

'''simple docstring'''





import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


UpperCamelCase_							=					logging.get_logger(__name__)  # pylint: disable=invalid-name





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				def __init__(					self    :		Optional[int]		,				UpperCAmelCase__    :		WhisperForConditionalGeneration		,				UpperCAmelCase__    :		WhisperProcessor		,				UpperCAmelCase__    :		AutoencoderKL		,				UpperCAmelCase__    :		CLIPTextModel		,				UpperCAmelCase__    :		CLIPTokenizer		,				UpperCAmelCase__    :		UNetaDConditionModel		,				UpperCAmelCase__    :		Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]		,				UpperCAmelCase__    :		StableDiffusionSafetyChecker		,				UpperCAmelCase__    :		CLIPImageProcessor		,				):
										'''simple docstring'''
										super().__init__()

										if safety_checker is None:
																logger.warning(
																    F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
																    ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
																    ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
																    ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
																    ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
																    ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'''      )

										self.register_modules(
										    speech_model=UpperCAmelCase__		,				speech_processor=UpperCAmelCase__		,				vae=UpperCAmelCase__		,				text_encoder=UpperCAmelCase__		,				tokenizer=UpperCAmelCase__		,				unet=UpperCAmelCase__		,				scheduler=UpperCAmelCase__		,				feature_extractor=UpperCAmelCase__		,				)







				def    lowerCamelCase_  (					self    :		Optional[Any]		,				UpperCAmelCase__    :		Optional[Union[str, int]] = "auto"      ):
										'''simple docstring'''
										if slice_size == "auto":
																lowercase   :       Optional[Any]								=self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										self.enable_attention_slicing(UpperCAmelCase__      )







				@torch.no_grad()
				def __call__(					self    :		Union[str, Any]		,				UpperCAmelCase__    :		Any		,				UpperCAmelCase__    :		Optional[Any]=16000		,				UpperCAmelCase__    :		int = 512		,				UpperCAmelCase__    :		int = 512		,				UpperCAmelCase__    :		int = 50		,				UpperCAmelCase__    :		float = 7.5		,				UpperCAmelCase__    :		Optional[Union[str, List[str]]] = None		,				UpperCAmelCase__    :		Optional[int] = 1		,				UpperCAmelCase__    :		float = 0.0		,				UpperCAmelCase__    :		Optional[torch.Generator] = None		,				UpperCAmelCase__    :		Optional[torch.FloatTensor] = None		,				UpperCAmelCase__    :		Optional[str] = "pil"		,				UpperCAmelCase__    :		bool = True		,				UpperCAmelCase__    :		Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,				UpperCAmelCase__    :		int = 1		,				**UpperCAmelCase__    :		Optional[int]		,				):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=self.speech_processor.feature_extractor(
										    UpperCAmelCase__		,				return_tensors='''pt'''		,				sampling_rate=UpperCAmelCase__      ).input_features.to(self.device      )
										lowercase   :       str								=self.speech_model.generate(UpperCAmelCase__		,				max_length=480000      )

										lowercase   :       Union[str, Any]								=self.speech_processor.tokenizer.batch_decode(UpperCAmelCase__		,				skip_special_tokens=UpperCAmelCase__		,				normalize=UpperCAmelCase__      )[
										    0
										]

										if isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ):
																lowercase   :       int								=1
										elif isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ):
																lowercase   :       List[str]								=len(UpperCAmelCase__      )
										else:
																raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase__      )}'''      )

										if height % 8 != 0 or width % 8 != 0:
																raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''      )

										if (callback_steps is None) or (
										    callback_steps is not None and (not isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ) or callback_steps <= 0)
										):
																raise ValueError(
																    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
																    F''' {type(UpperCAmelCase__      )}.'''      )

										# get prompt text embeddings
										lowercase   :       Dict								=self.tokenizer(
										    UpperCAmelCase__		,				padding='''max_length'''		,				max_length=self.tokenizer.model_max_length		,				return_tensors='''pt'''		,				)
										lowercase   :       Dict								=text_inputs.input_ids

										if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																lowercase   :       List[str]								=self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]      )
																logger.warning(
																    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
																    F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''      )
																lowercase   :       Any								=text_input_ids[:, : self.tokenizer.model_max_length]
										lowercase   :       Optional[Any]								=self.text_encoder(text_input_ids.to(self.device      )      )[0]

										# duplicate text embeddings for each generation per prompt, using mps friendly method
										lowercase		,  lowercase		,  lowercase   :       str								=text_embeddings.shape
										lowercase   :       Dict								=text_embeddings.repeat(1		,				UpperCAmelCase__		,				1      )
										lowercase   :       Dict								=text_embeddings.view(bs_embed * num_images_per_prompt		,				UpperCAmelCase__		,				-1      )

										# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
										# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
										# corresponds to doing no classifier free guidance.
										lowercase   :       Union[str, Any]								=guidance_scale > 1.0
										# get unconditional embeddings for classifier free guidance
										if do_classifier_free_guidance:
																lowercase   :       List[str]
																if negative_prompt is None:
																						lowercase   :       str								=[''''''] * batch_size
																elif type(UpperCAmelCase__      ) is not type(UpperCAmelCase__      ):
																						raise TypeError(
																						    F'''`negative_prompt` should be the same type to `prompt`, but got {type(UpperCAmelCase__      )} !='''
																						    F''' {type(UpperCAmelCase__      )}.'''      )
																elif isinstance(UpperCAmelCase__		,				UpperCAmelCase__      ):
																						lowercase   :       Union[str, Any]								=[negative_prompt]
																elif batch_size != len(UpperCAmelCase__      ):
																						raise ValueError(
																						    F'''`negative_prompt`: {negative_prompt} has batch size {len(UpperCAmelCase__      )}, but `prompt`:'''
																						    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
																						    ''' the batch size of `prompt`.'''      )
																else:
																						lowercase   :       str								=negative_prompt

																lowercase   :       Union[str, Any]								=text_input_ids.shape[-1]
																lowercase   :       Union[str, Any]								=self.tokenizer(
																    UpperCAmelCase__		,				padding='''max_length'''		,				max_length=UpperCAmelCase__		,				truncation=UpperCAmelCase__		,				return_tensors='''pt'''		,				)
																lowercase   :       Dict								=self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

																# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																lowercase   :       Tuple								=uncond_embeddings.shape[1]
																lowercase   :       Union[str, Any]								=uncond_embeddings.repeat(1		,				UpperCAmelCase__		,				1      )
																lowercase   :       Any								=uncond_embeddings.view(batch_size * num_images_per_prompt		,				UpperCAmelCase__		,				-1      )

																# For classifier free guidance, we need to do two forward passes.
																# Here we concatenate the unconditional and text embeddings into a single batch
																# to avoid doing two forward passes
																lowercase   :       Union[str, Any]								=torch.cat([uncond_embeddings, text_embeddings]      )

										# get the initial random noise unless the user supplied it

										# Unlike in other pipelines, latents need to be generated in the target device
										# for 1-to-1 results reproducibility with the CompVis implementation.
										# However this currently doesn't work in `mps`.
										lowercase   :       List[str]								=(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
										lowercase   :       str								=text_embeddings.dtype
										if latents is None:
																if self.device.type == "mps":
																						# randn does not exist on mps
																						lowercase   :       Dict								=torch.randn(UpperCAmelCase__		,				generator=UpperCAmelCase__		,				device='''cpu'''		,				dtype=UpperCAmelCase__      ).to(
																						    self.device      )
																else:
																						lowercase   :       Optional[Any]								=torch.randn(UpperCAmelCase__		,				generator=UpperCAmelCase__		,				device=self.device		,				dtype=UpperCAmelCase__      )
										else:
																if latents.shape != latents_shape:
																						raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''      )
																lowercase   :       Dict								=latents.to(self.device      )

										# set timesteps
										self.scheduler.set_timesteps(UpperCAmelCase__      )

										# Some schedulers like PNDM have timesteps as arrays
										# It's more optimized to move all timesteps to correct device beforehand
										lowercase   :       Any								=self.scheduler.timesteps.to(self.device      )

										# scale the initial noise by the standard deviation required by the scheduler
										lowercase   :       List[str]								=latents * self.scheduler.init_noise_sigma

										# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
										# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
										# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
										# and should be between [0, 1]
										lowercase   :       Dict								='''eta''' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
										lowercase   :       Tuple								={}
										if accepts_eta:
																lowercase   :       int								=eta

										for i, t in enumerate(self.progress_bar(UpperCAmelCase__      )      ):
																# expand the latents if we are doing classifier free guidance
																lowercase   :       str								=torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents
																lowercase   :       Optional[int]								=self.scheduler.scale_model_input(UpperCAmelCase__		,				UpperCAmelCase__      )

																# predict the noise residual
																lowercase   :       Union[str, Any]								=self.unet(UpperCAmelCase__		,				UpperCAmelCase__		,				encoder_hidden_states=UpperCAmelCase__      ).sample

																# perform guidance
																if do_classifier_free_guidance:
																						lowercase		,  lowercase   :       Optional[Any]								=noise_pred.chunk(2      )
																						lowercase   :       Optional[int]								=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																# compute the previous noisy sample x_t -> x_t-1
																lowercase   :       List[Any]								=self.scheduler.step(UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				**UpperCAmelCase__      ).prev_sample

																# call the callback, if provided
																if callback is not None and i % callback_steps == 0:
																						callback(UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__      )

										lowercase   :       List[str]								=1 / 0.1_82_15 * latents
										lowercase   :       List[str]								=self.vae.decode(UpperCAmelCase__      ).sample

										lowercase   :       Union[str, Any]								=(image / 2 + 0.5).clamp(0		,				1      )

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										lowercase   :       int								=image.cpu().permute(0		,				2		,				3		,				1      ).float().numpy()

										if output_type == "pil":
																lowercase   :       str								=self.numpy_to_pil(UpperCAmelCase__      )

										if not return_dict:
																return image

										return StableDiffusionPipelineOutput(images=UpperCAmelCase__		,				nsfw_content_detected=UpperCAmelCase__      )








'''simple docstring'''





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


UpperCamelCase_							=					"""▁"""

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





@require_sentencepiece
@require_tokenizers
class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						BigBirdTokenizer
				lowerCamelCase_							=						BigBirdTokenizerFast
				lowerCamelCase_							=						True
				lowerCamelCase_							=						True







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										super().setUp()

										lowercase   :       Optional[int]								=self.tokenizer_class(UpperCAmelCase__		,				keep_accents=UpperCAmelCase__      )
										tokenizer.save_pretrained(self.tmpdirname      )







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								='''<s>'''
										lowercase   :       int								=1

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







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       Dict								=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(UpperCAmelCase__      )		,				1004      )







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										self.assertEqual(self.get_tokenizer().vocab_size		,				1000      )







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										if not self.test_rust_tokenizer:
																return

										lowercase   :       Optional[int]								=self.get_tokenizer()
										lowercase   :       Any								=self.get_rust_tokenizer()

										lowercase   :       int								='''I was born in 92000, and this is falsé.'''

										lowercase   :       List[str]								=tokenizer.tokenize(UpperCAmelCase__      )
										lowercase   :       Dict								=rust_tokenizer.tokenize(UpperCAmelCase__      )
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )

										lowercase   :       str								=tokenizer.encode(UpperCAmelCase__		,				add_special_tokens=UpperCAmelCase__      )
										lowercase   :       Union[str, Any]								=rust_tokenizer.encode(UpperCAmelCase__		,				add_special_tokens=UpperCAmelCase__      )
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )

										lowercase   :       Optional[Any]								=self.get_rust_tokenizer()
										lowercase   :       Optional[Any]								=tokenizer.encode(UpperCAmelCase__      )
										lowercase   :       Union[str, Any]								=rust_tokenizer.encode(UpperCAmelCase__      )
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       Tuple								=BigBirdTokenizer(UpperCAmelCase__		,				keep_accents=UpperCAmelCase__      )

										lowercase   :       Tuple								=tokenizer.tokenize('''This is a test'''      )
										self.assertListEqual(UpperCAmelCase__		,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']      )

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

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

										lowercase   :       List[Any]								=tokenizer.convert_ids_to_tokens(UpperCAmelCase__      )
										self.assertListEqual(
										    UpperCAmelCase__		,				[
										        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    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										return BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''      )







				@slow
				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       str								='''Hello World!'''
										lowercase   :       Union[str, Any]								=[65, 18536, 2260, 101, 66]

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







				@slow
				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       int								=(
										    '''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
										lowercase   :       Tuple								=[65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66]  # noqa: E231
										# fmt: on
										self.assertListEqual(UpperCAmelCase__		,				self.big_tokenizer.encode(UpperCAmelCase__      )      )







				@require_torch
				@slow
				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										import torch

										from transformers import BigBirdConfig, BigBirdModel

										# Build sequence
										lowercase   :       List[str]								=list(self.big_tokenizer.get_vocab().keys()      )[:10]
										lowercase   :       Dict								=''' '''.join(UpperCAmelCase__      )
										lowercase   :       Union[str, Any]								=self.big_tokenizer.encode_plus(UpperCAmelCase__		,				return_tensors='''pt'''		,				return_token_type_ids=UpperCAmelCase__      )
										lowercase   :       Dict								=self.big_tokenizer.batch_encode_plus(
										    [sequence + ''' ''' + sequence]		,				return_tensors='''pt'''		,				return_token_type_ids=UpperCAmelCase__      )

										lowercase   :       Optional[int]								=BigBirdConfig(attention_type='''original_full'''      )
										lowercase   :       Dict								=BigBirdModel(UpperCAmelCase__      )

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

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







				@slow
				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''      )
										lowercase   :       Dict								=tokenizer.decode(tokenizer('''Paris is the [MASK].'''      ).input_ids      )

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







				@slow
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										# fmt: off
										lowercase   :       str								={'''input_ids''': [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 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, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 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
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										self.tokenizer_integration_test_util(
										    expected_encoding=UpperCAmelCase__		,				model_name='''google/bigbird-roberta-base'''		,				revision='''215c99f1600e06f83acce68422f2035b2b5c3510'''		,				)





