


import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


_lowerCAmelCase:						Tuple   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Union[str, Any]   				=       OrderedDict(
    [
        ('align', 'EfficientNetImageProcessor'),
        ('beit', 'BeitImageProcessor'),
        ('bit', 'BitImageProcessor'),
        ('blip', 'BlipImageProcessor'),
        ('blip-2', 'BlipImageProcessor'),
        ('bridgetower', 'BridgeTowerImageProcessor'),
        ('chinese_clip', 'ChineseCLIPImageProcessor'),
        ('clip', 'CLIPImageProcessor'),
        ('clipseg', 'ViTImageProcessor'),
        ('conditional_detr', 'ConditionalDetrImageProcessor'),
        ('convnext', 'ConvNextImageProcessor'),
        ('convnextv2', 'ConvNextImageProcessor'),
        ('cvt', 'ConvNextImageProcessor'),
        ('data2vec-vision', 'BeitImageProcessor'),
        ('deformable_detr', 'DeformableDetrImageProcessor'),
        ('deit', 'DeiTImageProcessor'),
        ('deta', 'DetaImageProcessor'),
        ('detr', 'DetrImageProcessor'),
        ('dinat', 'ViTImageProcessor'),
        ('donut-swin', 'DonutImageProcessor'),
        ('dpt', 'DPTImageProcessor'),
        ('efficientformer', 'EfficientFormerImageProcessor'),
        ('efficientnet', 'EfficientNetImageProcessor'),
        ('flava', 'FlavaImageProcessor'),
        ('focalnet', 'BitImageProcessor'),
        ('git', 'CLIPImageProcessor'),
        ('glpn', 'GLPNImageProcessor'),
        ('groupvit', 'CLIPImageProcessor'),
        ('imagegpt', 'ImageGPTImageProcessor'),
        ('instructblip', 'BlipImageProcessor'),
        ('layoutlmv2', 'LayoutLMv2ImageProcessor'),
        ('layoutlmv3', 'LayoutLMv3ImageProcessor'),
        ('levit', 'LevitImageProcessor'),
        ('mask2former', 'Mask2FormerImageProcessor'),
        ('maskformer', 'MaskFormerImageProcessor'),
        ('mgp-str', 'ViTImageProcessor'),
        ('mobilenet_v1', 'MobileNetV1ImageProcessor'),
        ('mobilenet_v2', 'MobileNetV2ImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevitv2', 'MobileViTImageProcessor'),
        ('nat', 'ViTImageProcessor'),
        ('oneformer', 'OneFormerImageProcessor'),
        ('owlvit', 'OwlViTImageProcessor'),
        ('perceiver', 'PerceiverImageProcessor'),
        ('pix2struct', 'Pix2StructImageProcessor'),
        ('poolformer', 'PoolFormerImageProcessor'),
        ('regnet', 'ConvNextImageProcessor'),
        ('resnet', 'ConvNextImageProcessor'),
        ('sam', 'SamImageProcessor'),
        ('segformer', 'SegformerImageProcessor'),
        ('swiftformer', 'ViTImageProcessor'),
        ('swin', 'ViTImageProcessor'),
        ('swin2sr', 'Swin2SRImageProcessor'),
        ('swinv2', 'ViTImageProcessor'),
        ('table-transformer', 'DetrImageProcessor'),
        ('timesformer', 'VideoMAEImageProcessor'),
        ('tvlt', 'TvltImageProcessor'),
        ('upernet', 'SegformerImageProcessor'),
        ('van', 'ConvNextImageProcessor'),
        ('videomae', 'VideoMAEImageProcessor'),
        ('vilt', 'ViltImageProcessor'),
        ('vit', 'ViTImageProcessor'),
        ('vit_hybrid', 'ViTHybridImageProcessor'),
        ('vit_mae', 'ViTImageProcessor'),
        ('vit_msn', 'ViTImageProcessor'),
        ('xclip', 'CLIPImageProcessor'),
        ('yolos', 'YolosImageProcessor'),
    ]
)

_lowerCAmelCase:						List[str]   				=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)






def   _lowercase(					__a				:						str     ):
							for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
														if class_name in extractors:
																					a__      =model_type_to_module_name(__a     )

																					a__      =importlib.import_module(f""".{module_name}"""    ,     'transformers.models'     )
																					try:
																												return getattr(__a    ,     __a     )
																					except AttributeError:
																												continue

							for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
														if getattr(__a    ,     '__name__'    ,     __a     ) == class_name:
																					return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
							a__      =importlib.import_module('transformers'     )
							if hasattr(__a    ,     __a     ):
														return getattr(__a    ,     __a     )

							return None






def   _lowercase(					__a				:						Union[str, os.PathLike]    ,     __a				:						Optional[Union[str, os.PathLike]] = None    ,     __a				:						bool = False    ,     __a				:						bool = False    ,     __a				:						Optional[Dict[str, str]] = None    ,     __a				:						Optional[Union[bool, str]] = None    ,     __a				:						Optional[str] = None    ,     __a				:						bool = False    ,     **__a				:						Any    ,     ):
							a__      =get_file_from_repo(
							    __a    ,     __a    ,     cache_dir=__a    ,     force_download=__a    ,     resume_download=__a    ,     proxies=__a    ,     use_auth_token=__a    ,     revision=__a    ,     local_files_only=__a    ,     )
							if resolved_config_file is None:
														logger.info(
														    'Could not locate the image processor configuration file, will try to use the model config instead.'     )
														return {}

							with open(__a    ,     encoding='utf-8'     ) as reader:
														return json.load(__a     )







class       lowercase_					:





				def __init__(				self)  -> str:
											raise EnvironmentError(
											    'AutoImageProcessor is designed to be instantiated '
											    'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.')





				@classmethod
				@replace_list_option_in_docstrings(lowercase_)
				def 						__UpperCamelCase    (				cls       ,  lowercase_       ,  **lowercase_)  -> str:
											a__      =kwargs.pop('config'       ,  lowercase_)
											a__      =kwargs.pop('trust_remote_code'       ,  lowercase_)
											a__      =True

											a__    ,     a__      =ImageProcessingMixin.get_image_processor_dict(lowercase_       ,  **lowercase_)
											a__      =config_dict.get('image_processor_type'       ,  lowercase_)
											a__      =None
											if "AutoImageProcessor" in config_dict.get('auto_map'       ,  {}):
																		a__      =config_dict['auto_map']['AutoImageProcessor']

											# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
											# and if so, infer the image processor class from there.
											if image_processor_class is None and image_processor_auto_map is None:
																		a__      =config_dict.pop('feature_extractor_type'       ,  lowercase_)
																		if feature_extractor_class is not None:
																									logger.warning(
																									    'Could not find image processor class in the image processor config or the model config. Loading'
																									    ' based on pattern matching with the model\'s feature extractor configuration.')
																									a__      =feature_extractor_class.replace('FeatureExtractor'       ,  'ImageProcessor')
																		if "AutoFeatureExtractor" in config_dict.get('auto_map'       ,  {}):
																									a__      =config_dict['auto_map']['AutoFeatureExtractor']
																									a__      =feature_extractor_auto_map.replace('FeatureExtractor'       ,  'ImageProcessor')
																									logger.warning(
																									    'Could not find image processor auto map in the image processor config or the model config.'
																									    ' Loading based on pattern matching with the model\'s feature extractor configuration.')

        # If we don't find the image processor class in the image processor config, let's try the model config.
											if image_processor_class is None and image_processor_auto_map is None:
																		if not isinstance(lowercase_       ,  lowercase_):
																									a__      =AutoConfig.from_pretrained(lowercase_       ,  **lowercase_)
																		# It could be in `config.image_processor_type``
																		a__      =getattr(lowercase_       ,  'image_processor_type'       ,  lowercase_)
																		if hasattr(lowercase_       ,  'auto_map') and "AutoImageProcessor" in config.auto_map:
																									a__      =config.auto_map['AutoImageProcessor']

											if image_processor_class is not None:
																		a__      =image_processor_class_from_name(lowercase_)

											a__      =image_processor_auto_map is not None
											a__      =image_processor_class is not None or type(lowercase_) in IMAGE_PROCESSOR_MAPPING
											a__      =resolve_trust_remote_code(
											    lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_)

											if has_remote_code and trust_remote_code:
																		a__      =get_class_from_dynamic_module(
																		    lowercase_       ,  lowercase_       ,  **lowercase_)
																		a__      =kwargs.pop('code_revision'       ,  lowercase_)
																		if os.path.isdir(lowercase_):
																									image_processor_class.register_for_auto_class()
																		return image_processor_class.from_dict(lowercase_       ,  **lowercase_)
											elif image_processor_class is not None:
																		return image_processor_class.from_dict(lowercase_       ,  **lowercase_)
											# Last try: we use the IMAGE_PROCESSOR_MAPPING.
											elif type(lowercase_) in IMAGE_PROCESSOR_MAPPING:
																		a__      =IMAGE_PROCESSOR_MAPPING[type(lowercase_)]
																		return image_processor_class.from_dict(lowercase_       ,  **lowercase_)

											raise ValueError(
											    F"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
											    F"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
											    F"""`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys())}""")





				@staticmethod
				def 						__UpperCamelCase    (				lowercase_       ,  lowercase_)  -> Union[str, Any]:
											IMAGE_PROCESSOR_MAPPING.register(lowercase_       ,  lowercase_)



import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase											=    """▁"""

_lowercase											=    get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
@require_tokenizers
class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						BigBirdTokenizer
       __lowerCamelCase				       =						BigBirdTokenizerFast
       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						True


       def 	_snake_case				(    self				)						->    Tuple:
              super().setUp()

              SCREAMING_SNAKE_CASE_							:				str		=self.tokenizer_class(__A						,       keep_accents=__A				)
              tokenizer.save_pretrained(self.tmpdirname				)


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				str		='''<s>'''
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=1

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


       def 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				str		=list(self.get_tokenizer().get_vocab().keys()				)

              self.assertEqual(vocab_keys[0]						,       '''<unk>'''				)
              self.assertEqual(vocab_keys[1]						,       '''<s>'''				)
              self.assertEqual(vocab_keys[-1]						,       '''[MASK]'''				)
              self.assertEqual(len(__A				)						,       1_004				)


       def 	_snake_case				(    self				)						->    Optional[Any]:
              self.assertEqual(self.get_tokenizer().vocab_size						,       1_000				)


       def 	_snake_case				(    self				)						->    str:
              if not self.test_rust_tokenizer:
                     return

              SCREAMING_SNAKE_CASE_							:				Dict		=self.get_tokenizer()
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.get_rust_tokenizer()

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		='''I was born in 92000, and this is falsé.'''

              SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer.tokenize(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=rust_tokenizer.tokenize(__A				)
              self.assertListEqual(__A						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tokenizer.encode(__A						,       add_special_tokens=__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=rust_tokenizer.encode(__A						,       add_special_tokens=__A				)
              self.assertListEqual(__A						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.get_rust_tokenizer()
              SCREAMING_SNAKE_CASE_							:				int		=tokenizer.encode(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=rust_tokenizer.encode(__A				)
              self.assertListEqual(__A						,       __A				)


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=BigBirdTokenizer(__A						,       keep_accents=__A				)

              SCREAMING_SNAKE_CASE_							:				str		=tokenizer.tokenize('''This is a test'''				)
              self.assertListEqual(__A						,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

              self.assertListEqual(
                  tokenizer.convert_tokens_to_ids(__A				)						,       [285, 46, 10, 170, 382]						,       )

              SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
              self.assertListEqual(
                  __A						,       [
                      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''',
                      '''.''',
                  ]						,       )
              SCREAMING_SNAKE_CASE_							:				Dict		=tokenizer.convert_tokens_to_ids(__A				)
              self.assertListEqual(
                  __A						,       [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]						,       )

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=tokenizer.convert_ids_to_tokens(__A				)
              self.assertListEqual(
                  __A						,       [
                      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>''',
                      '''.''',
                  ]						,       )


       @cached_property
       def 	_snake_case				(    self				)						->    Union[str, Any]:
              return BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''				)


       @slow
       def 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				str		='''Hello World!'''
              SCREAMING_SNAKE_CASE_							:				Dict		=[65, 18_536, 2_260, 101, 66]

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


       @slow
       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Union[str, 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'''
              )
              # fmt: off
              SCREAMING_SNAKE_CASE_							:				List[Any]		=[65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66]  # noqa: E231
              # fmt: on
              self.assertListEqual(__A						,       self.big_tokenizer.encode(__A				)				)


       @require_torch
       @slow
       def 	_snake_case				(    self				)						->    int:
              import torch

              from transformers import BigBirdConfig, BigBirdModel

              # Build sequence
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=list(self.big_tokenizer.get_vocab().keys()				)[:10]
              SCREAMING_SNAKE_CASE_							:				Any		=''' '''.join(__A				)
              SCREAMING_SNAKE_CASE_							:				int		=self.big_tokenizer.encode_plus(__A						,       return_tensors='''pt'''						,       return_token_type_ids=__A				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.big_tokenizer.batch_encode_plus(
                  [sequence + ''' ''' + sequence]						,       return_tensors='''pt'''						,       return_token_type_ids=__A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=BigBirdConfig(attention_type='''original_full'''				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=BigBirdModel(__A				)

              assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

              with torch.no_grad():
                     model(**__A				)
                     model(**__A				)


       @slow
       def 	_snake_case				(    self				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				Any		=BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer.decode(tokenizer('''Paris is the [MASK].'''				).input_ids				)

              self.assertTrue(decoded_text == '''[CLS] Paris is the[MASK].[SEP]'''				)





       @slow
       def 	_snake_case				(    self				)						->    Any:
              # fmt: off
              SCREAMING_SNAKE_CASE_							:				Optional[int]		={'''input_ids''': [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 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], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 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]], '''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, 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, 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=__A						,       model_name='''google/bigbird-roberta-base'''						,       revision='''215c99f1600e06f83acce68422f2035b2b5c3510'''						,       )
