



"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
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


enable_full_determinism()
class    lowercase__     (					unittest.TestCase   ):







       '''simple docstring'''


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


             '''simple docstring'''




             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()



       @property
       def   lowercase__       (							self    :						Optional[Any]					)				->		Any:


             '''simple docstring'''




             UpperCAmelCase_    				=							1
             UpperCAmelCase_    				=							3
             UpperCAmelCase_    				=							(32, 32)

             UpperCAmelCase_    				=							floats_tensor((batch_size, num_channels) + sizes	,       rng=random.Random(0					)					).to(_UpperCAmelCase					)
             return image



       @property
       def   lowercase__       (							self    :						Any					)				->		Union[str, Any]:


             '''simple docstring'''




             torch.manual_seed(0					)
             UpperCAmelCase_    				=							UNetaDConditionModel(
                 block_out_channels=(32, 32, 64)	,       layers_per_block=2	,       sample_size=32	,       in_channels=7	,       out_channels=4	,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")	,       up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")	,       cross_attention_dim=32	,       attention_head_dim=8	,       use_linear_projection=_UpperCAmelCase	,       only_cross_attention=(True, True, False)	,       num_class_embeds=100	,       )
             return model



       @property
       def   lowercase__       (							self    :						int					)				->		int:


             '''simple docstring'''




             torch.manual_seed(0					)
             UpperCAmelCase_    				=							AutoencoderKL(
                 block_out_channels=[32, 32, 64]	,       in_channels=3	,       out_channels=3	,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"]	,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]	,       latent_channels=4	,       )
             return model



       @property
       def   lowercase__       (							self    :						Tuple					)				->		Optional[Any]:


             '''simple docstring'''




             torch.manual_seed(0					)
             UpperCAmelCase_    				=							CLIPTextConfig(
                 bos_token_id=0	,       eos_token_id=2	,       hidden_size=32	,       intermediate_size=37	,       layer_norm_eps=1e-05	,       num_attention_heads=4	,       num_hidden_layers=5	,       pad_token_id=1	,       vocab_size=1000	,       hidden_act="gelu"	,       projection_dim=512	,       )
             return CLIPTextModel(_UpperCAmelCase					)



       def   lowercase__       (							self    :						Optional[Any]					)				->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_    				=							"cpu"  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase_    				=							self.dummy_cond_unet_upscale
             UpperCAmelCase_    				=							DDPMScheduler()
             UpperCAmelCase_    				=							DDIMScheduler(prediction_type="v_prediction"					)
             UpperCAmelCase_    				=							self.dummy_vae
             UpperCAmelCase_    				=							self.dummy_text_encoder
             UpperCAmelCase_    				=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

             UpperCAmelCase_    				=							self.dummy_image.cpu().permute(0	,       2	,       3	,       1					)[0]
             UpperCAmelCase_    				=							Image.fromarray(np.uinta(_UpperCAmelCase					)					).convert("RGB"					).resize((64, 64)					)

             # make sure here that pndm scheduler skips prk
             UpperCAmelCase_    				=							StableDiffusionUpscalePipeline(
                 unet=_UpperCAmelCase	,       low_res_scheduler=_UpperCAmelCase	,       scheduler=_UpperCAmelCase	,       vae=_UpperCAmelCase	,       text_encoder=_UpperCAmelCase	,       tokenizer=_UpperCAmelCase	,       max_noise_level=350	,       )
             UpperCAmelCase_    				=							sd_pipe.to(_UpperCAmelCase					)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

             UpperCAmelCase_    				=							"A painting of a squirrel eating a burger"
             UpperCAmelCase_    				=							torch.Generator(device=_UpperCAmelCase					).manual_seed(0					)
             UpperCAmelCase_    				=							sd_pipe(
                 [prompt]	,       image=_UpperCAmelCase	,       generator=_UpperCAmelCase	,       guidance_scale=6.0	,       noise_level=20	,       num_inference_steps=2	,       output_type="np"	,       )

             UpperCAmelCase_    				=							output.images

             UpperCAmelCase_    				=							torch.Generator(device=_UpperCAmelCase					).manual_seed(0					)
             UpperCAmelCase_    				=							sd_pipe(
                 [prompt]	,       image=_UpperCAmelCase	,       generator=_UpperCAmelCase	,       guidance_scale=6.0	,       noise_level=20	,       num_inference_steps=2	,       output_type="np"	,       return_dict=_UpperCAmelCase	,       )[0]

             UpperCAmelCase_    				=							image[0, -3:, -3:, -1]
             UpperCAmelCase_    				=							image_from_tuple[0, -3:, -3:, -1]

             UpperCAmelCase_    				=							low_res_image.size[0] * 4
             assert image.shape == (1, expected_height_width, expected_height_width, 3)
             UpperCAmelCase_    				=							np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]					)

             assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
             assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2



       def   lowercase__       (							self    :						Any					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							"cpu"  # ensure determinism for the device-dependent torch.Generator
             UpperCAmelCase_    				=							self.dummy_cond_unet_upscale
             UpperCAmelCase_    				=							DDPMScheduler()
             UpperCAmelCase_    				=							DDIMScheduler(prediction_type="v_prediction"					)
             UpperCAmelCase_    				=							self.dummy_vae
             UpperCAmelCase_    				=							self.dummy_text_encoder
             UpperCAmelCase_    				=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

             UpperCAmelCase_    				=							self.dummy_image.cpu().permute(0	,       2	,       3	,       1					)[0]
             UpperCAmelCase_    				=							Image.fromarray(np.uinta(_UpperCAmelCase					)					).convert("RGB"					).resize((64, 64)					)

             # make sure here that pndm scheduler skips prk
             UpperCAmelCase_    				=							StableDiffusionUpscalePipeline(
                 unet=_UpperCAmelCase	,       low_res_scheduler=_UpperCAmelCase	,       scheduler=_UpperCAmelCase	,       vae=_UpperCAmelCase	,       text_encoder=_UpperCAmelCase	,       tokenizer=_UpperCAmelCase	,       max_noise_level=350	,       )
             UpperCAmelCase_    				=							sd_pipe.to(_UpperCAmelCase					)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

             UpperCAmelCase_    				=							"A painting of a squirrel eating a burger"
             UpperCAmelCase_    				=							sd_pipe(
                 2 * [prompt]	,       image=2 * [low_res_image]	,       guidance_scale=6.0	,       noise_level=20	,       num_inference_steps=2	,       output_type="np"	,       )
             UpperCAmelCase_    				=							output.images
             assert image.shape[0] == 2

             UpperCAmelCase_    				=							torch.Generator(device=_UpperCAmelCase					).manual_seed(0					)
             UpperCAmelCase_    				=							sd_pipe(
                 [prompt]	,       image=_UpperCAmelCase	,       generator=_UpperCAmelCase	,       num_images_per_prompt=2	,       guidance_scale=6.0	,       noise_level=20	,       num_inference_steps=2	,       output_type="np"	,       )
             UpperCAmelCase_    				=							output.images
             assert image.shape[0] == 2







       @unittest.skipIf(torch_device != "cuda"	,       "This test requires a GPU"					)
       def   lowercase__       (							self    :						Optional[int]					)				->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.dummy_cond_unet_upscale
             UpperCAmelCase_    				=							DDPMScheduler()
             UpperCAmelCase_    				=							DDIMScheduler(prediction_type="v_prediction"					)
             UpperCAmelCase_    				=							self.dummy_vae
             UpperCAmelCase_    				=							self.dummy_text_encoder
             UpperCAmelCase_    				=							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

             UpperCAmelCase_    				=							self.dummy_image.cpu().permute(0	,       2	,       3	,       1					)[0]
             UpperCAmelCase_    				=							Image.fromarray(np.uinta(_UpperCAmelCase					)					).convert("RGB"					).resize((64, 64)					)

             # put models in fp16, except vae as it overflows in fp16
             UpperCAmelCase_    				=							unet.half()
             UpperCAmelCase_    				=							text_encoder.half()

             # make sure here that pndm scheduler skips prk
             UpperCAmelCase_    				=							StableDiffusionUpscalePipeline(
                 unet=_UpperCAmelCase	,       low_res_scheduler=_UpperCAmelCase	,       scheduler=_UpperCAmelCase	,       vae=_UpperCAmelCase	,       text_encoder=_UpperCAmelCase	,       tokenizer=_UpperCAmelCase	,       max_noise_level=350	,       )
             UpperCAmelCase_    				=							sd_pipe.to(_UpperCAmelCase					)
             sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

             UpperCAmelCase_    				=							"A painting of a squirrel eating a burger"
             UpperCAmelCase_    				=							torch.manual_seed(0					)
             UpperCAmelCase_    				=							sd_pipe(
                 [prompt]	,       image=_UpperCAmelCase	,       generator=_UpperCAmelCase	,       num_inference_steps=2	,       output_type="np"	,       ).images

             UpperCAmelCase_    				=							low_res_image.size[0] * 4
             assert image.shape == (1, expected_height_width, expected_height_width, 3)



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







       '''simple docstring'''


       def   lowercase__       (							self    :						Dict					)				->		Optional[Any]:


             '''simple docstring'''




             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()



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


             '''simple docstring'''




             UpperCAmelCase_    				=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/sd2-upscale/low_res_cat.png"					)
             UpperCAmelCase_    				=							load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
                 "/upsampled_cat.npy"					)

             UpperCAmelCase_    				=							"stabilityai/stable-diffusion-x4-upscaler"
             UpperCAmelCase_    				=							StableDiffusionUpscalePipeline.from_pretrained(_UpperCAmelCase					)
             pipe.to(_UpperCAmelCase					)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase					)
             pipe.enable_attention_slicing()

             UpperCAmelCase_    				=							"a cat sitting on a park bench"

             UpperCAmelCase_    				=							torch.manual_seed(0					)
             UpperCAmelCase_    				=							pipe(
                 prompt=_UpperCAmelCase	,       image=_UpperCAmelCase	,       generator=_UpperCAmelCase	,       output_type="np"	,       )
             UpperCAmelCase_    				=							output.images[0]

             assert image.shape == (512, 512, 3)
             assert np.abs(expected_image - image					).max() < 1e-3



       def   lowercase__       (							self    :						Tuple					)				->		Any:


             '''simple docstring'''




             UpperCAmelCase_    				=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/sd2-upscale/low_res_cat.png"					)
             UpperCAmelCase_    				=							load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
                 "/upsampled_cat_fp16.npy"					)

             UpperCAmelCase_    				=							"stabilityai/stable-diffusion-x4-upscaler"
             UpperCAmelCase_    				=							StableDiffusionUpscalePipeline.from_pretrained(
                 _UpperCAmelCase	,       torch_dtype=torch.floataa	,       )
             pipe.to(_UpperCAmelCase					)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase					)
             pipe.enable_attention_slicing()

             UpperCAmelCase_    				=							"a cat sitting on a park bench"

             UpperCAmelCase_    				=							torch.manual_seed(0					)
             UpperCAmelCase_    				=							pipe(
                 prompt=_UpperCAmelCase	,       image=_UpperCAmelCase	,       generator=_UpperCAmelCase	,       output_type="np"	,       )
             UpperCAmelCase_    				=							output.images[0]

             assert image.shape == (512, 512, 3)
             assert np.abs(expected_image - image					).max() < 5e-1







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


             '''simple docstring'''




             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

             UpperCAmelCase_    				=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/sd2-upscale/low_res_cat.png"					)

             UpperCAmelCase_    				=							"stabilityai/stable-diffusion-x4-upscaler"
             UpperCAmelCase_    				=							StableDiffusionUpscalePipeline.from_pretrained(
                 _UpperCAmelCase	,       torch_dtype=torch.floataa	,       )
             pipe.to(_UpperCAmelCase					)
             pipe.set_progress_bar_config(disable=_UpperCAmelCase					)
             pipe.enable_attention_slicing(1					)
             pipe.enable_sequential_cpu_offload()

             UpperCAmelCase_    				=							"a cat sitting on a park bench"

             UpperCAmelCase_    				=							torch.manual_seed(0					)
             UpperCAmelCase_    				=							pipe(
                 prompt=_UpperCAmelCase	,       image=_UpperCAmelCase	,       generator=_UpperCAmelCase	,       num_inference_steps=5	,       output_type="np"	,       )

             UpperCAmelCase_    				=							torch.cuda.max_memory_allocated()
             # make sure that less than 2.9 GB is allocated
             assert mem_bytes < 2.9 * 10**9









"""simple docstring"""





import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase      =		get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
class    lowercase__     (					SCREAMING_SNAKE_CASE       ,      unittest.TestCase   ):







       '''simple docstring'''


       UpperCamelCase		=  XLMProphetNetTokenizer
       UpperCamelCase		=  False
       UpperCamelCase		=  True



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


             '''simple docstring'''




             super().setUp()

             # We have a SentencePiece fixture for testing
             UpperCAmelCase_    				=							XLMProphetNetTokenizer(_UpperCAmelCase	,       keep_accents=_UpperCAmelCase					)
             tokenizer.save_pretrained(self.tmpdirname					)



       def   lowercase__       (							self    :						Tuple					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							"[PAD]"
             UpperCAmelCase_    				=							0

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



       def   lowercase__       (							self    :						Dict					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							list(self.get_tokenizer().get_vocab().keys()					)

             self.assertEqual(vocab_keys[0]	,       "[PAD]"					)
             self.assertEqual(vocab_keys[1]	,       "[CLS]"					)
             self.assertEqual(vocab_keys[-1]	,       "j"					)
             self.assertEqual(len(_UpperCAmelCase					)	,       1012					)



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


             '''simple docstring'''




             self.assertEqual(self.get_tokenizer().vocab_size	,       1012					)



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


             '''simple docstring'''




             UpperCAmelCase_    				=							XLMProphetNetTokenizer(_UpperCAmelCase	,       keep_accents=_UpperCAmelCase					)

             UpperCAmelCase_    				=							tokenizer.tokenize("This is a test"					)
             self.assertListEqual(_UpperCAmelCase	,       ["▁This", "▁is", "▁a", "▁t", "est"]					)

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(_UpperCAmelCase					)	,       [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]	,       )

             UpperCAmelCase_    				=							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",
                     ".",
                 ]	,       )
             UpperCAmelCase_    				=							tokenizer.convert_tokens_to_ids(_UpperCAmelCase					)
             self.assertListEqual(
                 _UpperCAmelCase	,       [
                     value + tokenizer.fairseq_offset
                     for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
                 ]	,       )

             UpperCAmelCase_    				=							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   lowercase__       (							self    :						Union[str, Any]					)				->		Optional[Any]:


             '''simple docstring'''




             return XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"					)



       @slow
       def   lowercase__       (							self    :						List[Any]					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							"Hello World!"
             UpperCAmelCase_    				=							[35389, 6672, 49, 2]
             self.assertListEqual(_UpperCAmelCase	,       self.big_tokenizer.encode(_UpperCAmelCase					)					)







       @slow
       def   lowercase__       (							self    :						Optional[int]					)				->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_    				=							{"input_ids": [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 0, 0, 0, 0, 0], [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, 0, 0, 0, 0, 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=_UpperCAmelCase	,       model_name="microsoft/xprophetnet-large-wiki100-cased"	,       revision="1acad1643ddd54a44df6a1b797ada8373685d90e"	,       )




