






'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				unittest.TestCase				):
 snake_case_  							=							KandinskyVaaImgaImgPipeline
 snake_case_  							=							["""image_embeds""", """negative_image_embeds""", """image"""]
 snake_case_  							=							[
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
 ]
 snake_case_  							=							[
     """generator""",
     """height""",
     """width""",
     """strength""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 snake_case_  							=							False




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						Tuple:
       return 32




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						str:
       return 32




 @property
 def      __magic_name__	(      self	:							Any      )	->						Optional[int]:
       return self.time_input_dim




 @property
 def      __magic_name__	(      self	:							List[Any]      )	->						int:
       return self.time_input_dim * 4




 @property
 def      __magic_name__	(      self	:							Tuple      )	->						Optional[int]:
       return 1_00




 @property
 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Tuple:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={
           '''in_channels''': 4,
           # 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,
       }

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=UNetaDConditionModel(**__lowercase      )
       return model




 @property
 def      __magic_name__	(      self	:							Dict      )	->						Any:
       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      __magic_name__	(      self	:							Tuple      )	->						Optional[Any]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=VQModel(**self.dummy_movq_kwargs      )
       return model




 def      __magic_name__	(      self	:							str      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.dummy_unet
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.dummy_movq

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={
           '''num_train_timesteps''': 10_00,
           '''beta_schedule''': '''linear''',
           '''beta_start''': 0.00085,
           '''beta_end''': 0.012,
           '''clip_sample''': False,
           '''set_alpha_to_one''': False,
           '''steps_offset''': 0,
           '''prediction_type''': '''epsilon''',
           '''thresholding''': False,
       }

       SCREAMING_SNAKE_CASE__     :				str									=DDIMScheduler(**__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Any									={
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components




 def      __magic_name__	(      self	:							str					,      __lowercase	:							Optional[Any]					,      __lowercase	:							Any=0      )	->						int:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=floats_tensor((1, self.text_embedder_hidden_size)					,      rng=random.Random(__lowercase      )      ).to(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=floats_tensor((1, self.text_embedder_hidden_size)					,      rng=random.Random(seed + 1      )      ).to(
           __lowercase      )
       # create init_image
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=floats_tensor((1, 3, 64, 64)					,      rng=random.Random(__lowercase      )      ).to(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Dict									=image.cpu().permute(0					,      2					,      3					,      1      )[0]
       SCREAMING_SNAKE_CASE__     :				Any									=Image.fromarray(np.uinta(__lowercase      )      ).convert('''RGB'''      ).resize((2_56, 2_56)      )

       if str(__lowercase      ).startswith('''mps'''      ):
             SCREAMING_SNAKE_CASE__     :				Dict									=torch.manual_seed(__lowercase      )
       else:
             SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				str									={
           '''image''': init_image,
           '''image_embeds''': image_embeds,
           '''negative_image_embeds''': negative_image_embeds,
           '''generator''': generator,
           '''height''': 64,
           '''width''': 64,
           '''num_inference_steps''': 10,
           '''guidance_scale''': 7.0,
           '''strength''': 0.2,
           '''output_type''': '''np''',
       }
       return inputs




 def      __magic_name__	(      self	:							int      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									='''cpu'''

       SCREAMING_SNAKE_CASE__     :				Tuple									=self.get_dummy_components()

       SCREAMING_SNAKE_CASE__     :				Dict									=self.pipeline_class(**__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=pipe.to(__lowercase      )

       pipe.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Dict									=pipe(**self.get_dummy_inputs(__lowercase      )      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=output.images

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=pipe(
           **self.get_dummy_inputs(__lowercase      )					,      return_dict=__lowercase					,      )[0]

       SCREAMING_SNAKE_CASE__     :				List[Any]									=image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE__     :				List[str]									=image_from_tuple[0, -3:, -3:, -1]

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

       SCREAMING_SNAKE_CASE__     :				Tuple									=np.array(
           [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263]      )
       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()}"






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




 def      __magic_name__	(      self	:							int      )	->						Any:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				str									=load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_img2img_frog.npy'''      )

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									='''A red cartoon frog, 4k'''

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

       SCREAMING_SNAKE_CASE__     :				Any									=KandinskyVaaImgaImgPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-decoder'''					,      torch_dtype=torch.floataa      )
       SCREAMING_SNAKE_CASE__     :				Dict									=pipeline.to(__lowercase      )

       pipeline.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Generator(device='''cpu'''      ).manual_seed(0      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=pipe_prior(
           __lowercase					,      generator=__lowercase					,      num_inference_steps=5					,      negative_prompt=''''''					,      ).to_tuple()

       SCREAMING_SNAKE_CASE__     :				List[Any]									=pipeline(
           image=__lowercase					,      image_embeds=__lowercase					,      negative_image_embeds=__lowercase					,      generator=__lowercase					,      num_inference_steps=1_00					,      height=7_68					,      width=7_68					,      strength=0.2					,      output_type='''np'''					,      )

       SCREAMING_SNAKE_CASE__     :				int									=output.images[0]

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

       assert_mean_pixel_difference(__lowercase					,      __lowercase      )







'''simple docstring'''
import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch






class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):
 snake_case_  							=							JukeboxTokenizer
 snake_case_  							=							{
     """artist""": """Zac Brown Band""",
     """genres""": """Country""",
     """lyrics""": """I met a traveller from an antique land,
        Who said \"Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        """,
 }




 @require_torch
 def      __magic_name__	(      self	:							Optional[int]      )	->						str:
       import torch

       SCREAMING_SNAKE_CASE__     :				List[str]									=JukeboxTokenizer.from_pretrained('''openai/jukebox-1b-lyrics'''      )
       SCREAMING_SNAKE_CASE__     :				str									=tokenizer(**self.metas      )['''input_ids''']
       # fmt: off
       SCREAMING_SNAKE_CASE__     :				str									=[
           torch.tensor([[
               0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
               76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
               44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
               47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
               76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
               30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
               27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
               45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
               41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
               19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
               76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
               76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
               64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
               30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
               27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
               34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
               27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
               41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
               76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
               44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
               76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
               32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
               40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
               20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
               45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
               31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
               45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
               76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
               34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
               31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
               40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
               38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
               76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
               41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
               27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
               46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
               76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
               41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
               46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
               41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
               40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
               27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
               76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
               76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
               41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
               76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
               27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
               34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
               44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
               40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
               46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
               38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
               40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
               27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
               20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
               76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
               76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
               76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
               76, 76]]      ),
           torch.tensor([[0, 0, 0, 10_69, 11]]      ),
           torch.tensor([[0, 0, 0, 10_69, 11]]      ),
       ]
       # fmt: on
       self.assertTrue(torch.allclose(tokens[0]					,      EXPECTED_OUTPUT[0]      )      )
       self.assertTrue(torch.allclose(tokens[1]					,      EXPECTED_OUTPUT[1]      )      )
       self.assertTrue(torch.allclose(tokens[2]					,      EXPECTED_OUTPUT[2]      )      )




 @require_torch
 def      __magic_name__	(      self	:							Any      )	->						List[str]:
       import torch

       SCREAMING_SNAKE_CASE__     :				int									=JukeboxTokenizer.from_pretrained('''openai/jukebox-5b-lyrics'''      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=tokenizer(**self.metas      )['''input_ids''']
       # fmt: off
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=[
           torch.tensor([[
               0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
               31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
               31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
               40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
               79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
               77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
               27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
               37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
               32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
               77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
               77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
               77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
               46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
               77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
               77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
               77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
               77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
               64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
               40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
               40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
               38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
               31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
               41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
               77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
               46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
               41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
               31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
               31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
               23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
               44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
               31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
               38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
               40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
               27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
               31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
               34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
               31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
               1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
               31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
               45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
               31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
               15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
               11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
               45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
               41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
               44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
               46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
               27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
               35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
               77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
               31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
               41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
               77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
               40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
               77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
               77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
               27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
               77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
               77, 77, 77, 77, 77, 77]]      ),
           torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]      ),
           torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]      ),
       ]
       # fmt: on
       self.assertTrue(torch.allclose(tokens[0]					,      EXPECTED_OUTPUT[0]      )      )
       self.assertTrue(torch.allclose(tokens[1]					,      EXPECTED_OUTPUT[1]      )      )
       self.assertTrue(torch.allclose(tokens[2]					,      EXPECTED_OUTPUT[2]      )      )