




'''simple docstring'''







import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


class       _a  (     unittest.TestCase			):
       def    A				(     self						:     int    ):



           '''simple docstring'''

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
       @property
       def    A				(     self						:     List[str]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		1
           UpperCAmelCase      	=		3
           UpperCAmelCase      	=		(32, 32)

           UpperCAmelCase      	=		floats_tensor((batch_size, num_channels) + sizes       ,    rng=random.Random(0    )    ).to(lowercase    )
           return image
       @property
       def    A				(     self						:     Optional[int]    ):



           '''simple docstring'''

           torch.manual_seed(0    )
           UpperCAmelCase      	=		UNetaDConditionModel(
               block_out_channels=(32, 64)       ,    layers_per_block=2       ,    sample_size=32       ,    in_channels=4       ,    out_channels=4       ,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,    cross_attention_dim=32       ,    )
           return model
       @property
       def    A				(     self						:     Tuple    ):



           '''simple docstring'''

           torch.manual_seed(0    )
           UpperCAmelCase      	=		AutoencoderKL(
               block_out_channels=[32, 64]       ,    in_channels=3       ,    out_channels=3       ,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,    latent_channels=4       ,    )
           return model
       @property
       def    A				(     self						:     Dict    ):



           '''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=1_000       ,    )
           return CLIPTextModel(lowercase    )
       @property
       def    A				(     self						:     Any    ):



           '''simple docstring'''

           def extract(*lowercase						:     Optional[int]       ,    **lowercase						:     Optional[int]    ):


               class       _a  :
                      def __init__(     self						:     Dict    ):



                          '''simple docstring'''

                          UpperCAmelCase      	=		torch.ones([0]    )







                      def    A				(     self						:     Dict       ,    lowercase						:     Optional[Any]    ):



                          '''simple docstring'''

                          self.pixel_values.to(lowercase    )
                          return self






               return Out()

           return extract
       def    A				(     self						:     Any    ):



           '''simple docstring'''

           UpperCAmelCase      	=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
           UpperCAmelCase      	=		self.dummy_cond_unet
           UpperCAmelCase      	=		DDIMScheduler(
               beta_start=0.0_0085       ,    beta_end=0.012       ,    beta_schedule='''scaled_linear'''       ,    clip_sample=lowercase       ,    set_alpha_to_one=lowercase       ,    )

           UpperCAmelCase      	=		self.dummy_vae
           UpperCAmelCase      	=		self.dummy_text_encoder
           UpperCAmelCase      	=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

           # make sure here that pndm scheduler skips prk
           UpperCAmelCase      	=		StableDiffusionPipeline(
               unet=lowercase       ,    scheduler=lowercase       ,    vae=lowercase       ,    text_encoder=lowercase       ,    tokenizer=lowercase       ,    safety_checker=lowercase       ,    feature_extractor=self.dummy_extractor       ,    )
           UpperCAmelCase      	=		sd_pipe.to(lowercase    )
           sd_pipe.set_progress_bar_config(disable=lowercase    )

           UpperCAmelCase      	=		'''A painting of a squirrel eating a burger'''

           UpperCAmelCase      	=		torch.Generator(device=lowercase    ).manual_seed(0    )
           UpperCAmelCase      	=		sd_pipe([prompt]       ,    generator=lowercase       ,    guidance_scale=6.0       ,    num_inference_steps=2       ,    output_type='''np'''    )
           UpperCAmelCase      	=		output.images

           UpperCAmelCase      	=		torch.Generator(device=lowercase    ).manual_seed(0    )
           UpperCAmelCase      	=		sd_pipe(
               [prompt]       ,    generator=lowercase       ,    guidance_scale=6.0       ,    num_inference_steps=2       ,    output_type='''np'''       ,    return_dict=lowercase       ,    )[0]

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

           assert image.shape == (1, 64, 64, 3)
           UpperCAmelCase      	=		np.array([0.5753, 0.6114, 0.5001, 0.5034, 0.5470, 0.4729, 0.4971, 0.4867, 0.4867]    )

           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    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
           UpperCAmelCase      	=		self.dummy_cond_unet
           UpperCAmelCase      	=		PNDMScheduler(skip_prk_steps=lowercase    )
           UpperCAmelCase      	=		self.dummy_vae
           UpperCAmelCase      	=		self.dummy_text_encoder
           UpperCAmelCase      	=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

           # make sure here that pndm scheduler skips prk
           UpperCAmelCase      	=		StableDiffusionPipeline(
               unet=lowercase       ,    scheduler=lowercase       ,    vae=lowercase       ,    text_encoder=lowercase       ,    tokenizer=lowercase       ,    safety_checker=lowercase       ,    feature_extractor=self.dummy_extractor       ,    )
           UpperCAmelCase      	=		sd_pipe.to(lowercase    )
           sd_pipe.set_progress_bar_config(disable=lowercase    )

           UpperCAmelCase      	=		'''A painting of a squirrel eating a burger'''
           UpperCAmelCase      	=		torch.Generator(device=lowercase    ).manual_seed(0    )
           UpperCAmelCase      	=		sd_pipe([prompt]       ,    generator=lowercase       ,    guidance_scale=6.0       ,    num_inference_steps=2       ,    output_type='''np'''    )

           UpperCAmelCase      	=		output.images

           UpperCAmelCase      	=		torch.Generator(device=lowercase    ).manual_seed(0    )
           UpperCAmelCase      	=		sd_pipe(
               [prompt]       ,    generator=lowercase       ,    guidance_scale=6.0       ,    num_inference_steps=2       ,    output_type='''np'''       ,    return_dict=lowercase       ,    )[0]

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

           assert image.shape == (1, 64, 64, 3)
           UpperCAmelCase      	=		np.array([0.5122, 0.5712, 0.4825, 0.5053, 0.5646, 0.4769, 0.5179, 0.4894, 0.4994]    )

           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    A				(     self						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase      	=		StableDiffusionPipeline.from_pretrained(
               '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''       ,    safety_checker=lowercase    )
           assert isinstance(lowercase       ,    lowercase    )
           assert isinstance(pipe.scheduler       ,    lowercase    )
           assert pipe.safety_checker is None

           UpperCAmelCase      	=		pipe('''example prompt'''       ,    num_inference_steps=2    ).images[0]
           assert image is not None

           # check that there's no error when saving a pipeline with one of the models being None
           with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(lowercase    )
               UpperCAmelCase      	=		StableDiffusionPipeline.from_pretrained(lowercase    )

           # sanity check that the pipeline still works
           assert pipe.safety_checker is None
           UpperCAmelCase      	=		pipe('''example prompt'''       ,    num_inference_steps=2    ).images[0]
           assert image is not None







       @unittest.skipIf(torch_device != '''cuda'''       ,    '''This test requires a GPU'''    )
       def    A				(     self						:     Optional[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.dummy_cond_unet
           UpperCAmelCase      	=		PNDMScheduler(skip_prk_steps=lowercase    )
           UpperCAmelCase      	=		self.dummy_vae
           UpperCAmelCase      	=		self.dummy_text_encoder
           UpperCAmelCase      	=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

           # put models in fp16
           UpperCAmelCase      	=		unet.half()
           UpperCAmelCase      	=		vae.half()
           UpperCAmelCase      	=		bert.half()

           # make sure here that pndm scheduler skips prk
           UpperCAmelCase      	=		StableDiffusionPipeline(
               unet=lowercase       ,    scheduler=lowercase       ,    vae=lowercase       ,    text_encoder=lowercase       ,    tokenizer=lowercase       ,    safety_checker=lowercase       ,    feature_extractor=self.dummy_extractor       ,    )
           UpperCAmelCase      	=		sd_pipe.to(lowercase    )
           sd_pipe.set_progress_bar_config(disable=lowercase    )

           UpperCAmelCase      	=		'''A painting of a squirrel eating a burger'''
           UpperCAmelCase      	=		sd_pipe([prompt]       ,    num_inference_steps=2       ,    output_type='''np'''    ).images

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






@nightly
@require_torch_gpu
class       _a  (     unittest.TestCase			):
       def    A				(     self						:     Optional[Any]    ):



           '''simple docstring'''

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
       def    A				(     self						:     List[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		'''cuda'''
           UpperCAmelCase      	=		StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''    )
           UpperCAmelCase      	=		pipe.to(lowercase    )
           pipe.set_progress_bar_config(disable=lowercase    )

           UpperCAmelCase      	=		'''a photo of a cat'''
           UpperCAmelCase      	=		{
               '''editing_prompt''': ['''sunglasses'''],
               '''reverse_editing_direction''': [False],
               '''edit_warmup_steps''': 10,
               '''edit_guidance_scale''': 6,
               '''edit_threshold''': 0.95,
               '''edit_momentum_scale''': 0.5,
               '''edit_mom_beta''': 0.6,
           }

           UpperCAmelCase      	=		3
           UpperCAmelCase      	=		7

           # no sega enabled
           UpperCAmelCase      	=		torch.Generator(lowercase    )
           generator.manual_seed(lowercase    )
           UpperCAmelCase      	=		pipe(
               [prompt]       ,    generator=lowercase       ,    guidance_scale=lowercase       ,    num_inference_steps=50       ,    output_type='''np'''       ,    width=512       ,    height=512       ,    )

           UpperCAmelCase      	=		output.images
           UpperCAmelCase      	=		image[0, -3:, -3:, -1]
           UpperCAmelCase      	=		[
               0.3467_3113,
               0.3849_2733,
               0.3759_7352,
               0.3408_6335,
               0.3565_0748,
               0.3557_9205,
               0.338_4763,
               0.3434_0236,
               0.357_3271,
           ]

           assert image.shape == (1, 512, 512, 3)

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

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(lowercase    )
           UpperCAmelCase      	=		pipe(
               [prompt]       ,    generator=lowercase       ,    guidance_scale=lowercase       ,    num_inference_steps=50       ,    output_type='''np'''       ,    width=512       ,    height=512       ,    **lowercase       ,    )

           UpperCAmelCase      	=		output.images
           UpperCAmelCase      	=		image[0, -3:, -3:, -1]
           UpperCAmelCase      	=		[
               0.4188_7826,
               0.3772_8766,
               0.3013_8272,
               0.4141_6335,
               0.4166_4985,
               0.3628_3392,
               0.3619_1246,
               0.4336_4465,
               0.4300_1732,
           ]

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		'''cuda'''
           UpperCAmelCase      	=		StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''    )
           UpperCAmelCase      	=		pipe.to(lowercase    )
           pipe.set_progress_bar_config(disable=lowercase    )

           UpperCAmelCase      	=		'''an image of a crowded boulevard, realistic, 4k'''
           UpperCAmelCase      	=		{
               '''editing_prompt''': '''crowd, crowded, people''',
               '''reverse_editing_direction''': True,
               '''edit_warmup_steps''': 10,
               '''edit_guidance_scale''': 8.3,
               '''edit_threshold''': 0.9,
               '''edit_momentum_scale''': 0.5,
               '''edit_mom_beta''': 0.6,
           }

           UpperCAmelCase      	=		9
           UpperCAmelCase      	=		7

           # no sega enabled
           UpperCAmelCase      	=		torch.Generator(lowercase    )
           generator.manual_seed(lowercase    )
           UpperCAmelCase      	=		pipe(
               [prompt]       ,    generator=lowercase       ,    guidance_scale=lowercase       ,    num_inference_steps=50       ,    output_type='''np'''       ,    width=512       ,    height=512       ,    )

           UpperCAmelCase      	=		output.images
           UpperCAmelCase      	=		image[0, -3:, -3:, -1]
           UpperCAmelCase      	=		[
               0.4349_7998,
               0.9181_4065,
               0.754_0739,
               0.5558_0205,
               0.846_7265,
               0.538_9691,
               0.6257_4506,
               0.5889_7763,
               0.5092_6757,
           ]

           assert image.shape == (1, 512, 512, 3)

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

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(lowercase    )
           UpperCAmelCase      	=		pipe(
               [prompt]       ,    generator=lowercase       ,    guidance_scale=lowercase       ,    num_inference_steps=50       ,    output_type='''np'''       ,    width=512       ,    height=512       ,    **lowercase       ,    )

           UpperCAmelCase      	=		output.images
           UpperCAmelCase      	=		image[0, -3:, -3:, -1]
           UpperCAmelCase      	=		[
               0.308_9719,
               0.3050_0144,
               0.2901_6042,
               0.3063_0964,
               0.32_5687,
               0.2941_9225,
               0.290_8091,
               0.2872_3598,
               0.2769_6294,
           ]

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
       def    A				(     self						:     List[str]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		'''cuda'''
           UpperCAmelCase      	=		StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''    )
           UpperCAmelCase      	=		pipe.to(lowercase    )
           pipe.set_progress_bar_config(disable=lowercase    )

           UpperCAmelCase      	=		'''a castle next to a river'''
           UpperCAmelCase      	=		{
               '''editing_prompt''': ['''boat on a river, boat''', '''monet, impression, sunrise'''],
               '''reverse_editing_direction''': False,
               '''edit_warmup_steps''': [15, 18],
               '''edit_guidance_scale''': 6,
               '''edit_threshold''': [0.9, 0.8],
               '''edit_momentum_scale''': 0.5,
               '''edit_mom_beta''': 0.6,
           }

           UpperCAmelCase      	=		48
           UpperCAmelCase      	=		7

           # no sega enabled
           UpperCAmelCase      	=		torch.Generator(lowercase    )
           generator.manual_seed(lowercase    )
           UpperCAmelCase      	=		pipe(
               [prompt]       ,    generator=lowercase       ,    guidance_scale=lowercase       ,    num_inference_steps=50       ,    output_type='''np'''       ,    width=512       ,    height=512       ,    )

           UpperCAmelCase      	=		output.images
           UpperCAmelCase      	=		image[0, -3:, -3:, -1]
           UpperCAmelCase      	=		[
               0.7516_3555,
               0.7603_7145,
               0.6_1785,
               0.918_9673,
               0.862_7701,
               0.8518_9694,
               0.851_2813,
               0.8701_2076,
               0.831_2857,
           ]

           assert image.shape == (1, 512, 512, 3)

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

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(lowercase    )
           UpperCAmelCase      	=		pipe(
               [prompt]       ,    generator=lowercase       ,    guidance_scale=lowercase       ,    num_inference_steps=50       ,    output_type='''np'''       ,    width=512       ,    height=512       ,    **lowercase       ,    )

           UpperCAmelCase      	=		output.images
           UpperCAmelCase      	=		image[0, -3:, -3:, -1]
           UpperCAmelCase      	=		[
               0.7355_3365,
               0.753_7271,
               0.7434_1905,
               0.6648_0356,
               0.647_2925,
               0.6303_9416,
               0.6481_2905,
               0.674_9717,
               0.651_7102,
           ]

           assert image.shape == (1, 512, 512, 3)

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







       def    A				(     self						:     Any    ):



           '''simple docstring'''

           UpperCAmelCase      	=		'''cuda'''
           UpperCAmelCase      	=		StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''       ,    torch_dtype=torch.floataa    )
           UpperCAmelCase      	=		pipe.to(lowercase    )
           pipe.set_progress_bar_config(disable=lowercase    )

           UpperCAmelCase      	=		'''a photo of a cat'''
           UpperCAmelCase      	=		{
               '''editing_prompt''': ['''sunglasses'''],
               '''reverse_editing_direction''': [False],
               '''edit_warmup_steps''': 10,
               '''edit_guidance_scale''': 6,
               '''edit_threshold''': 0.95,
               '''edit_momentum_scale''': 0.5,
               '''edit_mom_beta''': 0.6,
           }

           UpperCAmelCase      	=		3
           UpperCAmelCase      	=		7

           # no sega enabled
           UpperCAmelCase      	=		torch.Generator(lowercase    )
           generator.manual_seed(lowercase    )
           UpperCAmelCase      	=		pipe(
               [prompt]       ,    generator=lowercase       ,    guidance_scale=lowercase       ,    num_inference_steps=50       ,    output_type='''np'''       ,    width=512       ,    height=512       ,    )

           UpperCAmelCase      	=		output.images
           UpperCAmelCase      	=		image[0, -3:, -3:, -1]
           UpperCAmelCase      	=		[
               0.3488_7695,
               0.387_6953,
               0.375,
               0.3442_3828,
               0.358_1543,
               0.3571_7773,
               0.338_3789,
               0.3457_0312,
               0.35_9375,
           ]

           assert image.shape == (1, 512, 512, 3)

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

           # with sega enabled
           # generator = torch.manual_seed(seed)
           generator.manual_seed(lowercase    )
           UpperCAmelCase      	=		pipe(
               [prompt]       ,    generator=lowercase       ,    guidance_scale=lowercase       ,    num_inference_steps=50       ,    output_type='''np'''       ,    width=512       ,    height=512       ,    **lowercase       ,    )

           UpperCAmelCase      	=		output.images
           UpperCAmelCase      	=		image[0, -3:, -3:, -1]
           UpperCAmelCase      	=		[
               0.4228_5156,
               0.3691_4062,
               0.2907_7148,
               0.4204_1016,
               0.4191_8945,
               0.3549_8047,
               0.361_8164,
               0.442_3828,
               0.4311_5234,
           ]

           assert image.shape == (1, 512, 512, 3)

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








'''simple docstring'''







import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class       _a  (     __a							,							unittest.TestCase			):
       __a    :					str           = HerbertTokenizer
       __a    :					Optional[Any]           = HerbertTokenizerFast
       __a    :					Any           = True
       def    A				(     self						:     Any    ):



           '''simple docstring'''

           super().setUp()

           # Use a simpler test file without japanese/chinese characters
           with open(f"{get_tests_dir()}/fixtures/sample_text_no_unicode.txt"       ,    encoding='''utf-8'''    ) as f_data:
               UpperCAmelCase      	=		f_data.read().replace('''\n\n'''       ,    '''\n'''    ).strip()

           UpperCAmelCase      	=		[
               '''<s>''',
               '''</s>''',
               '''l''',
               '''o''',
               '''w''',
               '''e''',
               '''r''',
               '''s''',
               '''t''',
               '''i''',
               '''d''',
               '''n''',
               '''w</w>''',
               '''r</w>''',
               '''t</w>''',
               '''lo''',
               '''low''',
               '''er</w>''',
               '''low</w>''',
               '''lowest</w>''',
               '''newer</w>''',
               '''wider</w>''',
               ''',</w>''',
               '''<unk>''',
           ]
           UpperCAmelCase      	=		dict(zip(lowercase       ,    range(len(lowercase    )    )    )    )
           UpperCAmelCase      	=		['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

           UpperCAmelCase      	=		os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['''vocab_file''']    )
           UpperCAmelCase      	=		os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['''merges_file''']    )
           with open(self.vocab_file       ,    '''w'''    ) as fp:
               fp.write(json.dumps(lowercase    )    )
           with open(self.merges_file       ,    '''w'''    ) as fp:
               fp.write('''\n'''.join(lowercase    )    )
       def    A				(     self						:     List[Any]       ,    lowercase						:     Optional[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		'''lower newer'''
           UpperCAmelCase      	=		'''lower newer'''
           return input_text, output_text
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class(vocab_file=self.vocab_file       ,    merges_file=self.merges_file    )

           UpperCAmelCase      	=		'''lower'''
           UpperCAmelCase      	=		['''low''', '''er</w>''']
           UpperCAmelCase      	=		tokenizer.tokenize(lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )

           UpperCAmelCase      	=		tokens + ['''<unk>''']
           UpperCAmelCase      	=		[16, 17, 23]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase    )       ,    lowercase    )
       def    A				(     self						:     str    ):



           '''simple docstring'''

           if not self.test_rust_tokenizer:
               return

           UpperCAmelCase      	=		self.get_tokenizer()
           UpperCAmelCase      	=		self.get_rust_tokenizer()

           UpperCAmelCase      	=		'''lower,newer'''

           UpperCAmelCase      	=		tokenizer.tokenize(lowercase    )
           UpperCAmelCase      	=		rust_tokenizer.tokenize(lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )

           UpperCAmelCase      	=		tokenizer.encode(lowercase       ,    add_special_tokens=lowercase    )
           UpperCAmelCase      	=		rust_tokenizer.encode(lowercase       ,    add_special_tokens=lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )

           UpperCAmelCase      	=		self.get_rust_tokenizer()
           UpperCAmelCase      	=		tokenizer.encode(lowercase    )
           UpperCAmelCase      	=		rust_tokenizer.encode(lowercase    )
           self.assertListEqual(lowercase       ,    lowercase    )







       @slow
       def    A				(     self						:     int    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased'''    )

           UpperCAmelCase      	=		tokenizer.encode('''konstruowanie sekwencji'''       ,    add_special_tokens=lowercase    )
           UpperCAmelCase      	=		tokenizer.encode('''konstruowanie wielu sekwencji'''       ,    add_special_tokens=lowercase    )

           UpperCAmelCase      	=		tokenizer.build_inputs_with_special_tokens(lowercase    )
           UpperCAmelCase      	=		tokenizer.build_inputs_with_special_tokens(lowercase       ,    lowercase    )

           assert encoded_sentence == [0] + text + [2]
           assert encoded_pair == [0] + text + [2] + text_a + [2]




