






"""simple docstring"""



import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
_UpperCamelCase		:  Dict						=		(7_2_0, 1_2_8_0)  # Height, Width
_UpperCamelCase		:  Optional[Any]						=		(0.4, 0.6)  # if height or width lower than this scale, drop it.
_UpperCamelCase		:  Dict						=		1 / 1_0_0
_UpperCamelCase		:  List[str]						=		''''''
_UpperCamelCase		:  str						=		''''''
_UpperCamelCase		:  Optional[int]						=		''''''
_UpperCamelCase		:  Tuple						=		2_5_0
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       get_dataset(_a		,					_a    )
  for index in range(_a    ):
    lowercase							    =       random.sample(range(len(_a    )    )		,					4    )
    lowercase							    =       update_image_and_anno(
        _a		,					_a		,					_a		,					_a		,					_a		,					filter_scale=_a		,					)

    # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
    lowercase							    =       random_chars(32    )
    lowercase							    =       path.split(os.sep    )[-1].rsplit('.'		,					1    )[0]
    lowercase							    =       f'{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'
    cva.imwrite(f'{file_root}.jpg'		,					_a		,					[cva.IMWRITE_JPEG_QUALITY, 85]    )
    print(f'Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'    )
    lowercase							    =       []
    for anno in new_annos:
      lowercase							    =       anno[3] - anno[1]
      lowercase							    =       anno[4] - anno[2]
      lowercase							    =       anno[1] + width / 2
      lowercase							    =       anno[2] + height / 2
      lowercase							    =       f'{anno[0]} {x_center} {y_center} {width} {height}'
      annos_list.append(_a    )
    with open(f'{file_root}.txt'		,					'w'    ) as outfile:
      outfile.write('\n'.join(line for line in annos_list    )    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[int]		,					__snake_case			:  Tuple    ):





  '''simple docstring'''





  lowercase							    =       []
  lowercase							    =       []
  for label_file in glob.glob(os.path.join(_a		,					'*.txt'    )    ):
    lowercase							    =       label_file.split(os.sep    )[-1].rsplit('.'		,					1    )[0]
    with open(_a    ) as in_file:
      lowercase							    =       in_file.readlines()
    lowercase							    =       os.path.join(_a		,					f'{label_name}.jpg'    )

    lowercase							    =       []
    for obj_list in obj_lists:
      lowercase							    =       obj_list.rstrip('\n'    ).split(' '    )
      lowercase							    =       float(obj[1]    ) - float(obj[3]    ) / 2
      lowercase							    =       float(obj[2]    ) - float(obj[4]    ) / 2
      lowercase							    =       float(obj[1]    ) + float(obj[3]    ) / 2
      lowercase							    =       float(obj[2]    ) + float(obj[4]    ) / 2

      boxes.append([int(obj[0]    ), xmin, ymin, xmax, ymax]    )
    if not boxes:
      continue
    img_paths.append(_a    )
    labels.append(_a    )
  return img_paths, labels
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Any		,					__snake_case			:  List[Any]		,					__snake_case			:  List[Any]		,					__snake_case			:  str		,					__snake_case			:  str		,					__snake_case			:  Optional[Any] = 0.0		,					):





  '''simple docstring'''





  lowercase							    =       np.zeros([output_size[0], output_size[1], 3]		,					dtype=np.uinta    )
  lowercase							    =       scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
  lowercase							    =       scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
  lowercase							    =       int(scale_x * output_size[1]    )
  lowercase							    =       int(scale_y * output_size[0]    )

  lowercase							    =       []
  lowercase							    =       []
  for i, index in enumerate(_a    ):
    lowercase							    =       all_img_list[index]
    path_list.append(_a    )
    lowercase							    =       all_annos[index]
    lowercase							    =       cva.imread(_a    )
    if i == 0:  # top-left
      lowercase							    =       cva.resize(_a		,					(divid_point_x, divid_point_y)    )
      lowercase							    =       img
      for bbox in img_annos:
        lowercase							    =       bbox[1] * scale_x
        lowercase							    =       bbox[2] * scale_y
        lowercase							    =       bbox[3] * scale_x
        lowercase							    =       bbox[4] * scale_y
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]    )
    elif i == 1:  # top-right
      lowercase							    =       cva.resize(_a		,					(output_size[1] - divid_point_x, divid_point_y)    )
      lowercase							    =       img
      for bbox in img_annos:
        lowercase							    =       scale_x + bbox[1] * (1 - scale_x)
        lowercase							    =       bbox[2] * scale_y
        lowercase							    =       scale_x + bbox[3] * (1 - scale_x)
        lowercase							    =       bbox[4] * scale_y
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]    )
    elif i == 2:  # bottom-left
      lowercase							    =       cva.resize(_a		,					(divid_point_x, output_size[0] - divid_point_y)    )
      lowercase							    =       img
      for bbox in img_annos:
        lowercase							    =       bbox[1] * scale_x
        lowercase							    =       scale_y + bbox[2] * (1 - scale_y)
        lowercase							    =       bbox[3] * scale_x
        lowercase							    =       scale_y + bbox[4] * (1 - scale_y)
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]    )
    else:  # bottom-right
      lowercase							    =       cva.resize(
          _a		,					(output_size[1] - divid_point_x, output_size[0] - divid_point_y)    )
      lowercase							    =       img
      for bbox in img_annos:
        lowercase							    =       scale_x + bbox[1] * (1 - scale_x)
        lowercase							    =       scale_y + bbox[2] * (1 - scale_y)
        lowercase							    =       scale_x + bbox[3] * (1 - scale_x)
        lowercase							    =       scale_y + bbox[4] * (1 - scale_y)
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]    )

    # Remove bounding box small than scale of filter
  if filter_scale > 0:
    lowercase							    =       [
        anno
        for anno in new_anno
        if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
    ]

  return output_img, new_anno, path_list[0]
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  assert number_char > 1, "The number of character should greater than 1"
  lowercase							    =       ascii_lowercase + digits
  return "".join(random.choice(_a    ) for _ in range(_a    )    )


if __name__ == "__main__":
    main()
    print('DONE ✅')







"""simple docstring"""

import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__    :  Dict							    =	get_tests_dir('''fixtures/test_sentencepiece.model''')




@require_sentencepiece
@require_tokenizers
class        _UpperCAmelCase       (			lowerCAmelCase__    ,     unittest.TestCase):
 _lowerCAmelCase   :			str							=		XLMRobertaTokenizer
 _lowerCAmelCase   :			int							=		XLMRobertaTokenizerFast
 _lowerCAmelCase   :			str							=		True
 _lowerCAmelCase   :			Dict							=		True

 def 						_snake_case					(     self		:       List[Any]				):
     super().setUp()

     # We have a SentencePiece fixture for testing
     snake_case_ :     List[str]				=	XLMRobertaTokenizer(lowercase_  ,     keep_accents=lowercase_				)
     tokenizer.save_pretrained(self.tmpdirname				)

 def 						_snake_case					(     self		:       str				):
     snake_case_ :     List[Any]				=	'''<pad>'''
     snake_case_ :     Optional[int]				=	1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_				)  ,     lowercase_				)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_				)  ,     lowercase_				)

 def 						_snake_case					(     self		:       Union[str, Any]				):
     snake_case_ :     Dict				=	list(self.get_tokenizer().get_vocab().keys()				)

     self.assertEqual(vocab_keys[0]  ,     '''<s>'''				)
     self.assertEqual(vocab_keys[1]  ,     '''<pad>'''				)
     self.assertEqual(vocab_keys[-1]  ,     '''<mask>'''				)
     self.assertEqual(len(lowercase_				)  ,     1002				)

 def 						_snake_case					(     self		:       Union[str, Any]				):
     self.assertEqual(self.get_tokenizer().vocab_size  ,     1002				)

 def 						_snake_case					(     self		:       Dict				):
     snake_case_ :     Optional[Any]				=	XLMRobertaTokenizer(lowercase_  ,     keep_accents=lowercase_				)

     snake_case_ :     Dict				=	tokenizer.tokenize('''This is a test'''				)
     self.assertListEqual(lowercase_  ,     ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

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

     snake_case_ :     Dict				=	tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
     self.assertListEqual(
         lowercase_  ,     [
             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''',
             '''.''',
         ]  ,     )
     snake_case_ :     List[Any]				=	tokenizer.convert_tokens_to_ids(lowercase_				)
     self.assertListEqual(
         lowercase_  ,     [
             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 ^
         ]  ,     )

     snake_case_ :     List[str]				=	tokenizer.convert_ids_to_tokens(lowercase_				)
     self.assertListEqual(
         lowercase_  ,     [
             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		:       List[str]				):
     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

     snake_case_ :     int				=	(self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"				):
             snake_case_ :     Union[str, Any]				=	self.rust_tokenizer_class.from_pretrained(lowercase_  ,     **lowercase_				)
             snake_case_ :     int				=	self.tokenizer_class.from_pretrained(lowercase_  ,     **lowercase_				)

             snake_case_ :     Optional[Any]				=	tempfile.mkdtemp()

             snake_case_ :     Tuple				=	tokenizer_r.save_pretrained(lowercase_				)
             snake_case_ :     List[str]				=	tokenizer_p.save_pretrained(lowercase_				)

             # 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				)				)
             snake_case_ :     str				=	tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f				)
             self.assertSequenceEqual(lowercase_  ,     lowercase_				)

             # Checks everything loads correctly in the same way
             snake_case_ :     Union[str, Any]				=	tokenizer_r.from_pretrained(lowercase_				)
             snake_case_ :     List[Any]				=	tokenizer_p.from_pretrained(lowercase_				)

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

             shutil.rmtree(lowercase_				)

             # Save tokenizer rust, legacy_format=True
             snake_case_ :     Optional[Any]				=	tempfile.mkdtemp()

             snake_case_ :     List[str]				=	tokenizer_r.save_pretrained(lowercase_  ,     legacy_format=lowercase_				)
             snake_case_ :     List[str]				=	tokenizer_p.save_pretrained(lowercase_				)

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

             # Checks everything loads correctly in the same way
             snake_case_ :     List[Any]				=	tokenizer_r.from_pretrained(lowercase_				)
             snake_case_ :     List[str]				=	tokenizer_p.from_pretrained(lowercase_				)

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

             shutil.rmtree(lowercase_				)

             # Save tokenizer rust, legacy_format=False
             snake_case_ :     Optional[Any]				=	tempfile.mkdtemp()

             snake_case_ :     List[Any]				=	tokenizer_r.save_pretrained(lowercase_  ,     legacy_format=lowercase_				)
             snake_case_ :     Tuple				=	tokenizer_p.save_pretrained(lowercase_				)

             # 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
             snake_case_ :     Optional[Any]				=	tokenizer_r.from_pretrained(lowercase_				)
             snake_case_ :     Dict				=	tokenizer_p.from_pretrained(lowercase_				)

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

             shutil.rmtree(lowercase_				)

 @cached_property
 def 						_snake_case					(     self		:       List[str]				):
     return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''				)

 def 						_snake_case					(     self		:       Optional[Any]				):
     with tempfile.NamedTemporaryFile() as f:
         shutil.copyfile(lowercase_  ,     f.name				)
         snake_case_ :     Any				=	XLMRobertaTokenizer(f.name  ,     keep_accents=lowercase_				)
         snake_case_ :     List[Any]				=	pickle.dumps(lowercase_				)
     pickle.loads(lowercase_				)

 def 						_snake_case					(     self		:       Tuple				):
     if not self.test_rust_tokenizer:
         return

     snake_case_ :     List[str]				=	self.get_tokenizer()
     snake_case_ :     Optional[int]				=	self.get_rust_tokenizer()

     snake_case_ :     Dict				=	'''I was born in 92000, and this is falsé.'''

     snake_case_ :     Optional[int]				=	tokenizer.tokenize(lowercase_				)
     snake_case_ :     Tuple				=	rust_tokenizer.tokenize(lowercase_				)
     self.assertListEqual(lowercase_  ,     lowercase_				)

     snake_case_ :     List[str]				=	tokenizer.encode(lowercase_  ,     add_special_tokens=lowercase_				)
     snake_case_ :     str				=	rust_tokenizer.encode(lowercase_  ,     add_special_tokens=lowercase_				)
     self.assertListEqual(lowercase_  ,     lowercase_				)

     snake_case_ :     int				=	self.get_rust_tokenizer()
     snake_case_ :     Any				=	tokenizer.encode(lowercase_				)
     snake_case_ :     int				=	rust_tokenizer.encode(lowercase_				)
     self.assertListEqual(lowercase_  ,     lowercase_				)

 @slow
 def 						_snake_case					(     self		:       Tuple				):
     snake_case_ :     int				=	'''Hello World!'''
     snake_case_ :     int				=	[0, 35378, 6661, 38, 2]
     # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
     # xlmr.eval()
     # xlmr.encode(symbols)

     self.assertListEqual(lowercase_  ,     self.big_tokenizer.encode(lowercase_				)				)

 @slow
 def 						_snake_case					(     self		:       List[Any]				):
     snake_case_ :     Any				=	(
         '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
         ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
     )
     snake_case_ :     Optional[int]				=	[
         0,
         3293,
         83,
         10,
         4552,
         4989,
         7986,
         678,
         10,
         5915,
         111,
         179459,
         124850,
         4,
         6044,
         237,
         12,
         6,
         5,
         6,
         4,
         6780,
         705,
         15,
         1388,
         44,
         378,
         10114,
         711,
         152,
         20,
         6,
         5,
         22376,
         642,
         1221,
         15190,
         34153,
         450,
         5608,
         959,
         1119,
         57702,
         136,
         186,
         47,
         1098,
         29367,
         47,
         # 4426, # What fairseq tokenizes from "<unk>": "_<"
         # 3678, # What fairseq tokenizes from "<unk>": "unk"
         # 2740, # What fairseq tokenizes from "<unk>": ">"
         3,  # What we tokenize from "<unk>": "<unk>"
         6,  # Residue from the tokenization: an extra sentencepiece underline
         4,
         6044,
         237,
         6284,
         50901,
         528,
         31,
         90,
         34,
         927,
         2,
     ]
     # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
     # xlmr.eval()
     # xlmr.encode(symbols)

     self.assertListEqual(lowercase_  ,     self.big_tokenizer.encode(lowercase_				)				)






 @slow
 def 						_snake_case					(     self		:       Dict				):
     # fmt: off
     snake_case_ :     int				=	{'''input_ids''': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=lowercase_  ,     model_name='''xlm-roberta-base'''  ,     revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''  ,     )





