  def  lowerCAmelCase__ (      self			:       int  )				->List[str]:
        return self.image_processor_tester.prepare_image_processor_dict()






  def  lowerCAmelCase__ (      self			:       List[Any]  )				->Tuple:
        UpperCAmelCase_            =  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  lowerCAmelCase__ (      self			:       List[Any]  )				->List[Any]:
        UpperCAmelCase_            =  self.image_processing_class.from_dict(self.image_processor_dict  )
        self.assertEqual(image_processor.size  ,							{'''height''': 18, '''width''': 18}  )

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






  def  lowerCAmelCase__ (      self			:       List[Any]  )				->Optional[Any]:
        pass






  def  lowerCAmelCase__ (      self			:       int  )				->Tuple:
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        UpperCAmelCase_            =  prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=_lowercase  )
        for image in image_inputs:
              self.assertIsInstance(_lowercase  ,							Image.Image  )

        # Test not batched input
        UpperCAmelCase_            =  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
        UpperCAmelCase_            =  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  lowerCAmelCase__ (      self			:       List[Any]  )				->Dict:
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        UpperCAmelCase_            =  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
        UpperCAmelCase_            =  image_processing(image_inputs[0]  ,							return_tensors='''pt'''  ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,							(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )  ,							)

        # Test batched
        UpperCAmelCase_            =  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  lowerCAmelCase__ (      self			:       List[str]  )				->List[Any]:
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        UpperCAmelCase_            =  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
        UpperCAmelCase_            =  image_processing(image_inputs[0]  ,							return_tensors='''pt'''  ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,							(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )  ,							)

        # Test batched
        UpperCAmelCase_            =  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  lowerCAmelCase__ (      self			:       Optional[Any]  )				->Optional[int]:
        UpperCAmelCase_            =  LayoutLMvaImageProcessor()

        from datasets import load_dataset

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

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

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

        # with apply_OCR = False
        UpperCAmelCase_            =  LayoutLMvaImageProcessor(apply_ocr=_lowercase  )

        UpperCAmelCase_            =  image_processing(_lowercase  ,							return_tensors='''pt'''  )

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






'''simple docstring'''
import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	MvpTokenizer
  lowerCAmelCase__										=	MvpTokenizerFast
  lowerCAmelCase__										=	True
  lowerCAmelCase__										=	filter_roberta_detectors






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Tuple:
        super().setUp()
        UpperCAmelCase_            =  [
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''\u0120''',
            '''\u0120l''',
            '''\u0120n''',
            '''\u0120lo''',
            '''\u0120low''',
            '''er''',
            '''\u0120lowest''',
            '''\u0120newer''',
            '''\u0120wider''',
            '''<unk>''',
        ]
        UpperCAmelCase_            =  dict(zip(UpperCAmelCase__  ,							range(len(UpperCAmelCase__  )  )  )  )
        UpperCAmelCase_            =  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
        UpperCAmelCase_            =  {'''unk_token''': '''<unk>'''}

        UpperCAmelCase_            =  os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['''vocab_file''']  )
        UpperCAmelCase_            =  os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['''merges_file''']  )
        with open(self.vocab_file  ,							'''w'''  ,							encoding='''utf-8'''  ) as fp:
              fp.write(json.dumps(UpperCAmelCase__  ) + '''\n'''  )
        with open(self.merges_file  ,							'''w'''  ,							encoding='''utf-8'''  ) as fp:
              fp.write('''\n'''.join(UpperCAmelCase__  )  )






  def  lowerCAmelCase__ (      self			:       Tuple  ,							**UpperCAmelCase__			:       List[str]  )				->Dict:
        kwargs.update(self.special_tokens_map  )
        return self.tokenizer_class.from_pretrained(self.tmpdirname  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							**UpperCAmelCase__			:       int  )				->Tuple:
        kwargs.update(self.special_tokens_map  )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,							**UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       Optional[Any]  )				->Union[str, Any]:
        return "lower newer", "lower newer"






  @cached_property
  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Optional[int]:
        return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''  )






  @cached_property
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Tuple:
        return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Any  )				->Dict:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        UpperCAmelCase_            =  [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							max_length=len(UpperCAmelCase__  )  ,							padding=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              self.assertIsInstance(UpperCAmelCase__  ,							UpperCAmelCase__  )

              self.assertEqual((2, 9)  ,							batch.input_ids.shape  )
              self.assertEqual((2, 9)  ,							batch.attention_mask.shape  )
              UpperCAmelCase_            =  batch.input_ids.tolist()[0]
              self.assertListEqual(UpperCAmelCase__  ,							UpperCAmelCase__  )
              # Test that special tokens are reset






  @require_torch
  def  lowerCAmelCase__ (      self			:       str  )				->int:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							padding=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              # check if input_ids are returned and no labels
              self.assertIn('''input_ids'''  ,							UpperCAmelCase__  )
              self.assertIn('''attention_mask'''  ,							UpperCAmelCase__  )
              self.assertNotIn('''labels'''  ,							UpperCAmelCase__  )
              self.assertNotIn('''decoder_attention_mask'''  ,							UpperCAmelCase__  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Tuple  )				->Optional[Any]:
        UpperCAmelCase_            =  [
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(text_target=UpperCAmelCase__  ,							max_length=32  ,							padding='''max_length'''  ,							return_tensors='''pt'''  )
              self.assertEqual(32  ,							targets['''input_ids'''].shape[1]  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       List[str]  )				->int:
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(
                  ['''I am a small frog''' * 1024, '''I am a small frog''']  ,							padding=UpperCAmelCase__  ,							truncation=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              self.assertIsInstance(UpperCAmelCase__  ,							UpperCAmelCase__  )
              self.assertEqual(batch.input_ids.shape  ,							(2, 1024)  )






  @require_torch
  def  lowerCAmelCase__ (      self			:       Dict  )				->Optional[int]:
        UpperCAmelCase_            =  ['''A long paragraph for summarization.''']
        UpperCAmelCase_            =  [
            '''Summary of the text.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase_            =  tokenizer(UpperCAmelCase__  ,							text_target=UpperCAmelCase__  ,							return_tensors='''pt'''  )
              UpperCAmelCase_            =  inputs['''input_ids''']
              UpperCAmelCase_            =  inputs['''labels''']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )






  def  lowerCAmelCase__ (      self			:       str  )				->Optional[Any]:
        pass






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Tuple:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
                    UpperCAmelCase_            =  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__  ,							**UpperCAmelCase__  )
                    UpperCAmelCase_            =  self.tokenizer_class.from_pretrained(UpperCAmelCase__  ,							**UpperCAmelCase__  )
                    UpperCAmelCase_            =  '''A, <mask> AllenNLP sentence.'''
                    UpperCAmelCase_            =  tokenizer_r.encode_plus(UpperCAmelCase__  ,							add_special_tokens=UpperCAmelCase__  ,							return_token_type_ids=UpperCAmelCase__  )
                    UpperCAmelCase_            =  tokenizer_p.encode_plus(UpperCAmelCase__  ,							add_special_tokens=UpperCAmelCase__  ,							return_token_type_ids=UpperCAmelCase__  )

                    # token_type_ids should put 0 everywhere
                    self.assertEqual(sum(tokens_r['''token_type_ids''']  )  ,							sum(tokens_p['''token_type_ids''']  )  )

                    # attention_mask should put 1 everywhere, so sum over length should be 1
                    self.assertEqual(
                        sum(tokens_r['''attention_mask''']  ) / len(tokens_r['''attention_mask''']  )  ,							sum(tokens_p['''attention_mask''']  ) / len(tokens_p['''attention_mask''']  )  ,							)

                    UpperCAmelCase_            =  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']  )
                    UpperCAmelCase_            =  tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']  )

                    # Rust correctly handles the space before the mask while python doesnt
                    self.assertSequenceEqual(tokens_p['''input_ids''']  ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )
                    self.assertSequenceEqual(tokens_r['''input_ids''']  ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )

                    self.assertSequenceEqual(
                        UpperCAmelCase__  ,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )
                    self.assertSequenceEqual(
                        UpperCAmelCase__  ,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )





