
"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class  A__       (	_lowerCamelCase       ,     unittest.TestCase):
		A_						:       Any				   =	KandinskyVaaInpaintPipeline
		A_						:       Tuple				   =	['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
		A_						:       List[str]				   =	[
		    'image_embeds',
		    'negative_image_embeds',
		    'image',
		    'mask_image',
		]
		A_						:       List[str]				   =	[
		    'generator',
		    'height',
		    'width',
		    'latents',
		    'guidance_scale',
		    'num_inference_steps',
		    'return_dict',
		    'guidance_scale',
		    'num_images_per_prompt',
		    'output_type',
		    'return_dict',
		]
		A_						:       Tuple				   =	False

		@property
		def 							__lowerCamelCase							(      self ):
			return 32

		@property
		def 							__lowerCamelCase							(      self ):
			return 32

		@property
		def 							__lowerCamelCase							(      self ):
			return self.time_input_dim

		@property
		def 							__lowerCamelCase							(      self ):
			return self.time_input_dim * 4

		@property
		def 							__lowerCamelCase							(      self ):
			return 1_00

		@property
		def 							__lowerCamelCase							(      self ):
			torch.manual_seed(0 )

			__lowerCAmelCase			:						Optional[Any]									=      {
			    'in_channels': 9,
			    # Out channels is double in channels because predicts mean and variance
			    'out_channels': 8,
			    'addition_embed_type': 'image',
			    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
			    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
			    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
			    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
			    'layers_per_block': 1,
			    'encoder_hid_dim': self.text_embedder_hidden_size,
			    'encoder_hid_dim_type': 'image_proj',
			    'cross_attention_dim': self.cross_attention_dim,
			    'attention_head_dim': 4,
			    'resnet_time_scale_shift': 'scale_shift',
			    'class_embed_type': None,
			}

			__lowerCAmelCase			:						Optional[Any]									=      UNetaDConditionModel(**_SCREAMING_SNAKE_CASE )
			return model

		@property
		def 							__lowerCamelCase							(      self ):
			return {
			    "block_out_channels": [32, 64],
			    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
			    "in_channels": 3,
			    "latent_channels": 4,
			    "layers_per_block": 1,
			    "norm_num_groups": 8,
			    "norm_type": "spatial",
			    "num_vq_embeddings": 12,
			    "out_channels": 3,
			    "up_block_types": [
			        "AttnUpDecoderBlock2D",
			        "UpDecoderBlock2D",
			    ],
			    "vq_embed_dim": 4,
			}

		@property
		def 							__lowerCamelCase							(      self ):
			torch.manual_seed(0 )
			__lowerCAmelCase			:						str									=      VQModel(**self.dummy_movq_kwargs )
			return model

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Dict									=      self.dummy_unet
			__lowerCAmelCase			:						List[str]									=      self.dummy_movq

			__lowerCAmelCase			:						Union[str, Any]									=      DDIMScheduler(
			    num_train_timesteps=10_00       ,		beta_schedule='linear'       ,		beta_start=0.0_0085       ,		beta_end=0.012       ,		clip_sample=_SCREAMING_SNAKE_CASE       ,		set_alpha_to_one=_SCREAMING_SNAKE_CASE       ,		steps_offset=1       ,		prediction_type='epsilon'       ,		thresholding=_SCREAMING_SNAKE_CASE       ,		)

			__lowerCAmelCase			:						str									=      {
			    'unet': unet,
			    'scheduler': scheduler,
			    'movq': movq,
			}

			return components

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=0 ):
			__lowerCAmelCase			:						List[Any]									=      floats_tensor((1, self.text_embedder_hidden_size)       ,		rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Optional[Any]									=      floats_tensor((1, self.text_embedder_hidden_size)       ,		rng=random.Random(seed + 1 ) ).to(
			    _SCREAMING_SNAKE_CASE )
			# create init_image
			__lowerCAmelCase			:						Tuple									=      floats_tensor((1, 3, 64, 64)       ,		rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						str									=      image.cpu().permute(0       ,		2       ,		3       ,		1 )[0]
			__lowerCAmelCase			:						List[Any]									=      Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE ) ).convert('RGB' ).resize((2_56, 2_56) )
			# create mask
			__lowerCAmelCase			:						Optional[Any]									=      np.ones((64, 64)       ,		dtype=np.floataa )
			__lowerCAmelCase			:						Dict									=      0

			if str(_SCREAMING_SNAKE_CASE ).startswith('mps' ):
				__lowerCAmelCase			:						Dict									=      torch.manual_seed(_SCREAMING_SNAKE_CASE )
			else:
				__lowerCAmelCase			:						Optional[int]									=      torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						int									=      {
			    'image': init_image,
			    'mask_image': mask,
			    'image_embeds': image_embeds,
			    'negative_image_embeds': negative_image_embeds,
			    'generator': generator,
			    'height': 64,
			    'width': 64,
			    'num_inference_steps': 2,
			    'guidance_scale': 4.0,
			    'output_type': 'np',
			}
			return inputs

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Dict									=      'cpu'

			__lowerCAmelCase			:						Union[str, Any]									=      self.get_dummy_components()

			__lowerCAmelCase			:						List[Any]									=      self.pipeline_class(**_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						str									=      pipe.to(_SCREAMING_SNAKE_CASE )

			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						List[str]									=      pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE ) )
			__lowerCAmelCase			:						Union[str, Any]									=      output.images

			__lowerCAmelCase			:						Optional[Any]									=      pipe(
			    **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )       ,		return_dict=_SCREAMING_SNAKE_CASE       ,		)[0]

			__lowerCAmelCase			:						str									=      image[0, -3:, -3:, -1]
			__lowerCAmelCase			:						int									=      image_from_tuple[0, -3:, -3:, -1]

			print(f"image.shape {image.shape}" )

			assert image.shape == (1, 64, 64, 3)

			__lowerCAmelCase			:						List[Any]									=      np.array(
			    [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848] )

			assert (
			    np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
			), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
			assert (
			    np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
			), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







		def 							__lowerCamelCase							(      self ):
			super().test_inference_batch_single_identical(expected_max_diff=3E-3 )




@slow
@require_torch_gpu
class  A__       (	unittest.TestCase):

		def 							__lowerCamelCase							(      self ):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[int]									=      load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy' )

			__lowerCAmelCase			:						Optional[int]									=      load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
			__lowerCAmelCase			:						Union[str, Any]									=      np.ones((7_68, 7_68)       ,		dtype=np.floataa )
			__lowerCAmelCase			:						List[str]									=      0

			__lowerCAmelCase			:						Dict									=      'a hat'

			__lowerCAmelCase			:						Optional[int]									=      KandinskyVaaPriorPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-prior'       ,		torch_dtype=torch.floataa )
			pipe_prior.to(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						str									=      KandinskyVaaInpaintPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-decoder-inpaint'       ,		torch_dtype=torch.floataa )
			__lowerCAmelCase			:						str									=      pipeline.to(_SCREAMING_SNAKE_CASE )
			pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						int									=      torch.Generator(device='cpu' ).manual_seed(0 )
			__lowerCAmelCase			,   __lowerCAmelCase			:						Union[str, Any]									=      pipe_prior(
			    _SCREAMING_SNAKE_CASE       ,		generator=_SCREAMING_SNAKE_CASE       ,		num_inference_steps=5       ,		negative_prompt=''       ,		).to_tuple()

			__lowerCAmelCase			:						int									=      pipeline(
			    image=_SCREAMING_SNAKE_CASE       ,		mask_image=_SCREAMING_SNAKE_CASE       ,		image_embeds=_SCREAMING_SNAKE_CASE       ,		negative_image_embeds=_SCREAMING_SNAKE_CASE       ,		generator=_SCREAMING_SNAKE_CASE       ,		num_inference_steps=1_00       ,		height=7_68       ,		width=7_68       ,		output_type='np'       ,		)

			__lowerCAmelCase			:						Optional[int]									=      output.images[0]

			assert image.shape == (7_68, 7_68, 3)

			assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

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


snake_case :		str							      =  "▁"

snake_case :		List[Any]							      =  get_tests_dir("fixtures/test_sentencepiece.model")





@require_sentencepiece
@require_tokenizers
class       _snake_case					(     snake_case				,						unittest.TestCase				):
			UpperCamelCase__          =    BigBirdTokenizer
			UpperCamelCase__          =    BigBirdTokenizerFast
			UpperCamelCase__          =    True
			UpperCamelCase__          =    True







			def 			SCREAMING_SNAKE_CASE    (				self		):
				super().setUp()

				__magic_name__		:		Optional[Any]	   =     self.tokenizer_class(_a						,   keep_accents=_a		)
				tokenizer.save_pretrained(self.tmpdirname		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Union[str, Any]	   =     "<s>"
				__magic_name__		:		Dict	   =     1

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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     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 			SCREAMING_SNAKE_CASE    (				self		):
				self.assertEqual(self.get_tokenizer().vocab_size						,   1_000		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				if not self.test_rust_tokenizer:
					return

				__magic_name__		:		Dict	   =     self.get_tokenizer()
				__magic_name__		:		str	   =     self.get_rust_tokenizer()

				__magic_name__		:		Any	   =     "I was born in 92000, and this is falsé."

				__magic_name__		:		Dict	   =     tokenizer.tokenize(_a		)
				__magic_name__		:		Any	   =     rust_tokenizer.tokenize(_a		)
				self.assertListEqual(_a						,   _a		)

				__magic_name__		:		List[Any]	   =     tokenizer.encode(_a						,   add_special_tokens=_a		)
				__magic_name__		:		List[str]	   =     rust_tokenizer.encode(_a						,   add_special_tokens=_a		)
				self.assertListEqual(_a						,   _a		)

				__magic_name__		:		str	   =     self.get_rust_tokenizer()
				__magic_name__		:		Dict	   =     tokenizer.encode(_a		)
				__magic_name__		:		Optional[int]	   =     rust_tokenizer.encode(_a		)
				self.assertListEqual(_a						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     BigBirdTokenizer(_a						,   keep_accents=_a		)

				__magic_name__		:		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]						,   )

				__magic_name__		:		Dict	   =     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",
				        ".",
				    ]						,   )
				__magic_name__		:		Union[str, Any]	   =     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]						,   )

				__magic_name__		:		int	   =     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 			SCREAMING_SNAKE_CASE    (				self		):
				return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"		)







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Any	   =     "Hello World!"
				__magic_name__		:		Dict	   =     [65, 18_536, 2_260, 101, 66]

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







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     (
				    "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
				__magic_name__		:		List[str]	   =     [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 			SCREAMING_SNAKE_CASE    (				self		):
				import torch

				from transformers import BigBirdConfig, BigBirdModel

				# Build sequence
				__magic_name__		:		Optional[Any]	   =     list(self.big_tokenizer.get_vocab().keys()		)[:10]
				__magic_name__		:		List[Any]	   =     " ".join(_a		)
				__magic_name__		:		Any	   =     self.big_tokenizer.encode_plus(_a						,   return_tensors="pt"						,   return_token_type_ids=_a		)
				__magic_name__		:		Union[str, Any]	   =     self.big_tokenizer.batch_encode_plus(
				    [sequence + " " + sequence]						,   return_tensors="pt"						,   return_token_type_ids=_a		)

				__magic_name__		:		List[str]	   =     BigBirdConfig(attention_type="original_full"		)
				__magic_name__		:		Optional[int]	   =     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 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		int	   =     BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"		)
				__magic_name__		:		int	   =     tokenizer.decode(tokenizer("Paris is the [MASK]."		).input_ids		)

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







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				# fmt: off
				__magic_name__		:		Optional[Any]	   =     {"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"						,   )
