SCREAMING_SNAKE_CASE__		:					Dict						=					"0.18.2"

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
						if not is_onnx_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from .utils.dummy_onnx_objects import *  # noqa F403
else:
						from .pipelines import OnnxRuntimeModel

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from .utils.dummy_pt_objects import *  # noqa F403
else:
						from .models import (
						    AutoencoderKL,
						    ControlNetModel,
						    ModelMixin,
						    PriorTransformer,
						    TaFilmDecoder,
						    TransformeraDModel,
						    UNetaDModel,
						    UNetaDConditionModel,
						    UNetaDModel,
						    UNetaDConditionModel,
						    VQModel,
						)
						from .optimization import (
						    get_constant_schedule,
						    get_constant_schedule_with_warmup,
						    get_cosine_schedule_with_warmup,
						    get_cosine_with_hard_restarts_schedule_with_warmup,
						    get_linear_schedule_with_warmup,
						    get_polynomial_decay_schedule_with_warmup,
						    get_scheduler,
						)
						from .pipelines import (
						    AudioPipelineOutput,
						    ConsistencyModelPipeline,
						    DanceDiffusionPipeline,
						    DDIMPipeline,
						    DDPMPipeline,
						    DiffusionPipeline,
						    DiTPipeline,
						    ImagePipelineOutput,
						    KarrasVePipeline,
						    LDMPipeline,
						    LDMSuperResolutionPipeline,
						    PNDMPipeline,
						    RePaintPipeline,
						    ScoreSdeVePipeline,
						)
						from .schedulers import (
						    CMStochasticIterativeScheduler,
						    DDIMInverseScheduler,
						    DDIMParallelScheduler,
						    DDIMScheduler,
						    DDPMParallelScheduler,
						    DDPMScheduler,
						    DEISMultistepScheduler,
						    DPMSolverMultistepInverseScheduler,
						    DPMSolverMultistepScheduler,
						    DPMSolverSinglestepScheduler,
						    EulerAncestralDiscreteScheduler,
						    EulerDiscreteScheduler,
						    HeunDiscreteScheduler,
						    IPNDMScheduler,
						    KarrasVeScheduler,
						    KDPMaAncestralDiscreteScheduler,
						    KDPMaDiscreteScheduler,
						    PNDMScheduler,
						    RePaintScheduler,
						    SchedulerMixin,
						    ScoreSdeVeScheduler,
						    UnCLIPScheduler,
						    UniPCMultistepScheduler,
						    VQDiffusionScheduler,
						)
						from .training_utils import EMAModel

try:
						if not (is_torch_available() and is_scipy_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
						from .schedulers import LMSDiscreteScheduler

try:
						if not (is_torch_available() and is_torchsde_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
						from .schedulers import DPMSolverSDEScheduler

try:
						if not (is_torch_available() and is_transformers_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
						from .pipelines import (
						    AltDiffusionImgaImgPipeline,
						    AltDiffusionPipeline,
						    AudioLDMPipeline,
						    CycleDiffusionPipeline,
						    IFImgaImgPipeline,
						    IFImgaImgSuperResolutionPipeline,
						    IFInpaintingPipeline,
						    IFInpaintingSuperResolutionPipeline,
						    IFPipeline,
						    IFSuperResolutionPipeline,
						    ImageTextPipelineOutput,
						    KandinskyImgaImgPipeline,
						    KandinskyInpaintPipeline,
						    KandinskyPipeline,
						    KandinskyPriorPipeline,
						    KandinskyVaaControlnetImgaImgPipeline,
						    KandinskyVaaControlnetPipeline,
						    KandinskyVaaImgaImgPipeline,
						    KandinskyVaaInpaintPipeline,
						    KandinskyVaaPipeline,
						    KandinskyVaaPriorEmbaEmbPipeline,
						    KandinskyVaaPriorPipeline,
						    LDMTextToImagePipeline,
						    PaintByExamplePipeline,
						    SemanticStableDiffusionPipeline,
						    ShapEImgaImgPipeline,
						    ShapEPipeline,
						    StableDiffusionAttendAndExcitePipeline,
						    StableDiffusionControlNetImgaImgPipeline,
						    StableDiffusionControlNetInpaintPipeline,
						    StableDiffusionControlNetPipeline,
						    StableDiffusionDepthaImgPipeline,
						    StableDiffusionDiffEditPipeline,
						    StableDiffusionImageVariationPipeline,
						    StableDiffusionImgaImgPipeline,
						    StableDiffusionInpaintPipeline,
						    StableDiffusionInpaintPipelineLegacy,
						    StableDiffusionInstructPixaPixPipeline,
						    StableDiffusionLatentUpscalePipeline,
						    StableDiffusionLDMaDPipeline,
						    StableDiffusionModelEditingPipeline,
						    StableDiffusionPanoramaPipeline,
						    StableDiffusionParadigmsPipeline,
						    StableDiffusionPipeline,
						    StableDiffusionPipelineSafe,
						    StableDiffusionPixaPixZeroPipeline,
						    StableDiffusionSAGPipeline,
						    StableDiffusionUpscalePipeline,
						    StableUnCLIPImgaImgPipeline,
						    StableUnCLIPPipeline,
						    TextToVideoSDPipeline,
						    TextToVideoZeroPipeline,
						    UnCLIPImageVariationPipeline,
						    UnCLIPPipeline,
						    UniDiffuserModel,
						    UniDiffuserPipeline,
						    UniDiffuserTextDecoder,
						    VersatileDiffusionDualGuidedPipeline,
						    VersatileDiffusionImageVariationPipeline,
						    VersatileDiffusionPipeline,
						    VersatileDiffusionTextToImagePipeline,
						    VideoToVideoSDPipeline,
						    VQDiffusionPipeline,
						)

try:
						if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
						from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
						if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
						from .pipelines import StableDiffusionKDiffusionPipeline

try:
						if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
						from .pipelines import (
						    OnnxStableDiffusionImgaImgPipeline,
						    OnnxStableDiffusionInpaintPipeline,
						    OnnxStableDiffusionInpaintPipelineLegacy,
						    OnnxStableDiffusionPipeline,
						    OnnxStableDiffusionUpscalePipeline,
						    StableDiffusionOnnxPipeline,
						)

try:
						if not (is_torch_available() and is_librosa_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
						from .pipelines import AudioDiffusionPipeline, Mel

try:
						if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
						from .pipelines import SpectrogramDiffusionPipeline

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from .utils.dummy_flax_objects import *  # noqa F403
else:
						from .models.controlnet_flax import FlaxControlNetModel
						from .models.modeling_flax_utils import FlaxModelMixin
						from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
						from .models.vae_flax import FlaxAutoencoderKL
						from .pipelines import FlaxDiffusionPipeline
						from .schedulers import (
						    FlaxDDIMScheduler,
						    FlaxDDPMScheduler,
						    FlaxDPMSolverMultistepScheduler,
						    FlaxKarrasVeScheduler,
						    FlaxLMSDiscreteScheduler,
						    FlaxPNDMScheduler,
						    FlaxSchedulerMixin,
						    FlaxScoreSdeVeScheduler,
						)


try:
						if not (is_flax_available() and is_transformers_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
						from .pipelines import (
						    FlaxStableDiffusionControlNetPipeline,
						    FlaxStableDiffusionImgaImgPipeline,
						    FlaxStableDiffusionInpaintPipeline,
						    FlaxStableDiffusionPipeline,
						)

try:
						if not (is_note_seq_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from .utils.dummy_note_seq_objects import *  # noqa F403
else:
						from .pipelines import MidiProcessor




import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
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


SCREAMING_SNAKE_CASE__		:					List[Any]						=					get_tests_dir("fixtures/test_sentencepiece_no_bos.model")

@require_sentencepiece
@require_tokenizers
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PegasusTokenizer
  lowercase_			=     PegasusTokenizerFast
  lowercase_			=     True
  lowercase_			=     True




  def 	__lowercase(      self     :	int				)->       List[Any]:




       """simple docstring"""
       super().setUp()

       # We have a SentencePiece fixture for testing
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		PegasusTokenizer(a_				)
       tokenizer.save_pretrained(self.tmpdirname				)




  @cached_property
  def 	__lowercase(      self     :	Optional[Any]				)->       Optional[int]:




       """simple docstring"""
       return PegasusTokenizer.from_pretrained('google/pegasus-large'				)




  def 	__lowercase(      self     :	Any     , **a_     :	Optional[Any]				)->       PegasusTokenizer:




       """simple docstring"""
       return PegasusTokenizer.from_pretrained(self.tmpdirname     , **a_				)




  def 	__lowercase(      self     :	Union[str, Any]     , a_     :	List[Any]				)->       Optional[int]:




       """simple docstring"""
       return ("This is a test", "This is a test")




  def 	__lowercase(      self     :	Optional[int]				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		'</s>'
       SCREAMING_SNAKE_CASE__				:				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 	__lowercase(      self     :	Dict				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		list(self.get_tokenizer().get_vocab().keys()				)

       self.assertEqual(vocab_keys[0]     , '<pad>'				)
       self.assertEqual(vocab_keys[1]     , '</s>'				)
       self.assertEqual(vocab_keys[-1]     , 'v'				)
       self.assertEqual(len(a_				)     , 1103				)




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




       """simple docstring"""
       self.assertEqual(self.get_tokenizer().vocab_size     , 1103				)




  def 	__lowercase(      self     :	List[Any]				)->       Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.rust_tokenizer_class.from_pretrained(self.tmpdirname				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.tokenizer_class.from_pretrained(self.tmpdirname				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		(
           'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
           ' </s> <pad> <pad> <pad>'
       )
       SCREAMING_SNAKE_CASE__				:				List[str]			=		rust_tokenizer([raw_input_str]     , return_tensors=a_     , add_special_tokens=a_				).input_ids[0]
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		py_tokenizer([raw_input_str]     , return_tensors=a_     , add_special_tokens=a_				).input_ids[0]
       self.assertListEqual(a_     , a_				)




  def 	__lowercase(      self     :	Any				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self._large_tokenizer
       # <mask_1> masks whole sentence while <mask_2> masks single word
       SCREAMING_SNAKE_CASE__				:				Any			=		'<mask_1> To ensure a <mask_2> flow of bank resolutions.'
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		tokenizer([raw_input_str]     , return_tensors=a_				).input_ids[0]
       self.assertListEqual(a_     , a_				)




  def 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		self._large_tokenizer
       # The tracebacks for the following asserts are **better** without messages or self.assertEqual
       assert tokenizer.vocab_size == 9_6103
       assert tokenizer.pad_token_id == 0
       assert tokenizer.eos_token_id == 1
       assert tokenizer.offset == 103
       assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
       assert tokenizer.unk_token == "<unk>"
       assert tokenizer.model_max_length == 1024
       SCREAMING_SNAKE_CASE__				:				int			=		'To ensure a smooth flow of bank resolutions.'
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		tokenizer([raw_input_str]     , return_tensors=a_				).input_ids[0]
       self.assertListEqual(a_     , a_				)
       assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]				) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]




  @require_torch
  def 	__lowercase(      self     :	Union[str, Any]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		['This is going to be way too long.' * 150, 'short example']
       SCREAMING_SNAKE_CASE__				:				int			=		['not super long but more than 5 tokens', 'tiny']
       SCREAMING_SNAKE_CASE__				:				int			=		self._large_tokenizer(a_     , padding=a_     , truncation=a_     , return_tensors='pt'				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self._large_tokenizer(
           text_target=a_     , max_length=5     , padding=a_     , truncation=a_     , return_tensors='pt'				)

       assert batch.input_ids.shape == (2, 1024)
       assert batch.attention_mask.shape == (2, 1024)
       assert targets["input_ids"].shape == (2, 5)
       assert len(a_				) == 2  # input_ids, attention_mask.




  @slow
  def 	__lowercase(      self     :	Any				)->       str:




       """simple docstring"""
       # fmt: off
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		{'input_ids': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 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]], '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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-pegasus-large-arxiv'     , revision='ba85d0851d708441f91440d509690f1ab6353415'     , )




@require_sentencepiece
@require_tokenizers
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PegasusTokenizer
  lowercase_			=     PegasusTokenizerFast
  lowercase_			=     True
  lowercase_			=     True




  def 	__lowercase(      self     :	Any				)->       Union[str, Any]:




       """simple docstring"""
       super().setUp()

       # We have a SentencePiece fixture for testing
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		PegasusTokenizer(a_     , offset=0     , mask_token_sent=a_     , mask_token='[MASK]'				)
       tokenizer.save_pretrained(self.tmpdirname				)




  @cached_property
  def 	__lowercase(      self     :	Optional[Any]				)->       List[str]:




       """simple docstring"""
       return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'				)




  def 	__lowercase(      self     :	List[str]     , **a_     :	Optional[Any]				)->       PegasusTokenizer:




       """simple docstring"""
       return PegasusTokenizer.from_pretrained(self.tmpdirname     , **a_				)




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	Tuple				)->       str:




       """simple docstring"""
       return ("This is a test", "This is a test")




  def 	__lowercase(      self     :	str				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.rust_tokenizer_class.from_pretrained(self.tmpdirname				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.tokenizer_class.from_pretrained(self.tmpdirname				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		(
           'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
           ' <pad> <pad> <pad>'
       )
       SCREAMING_SNAKE_CASE__				:				str			=		rust_tokenizer([raw_input_str]     , return_tensors=a_     , add_special_tokens=a_				).input_ids[0]
       SCREAMING_SNAKE_CASE__				:				str			=		py_tokenizer([raw_input_str]     , return_tensors=a_     , add_special_tokens=a_				).input_ids[0]
       self.assertListEqual(a_     , a_				)




  @require_torch
  def 	__lowercase(      self     :	List[str]				)->       Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		['This is going to be way too long.' * 1000, 'short example']
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		['not super long but more than 5 tokens', 'tiny']
       SCREAMING_SNAKE_CASE__				:				str			=		self._large_tokenizer(a_     , padding=a_     , truncation=a_     , return_tensors='pt'				)
       SCREAMING_SNAKE_CASE__				:				int			=		self._large_tokenizer(
           text_target=a_     , max_length=5     , padding=a_     , truncation=a_     , return_tensors='pt'				)

       assert batch.input_ids.shape == (2, 4096)
       assert batch.attention_mask.shape == (2, 4096)
       assert targets["input_ids"].shape == (2, 5)
       assert len(a_				) == 2  # input_ids, attention_mask.




  def 	__lowercase(      self     :	Dict				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		(
           'This is an example string that is used to test the original TF implementation against the HF'
           ' implementation'
       )

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self._large_tokenizer(a_				).input_ids

       self.assertListEqual(
           a_     , [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]     , )



