




'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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     A					(	unittest.TestCase					):







       def      __lowerCAmelCase    (						self :						int      )    ->							int:


              """simple docstring"""


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







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


              """simple docstring"""


              _a			  =       1
              _a			  =       3
              _a			  =       (32, 32)

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







       @property
       def      __lowerCAmelCase    (						self :						Dict      )    ->							List[str]:


              """simple docstring"""


              torch.manual_seed(0      )
              _a			  =       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      __lowerCAmelCase    (						self :						str      )    ->							List[Any]:


              """simple docstring"""


              torch.manual_seed(0      )
              _a			  =       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      __lowerCAmelCase    (						self :						str      )    ->							Optional[int]:


              """simple docstring"""


              torch.manual_seed(0      )
              _a			  =       RobertaSeriesConfig(
                  hidden_size=32							,    project_dim=32							,    intermediate_size=37							,    layer_norm_eps=1e-05							,    num_attention_heads=4							,    num_hidden_layers=5							,    pad_token_id=1							,    vocab_size=50_06							,    )
              return RobertaSeriesModelWithTransformation(lowerCAmelCase_      )







       @property
       def      __lowerCAmelCase    (						self :						str      )    ->							List[Any]:


              """simple docstring"""


              def extract(*lowerCAmelCase_ :						Dict							,    **lowerCAmelCase_ :						str      ):

                     class     A					:







                            def __init__(						self :						Tuple      )    ->							str:


                                   """simple docstring"""


                                   _a			  =       torch.ones([0]      )







                            def      __lowerCAmelCase    (						self :						int							,    lowerCAmelCase_ :						Optional[int]      )    ->							int:


                                   """simple docstring"""


                                   self.pixel_values.to(lowerCAmelCase_      )
                                   return self



                     return Out()

              return extract







       def      __lowerCAmelCase    (						self :						Dict      )    ->							int:


              """simple docstring"""


              _a			  =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
              _a			  =       self.dummy_cond_unet
              _a			  =       PNDMScheduler(skip_prk_steps=lowerCAmelCase_      )
              _a			  =       self.dummy_vae
              _a			  =       self.dummy_text_encoder
              _a			  =       XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''      )
              _a			  =       77

              _a			  =       self.dummy_image.to(lowerCAmelCase_      )
              _a			  =       init_image / 2 + 0.5

              # make sure here that pndm scheduler skips prk
              _a			  =       AltDiffusionImgaImgPipeline(
                  unet=lowerCAmelCase_							,    scheduler=lowerCAmelCase_							,    vae=lowerCAmelCase_							,    text_encoder=lowerCAmelCase_							,    tokenizer=lowerCAmelCase_							,    safety_checker=lowerCAmelCase_							,    feature_extractor=self.dummy_extractor							,    )
              _a			  =       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor							,    do_normalize=lowerCAmelCase_      )
              _a			  =       alt_pipe.to(lowerCAmelCase_      )
              alt_pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

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

              _a			  =       output.images

              _a			  =       torch.Generator(device=lowerCAmelCase_      ).manual_seed(0      )
              _a			  =       alt_pipe(
                  [prompt]							,    generator=lowerCAmelCase_							,    guidance_scale=6.0							,    num_inference_steps=2							,    output_type='''np'''							,    image=lowerCAmelCase_							,    return_dict=lowerCAmelCase_							,    )[0]

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

              assert image.shape == (1, 32, 32, 3)
              _a			  =       np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]      )

              assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5e-3
              assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 5e-3







       @unittest.skipIf(torch_device != '''cuda'''							,    '''This test requires a GPU'''      )
       def      __lowerCAmelCase    (						self :						Any      )    ->							Tuple:


              """simple docstring"""


              _a			  =       self.dummy_cond_unet
              _a			  =       PNDMScheduler(skip_prk_steps=lowerCAmelCase_      )
              _a			  =       self.dummy_vae
              _a			  =       self.dummy_text_encoder
              _a			  =       XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''      )
              _a			  =       77

              _a			  =       self.dummy_image.to(lowerCAmelCase_      )

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

              # make sure here that pndm scheduler skips prk
              _a			  =       AltDiffusionImgaImgPipeline(
                  unet=lowerCAmelCase_							,    scheduler=lowerCAmelCase_							,    vae=lowerCAmelCase_							,    text_encoder=lowerCAmelCase_							,    tokenizer=lowerCAmelCase_							,    safety_checker=lowerCAmelCase_							,    feature_extractor=self.dummy_extractor							,    )
              _a			  =       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor							,    do_normalize=lowerCAmelCase_      )
              _a			  =       alt_pipe.to(lowerCAmelCase_      )
              alt_pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

              _a			  =       '''A painting of a squirrel eating a burger'''
              _a			  =       torch.manual_seed(0      )
              _a			  =       alt_pipe(
                  [prompt]							,    generator=lowerCAmelCase_							,    num_inference_steps=2							,    output_type='''np'''							,    image=lowerCAmelCase_							,    ).images

              assert image.shape == (1, 32, 32, 3)







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


              """simple docstring"""


              _a			  =       load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/img2img/sketch-mountains-input.jpg'''      )
              # resize to resolution that is divisible by 8 but not 16 or 32
              _a			  =       init_image.resize((7_60, 5_04)      )

              _a			  =       '''BAAI/AltDiffusion'''
              _a			  =       AltDiffusionImgaImgPipeline.from_pretrained(
                  lowerCAmelCase_							,    safety_checker=lowerCAmelCase_							,    )
              pipe.to(lowerCAmelCase_      )
              pipe.set_progress_bar_config(disable=lowerCAmelCase_      )
              pipe.enable_attention_slicing()

              _a			  =       '''A fantasy landscape, trending on artstation'''

              _a			  =       torch.manual_seed(0      )
              _a			  =       pipe(
                  prompt=lowerCAmelCase_							,    image=lowerCAmelCase_							,    strength=0.7_5							,    guidance_scale=7.5							,    generator=lowerCAmelCase_							,    output_type='''np'''							,    )
              _a			  =       output.images[0]

              _a			  =       image[2_55:2_58, 3_83:3_86, -1]

              assert image.shape == (5_04, 7_60, 3)
              _a			  =       np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]      )

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



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







       def      __lowerCAmelCase    (						self :						Any      )    ->							Dict:


              """simple docstring"""


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







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


              """simple docstring"""


              _a			  =       load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  '''/img2img/sketch-mountains-input.jpg'''      )
              _a			  =       init_image.resize((7_68, 5_12)      )
              _a			  =       load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'''      )

              _a			  =       '''BAAI/AltDiffusion'''
              _a			  =       AltDiffusionImgaImgPipeline.from_pretrained(
                  lowerCAmelCase_							,    safety_checker=lowerCAmelCase_							,    )
              pipe.to(lowerCAmelCase_      )
              pipe.set_progress_bar_config(disable=lowerCAmelCase_      )
              pipe.enable_attention_slicing()

              _a			  =       '''A fantasy landscape, trending on artstation'''

              _a			  =       torch.manual_seed(0      )
              _a			  =       pipe(
                  prompt=lowerCAmelCase_							,    image=lowerCAmelCase_							,    strength=0.7_5							,    guidance_scale=7.5							,    generator=lowerCAmelCase_							,    output_type='''np'''							,    )
              _a			  =       output.images[0]

              assert image.shape == (5_12, 7_68, 3)
              # img2img is flaky across GPUs even in fp32, so using MAE here
              assert np.abs(expected_image - image      ).max() < 1e-2







'''simple docstring'''


import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
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   :   Tuple     							=	get_tests_dir('fixtures/test_sentencepiece_no_bos.model')

@require_sentencepiece
@require_tokenizers
class     A					(	_a  ,unittest.TestCase					):
							lowercase_           =       PegasusTokenizer
							lowercase_           =       PegasusTokenizerFast
							lowercase_           =       True
							lowercase_           =       True







							def      __lowerCAmelCase    (						self :						str      )    ->							Any:


														"""simple docstring"""


														super().setUp()

														# We have a SentencePiece fixture for testing
														_a			  =       PegasusTokenizer(lowerCAmelCase_      )
														tokenizer.save_pretrained(self.tmpdirname      )







							@cached_property
							def      __lowerCAmelCase    (						self :						Any      )    ->							Optional[int]:


														"""simple docstring"""


														return PegasusTokenizer.from_pretrained('''google/pegasus-large'''      )







							def      __lowerCAmelCase    (						self :						Optional[int]							,    **lowerCAmelCase_ :						List[str]      )    ->							PegasusTokenizer:


														"""simple docstring"""


														return PegasusTokenizer.from_pretrained(self.tmpdirname							,    **lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Optional[Any]							,    lowerCAmelCase_ :						List[str]      )    ->							Any:


														"""simple docstring"""


														return ("This is a test", "This is a test")







							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							Any:


														"""simple docstring"""


														_a			  =       '''</s>'''
														_a			  =       1

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







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							List[str]:


														"""simple docstring"""


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

														self.assertEqual(vocab_keys[0]							,    '''<pad>'''      )
														self.assertEqual(vocab_keys[1]							,    '''</s>'''      )
														self.assertEqual(vocab_keys[-1]							,    '''v'''      )
														self.assertEqual(len(lowerCAmelCase_      )							,    11_03      )







							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							Any:


														"""simple docstring"""


														self.assertEqual(self.get_tokenizer().vocab_size							,    11_03      )







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							str:


														"""simple docstring"""


														_a			  =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
														_a			  =       self.tokenizer_class.from_pretrained(self.tmpdirname      )
														_a			  =       (
														    '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
														    ''' </s> <pad> <pad> <pad>'''
														)
														_a			  =       rust_tokenizer([raw_input_str]							,    return_tensors=lowerCAmelCase_							,    add_special_tokens=lowerCAmelCase_      ).input_ids[0]
														_a			  =       py_tokenizer([raw_input_str]							,    return_tensors=lowerCAmelCase_							,    add_special_tokens=lowerCAmelCase_      ).input_ids[0]
														self.assertListEqual(lowerCAmelCase_							,    lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							List[str]:


														"""simple docstring"""


														_a			  =       self._large_tokenizer
														# <mask_1> masks whole sentence while <mask_2> masks single word
														_a			  =       '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
														_a			  =       [2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
														_a			  =       tokenizer([raw_input_str]							,    return_tensors=lowerCAmelCase_      ).input_ids[0]
														self.assertListEqual(lowerCAmelCase_							,    lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Dict      )    ->							Dict:


														"""simple docstring"""


														_a			  =       self._large_tokenizer
														# The tracebacks for the following asserts are **better** without messages or self.assertEqual
														assert tokenizer.vocab_size == 9_61_03
														assert tokenizer.pad_token_id == 0
														assert tokenizer.eos_token_id == 1
														assert tokenizer.offset == 1_03
														assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
														assert tokenizer.unk_token == "<unk>"
														assert tokenizer.model_max_length == 10_24
														_a			  =       '''To ensure a smooth flow of bank resolutions.'''
														_a			  =       [4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
														_a			  =       tokenizer([raw_input_str]							,    return_tensors=lowerCAmelCase_      ).input_ids[0]
														self.assertListEqual(lowerCAmelCase_							,    lowerCAmelCase_      )
														assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]      ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]







							@require_torch
							def      __lowerCAmelCase    (						self :						Tuple      )    ->							Union[str, Any]:


														"""simple docstring"""


														_a			  =       ['''This is going to be way too long.''' * 1_50, '''short example''']
														_a			  =       ['''not super long but more than 5 tokens''', '''tiny''']
														_a			  =       self._large_tokenizer(lowerCAmelCase_							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    return_tensors='''pt'''      )
														_a			  =       self._large_tokenizer(
														    text_target=lowerCAmelCase_							,    max_length=5							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    return_tensors='''pt'''      )

														assert batch.input_ids.shape == (2, 10_24)
														assert batch.attention_mask.shape == (2, 10_24)
														assert targets["input_ids"].shape == (2, 5)
														assert len(lowerCAmelCase_      ) == 2  # input_ids, attention_mask.







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


														"""simple docstring"""


														_a			  =       {'''input_ids''': [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 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], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 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]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=lowerCAmelCase_							,    model_name='''google/bigbird-pegasus-large-arxiv'''							,    revision='''ba85d0851d708441f91440d509690f1ab6353415'''							,    )



@require_sentencepiece
@require_tokenizers
class     A					(	_a  ,unittest.TestCase					):
							lowercase_           =       PegasusTokenizer
							lowercase_           =       PegasusTokenizerFast
							lowercase_           =       True
							lowercase_           =       True







							def      __lowerCAmelCase    (						self :						Any      )    ->							Optional[Any]:


														"""simple docstring"""


														super().setUp()

														# We have a SentencePiece fixture for testing
														_a			  =       PegasusTokenizer(lowerCAmelCase_							,    offset=0							,    mask_token_sent=lowerCAmelCase_							,    mask_token='''[MASK]'''      )
														tokenizer.save_pretrained(self.tmpdirname      )







							@cached_property
							def      __lowerCAmelCase    (						self :						Dict      )    ->							int:


														"""simple docstring"""


														return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''      )







							def      __lowerCAmelCase    (						self :						Optional[int]							,    **lowerCAmelCase_ :						Tuple      )    ->							PegasusTokenizer:


														"""simple docstring"""


														return PegasusTokenizer.from_pretrained(self.tmpdirname							,    **lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						Optional[int]      )    ->							Optional[Any]:


														"""simple docstring"""


														return ("This is a test", "This is a test")







							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							str:


														"""simple docstring"""


														_a			  =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
														_a			  =       self.tokenizer_class.from_pretrained(self.tmpdirname      )
														_a			  =       (
														    '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
														    ''' <pad> <pad> <pad>'''
														)
														_a			  =       rust_tokenizer([raw_input_str]							,    return_tensors=lowerCAmelCase_							,    add_special_tokens=lowerCAmelCase_      ).input_ids[0]
														_a			  =       py_tokenizer([raw_input_str]							,    return_tensors=lowerCAmelCase_							,    add_special_tokens=lowerCAmelCase_      ).input_ids[0]
														self.assertListEqual(lowerCAmelCase_							,    lowerCAmelCase_      )







							@require_torch
							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       ['''This is going to be way too long.''' * 10_00, '''short example''']
														_a			  =       ['''not super long but more than 5 tokens''', '''tiny''']
														_a			  =       self._large_tokenizer(lowerCAmelCase_							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    return_tensors='''pt'''      )
														_a			  =       self._large_tokenizer(
														    text_target=lowerCAmelCase_							,    max_length=5							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    return_tensors='''pt'''      )

														assert batch.input_ids.shape == (2, 40_96)
														assert batch.attention_mask.shape == (2, 40_96)
														assert targets["input_ids"].shape == (2, 5)
														assert len(lowerCAmelCase_      ) == 2  # input_ids, attention_mask.







							def      __lowerCAmelCase    (						self :						List[str]      )    ->							Any:


														"""simple docstring"""


														_a			  =       (
														    '''This is an example string that is used to test the original TF implementation against the HF'''
														    ''' implementation'''
														)

														_a			  =       self._large_tokenizer(lowerCAmelCase_      ).input_ids

														self.assertListEqual(
														    lowerCAmelCase_							,    [1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]							,    )

