






"""simple docstring"""



import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[Any]    ):





  '''simple docstring'''





  lowercase							    =       [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
  lowercase							    =       True if '''large''' in model_name or '''huge''' in model_name else False
  lowercase							    =       True if '''large''' in model_name or '''huge''' in model_name else False
  lowercase							    =       True if '''large''' in model_name or '''huge''' in model_name else False

  if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
    if "fl3" in model_name:
      lowercase							    =       [3, 3, 3, 3]
      lowercase							    =       [5, 5, 5, 5]
    elif "fl4" in model_name:
      lowercase							    =       [4, 4, 4, 4]
      lowercase							    =       [3, 3, 3, 3]

  if "tiny" in model_name or "small" in model_name or "base" in model_name:
    lowercase							    =       [3, 3, 3, 3]
    if "lrf" in model_name:
      lowercase							    =       [3, 3, 3, 3]
    else:
      lowercase							    =       [2, 2, 2, 2]

  if "tiny" in model_name:
    lowercase							    =       96
  elif "small" in model_name:
    lowercase							    =       96
  elif "base" in model_name:
    lowercase							    =       1_28
  elif "large" in model_name:
    lowercase							    =       1_92
  elif "xlarge" in model_name:
    lowercase							    =       2_56
  elif "huge" in model_name:
    lowercase							    =       3_52

  # set label information
  lowercase							    =       '''huggingface/label-files'''
  if "large" in model_name or "huge" in model_name:
    lowercase							    =       '''imagenet-22k-id2label.json'''
  else:
    lowercase							    =       '''imagenet-1k-id2label.json'''

  lowercase							    =       json.load(open(hf_hub_download(_a		,					_a		,					repo_type='dataset'    )		,					'r'    )    )
  lowercase							    =       {int(_a    ): v for k, v in idalabel.items()}
  lowercase							    =       {v: k for k, v in idalabel.items()}

  lowercase							    =       FocalNetConfig(
      embed_dim=_a		,					depths=_a		,					focal_levels=_a		,					focal_windows=_a		,					use_conv_embed=_a		,					idalabel=_a		,					labelaid=_a		,					use_post_layernorm=_a		,					use_layerscale=_a		,					)

  return config
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Tuple    ):





  '''simple docstring'''





  if "patch_embed.proj" in name:
    lowercase							    =       name.replace('patch_embed.proj'		,					'embeddings.patch_embeddings.projection'    )
  if "patch_embed.norm" in name:
    lowercase							    =       name.replace('patch_embed.norm'		,					'embeddings.norm'    )
  if "layers" in name:
    lowercase							    =       '''encoder.''' + name
  if "encoder.layers" in name:
    lowercase							    =       name.replace('encoder.layers'		,					'encoder.stages'    )
  if "downsample.proj" in name:
    lowercase							    =       name.replace('downsample.proj'		,					'downsample.projection'    )
  if "blocks" in name:
    lowercase							    =       name.replace('blocks'		,					'layers'    )
  if "modulation.f.weight" in name or "modulation.f.bias" in name:
    lowercase							    =       name.replace('modulation.f'		,					'modulation.projection_in'    )
  if "modulation.h.weight" in name or "modulation.h.bias" in name:
    lowercase							    =       name.replace('modulation.h'		,					'modulation.projection_context'    )
  if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
    lowercase							    =       name.replace('modulation.proj'		,					'modulation.projection_out'    )

  if name == "norm.weight":
    lowercase							    =       '''layernorm.weight'''
  if name == "norm.bias":
    lowercase							    =       '''layernorm.bias'''

  if "head" in name:
    lowercase							    =       name.replace('head'		,					'classifier'    )
  else:
    lowercase							    =       '''focalnet.''' + name

  return name
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Tuple		,					__snake_case			:  List[Any]		,					__snake_case			:  List[Any]=False    ):





  '''simple docstring'''





  lowercase							    =       {
      '''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
      '''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
      '''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
      '''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
      '''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
      '''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
      '''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
      '''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
      '''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
      '''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
  }
  # fmt: on

  lowercase							    =       model_name_to_url[model_name]
  print('Checkpoint URL: '		,					_a    )
  lowercase							    =       torch.hub.load_state_dict_from_url(_a		,					map_location='cpu'    )['''model''']

  # rename keys
  for key in state_dict.copy().keys():
    lowercase							    =       state_dict.pop(_a    )
    lowercase							    =       val

  lowercase							    =       get_focalnet_config(_a    )
  lowercase							    =       FocalNetForImageClassification(_a    )
  model.eval()

  # load state dict
  model.load_state_dict(_a    )

  # verify conversion
  lowercase							    =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''

  lowercase							    =       BitImageProcessor(
      do_resize=_a		,					size={'shortest_edge': 2_56}		,					resample=PILImageResampling.BILINEAR		,					do_center_crop=_a		,					crop_size=2_24		,					do_normalize=_a		,					image_mean=_a		,					image_std=_a		,					)
  lowercase							    =       Image.open(requests.get(_a		,					stream=_a    ).raw    )
  lowercase							    =       processor(images=_a		,					return_tensors='pt'    )

  lowercase							    =       transforms.Compose(
      [
          transforms.Resize(2_56    ),
          transforms.CenterCrop(2_24    ),
          transforms.ToTensor(),
          transforms.Normalize(mean=[0.485, 0.456, 0.406]		,					std=[0.229, 0.224, 0.225]    ),
      ]    )

  lowercase							    =       image_transforms(_a    ).unsqueeze(0    )

  # verify pixel_values
  assert torch.allclose(inputs.pixel_values		,					_a		,					atol=1e-4    )

  lowercase							    =       model(**_a    )

  lowercase							    =       outputs.logits.argmax(-1    ).item()
  print('Predicted class:'		,					model.config.idalabel[predicted_class_idx]    )

  print('First values of logits:'		,					outputs.logits[0, :3]    )

  if model_name == "focalnet-tiny":
    lowercase							    =       torch.tensor([0.2166, -0.4368, 0.2191]    )
  elif model_name == "focalnet-tiny-lrf":
    lowercase							    =       torch.tensor([1.1669, 0.0125, -0.1695]    )
  elif model_name == "focalnet-small":
    lowercase							    =       torch.tensor([0.4917, -0.0430, 0.1341]    )
  elif model_name == "focalnet-small-lrf":
    lowercase							    =       torch.tensor([-0.2588, -0.5342, -0.2331]    )
  elif model_name == "focalnet-base":
    lowercase							    =       torch.tensor([-0.1655, -0.4090, -0.1730]    )
  elif model_name == "focalnet-base-lrf":
    lowercase							    =       torch.tensor([0.5306, -0.0483, -0.3928]    )
  assert torch.allclose(outputs.logits[0, :3]		,					_a		,					atol=1e-4    )
  print('Looks ok!'    )

  if pytorch_dump_folder_path is not None:
    print(f'Saving model and processor of {model_name} to {pytorch_dump_folder_path}'    )
    model.save_pretrained(_a    )
    processor.save_pretrained(_a    )

  if push_to_hub:
    print(f'Pushing model and processor of {model_name} to the hub...'    )
    model.push_to_hub(f'{model_name}'    )
    processor.push_to_hub(f'{model_name}'    )


if __name__ == "__main__":
    _UpperCamelCase		:  Tuple						=		argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default='focalnet-tiny',
        type=str,
        help='Name of the FocalNet model you\'d like to convert.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )
    parser.add_argument(
        '--push_to_hub',
        action='store_true',
        help='Whether to push the model and processor to the hub.',
    )

    _UpperCamelCase		:  Optional[int]						=		parser.parse_args()
    convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""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'''  ,     )





