


import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class       __magic_name__	(     __lowerCAmelCase					,				unittest.TestCase):
     A:		Optional[int]			=      UnCLIPImageVariationPipeline
     A:		Optional[Any]			=      IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
     A:		List[str]			=      IMAGE_VARIATION_BATCH_PARAMS

     A:		Optional[int]			=      [
         "generator",
         "return_dict",
         "decoder_num_inference_steps",
         "super_res_num_inference_steps",
     ]
     A:		List[Any]			=      False


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





      '''simple docstring'''






      return 32


     @property
     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      Dict:





      '''simple docstring'''






      return 32


     @property
     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      Optional[int]:





      '''simple docstring'''






      return self.time_input_dim


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





      '''simple docstring'''






      return self.time_input_dim * 4


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





      '''simple docstring'''






      return 100


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





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)
      return tokenizer


     @property
     def      UpperCAmelCase__	(      self							:     str			)  ->      str:





      '''simple docstring'''






      torch.manual_seed(0			)
      UpperCamelCase__					:    Tuple          =   CLIPTextConfig(
          bos_token_id=0							,	eos_token_id=2							,	hidden_size=self.text_embedder_hidden_size							,	projection_dim=self.text_embedder_hidden_size							,	intermediate_size=37							,	layer_norm_eps=1E-0_5							,	num_attention_heads=4							,	num_hidden_layers=5							,	pad_token_id=1							,	vocab_size=1000							,	)
      return CLIPTextModelWithProjection(__a			)


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





      '''simple docstring'''






      torch.manual_seed(0			)
      UpperCamelCase__					:    int          =   CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size							,	projection_dim=self.text_embedder_hidden_size							,	num_hidden_layers=5							,	num_attention_heads=4							,	image_size=32							,	intermediate_size=37							,	patch_size=1							,	)
      return CLIPVisionModelWithProjection(__a			)


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





      '''simple docstring'''






      torch.manual_seed(0			)

      UpperCamelCase__					:    int          =   {
          '''clip_embeddings_dim''': self.text_embedder_hidden_size,
          '''time_embed_dim''': self.time_embed_dim,
          '''cross_attention_dim''': self.cross_attention_dim,
      }

      UpperCamelCase__					:    Union[str, Any]          =   UnCLIPTextProjModel(**__a			)
      return model


     @property
     def      UpperCAmelCase__	(      self							:     Any			)  ->      Any:





      '''simple docstring'''






      torch.manual_seed(0			)

      UpperCamelCase__					:    str          =   {
          '''sample_size''': 32,
          # RGB in channels
          '''in_channels''': 3,
          # Out channels is double in channels because predicts mean and variance
          '''out_channels''': 6,
          '''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,
          '''cross_attention_dim''': self.cross_attention_dim,
          '''attention_head_dim''': 4,
          '''resnet_time_scale_shift''': '''scale_shift''',
          '''class_embed_type''': '''identity''',
      }

      UpperCamelCase__					:    Dict          =   UNetaDConditionModel(**__a			)
      return model


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





      '''simple docstring'''






      return {
          "sample_size": 64,
          "layers_per_block": 1,
          "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
          "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
          "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
          "in_channels": 6,
          "out_channels": 3,
      }


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





      '''simple docstring'''






      torch.manual_seed(0			)

      UpperCamelCase__					:    str          =   UNetaDModel(**self.dummy_super_res_kwargs			)
      return model


     @property
     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      int:





      '''simple docstring'''






      torch.manual_seed(1			)

      UpperCamelCase__					:    Union[str, Any]          =   UNetaDModel(**self.dummy_super_res_kwargs			)
      return model


     def      UpperCAmelCase__	(      self							:     int			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   self.dummy_decoder
      UpperCamelCase__					:    Union[str, Any]          =   self.dummy_text_proj
      UpperCamelCase__					:    str          =   self.dummy_text_encoder
      UpperCamelCase__					:    Optional[Any]          =   self.dummy_tokenizer
      UpperCamelCase__					:    int          =   self.dummy_super_res_first
      UpperCamelCase__					:    Union[str, Any]          =   self.dummy_super_res_last

      UpperCamelCase__					:    Optional[int]          =   UnCLIPScheduler(
          variance_type='''learned_range'''							,	prediction_type='''epsilon'''							,	num_train_timesteps=1000							,	)

      UpperCamelCase__					:    List[Any]          =   UnCLIPScheduler(
          variance_type='''fixed_small_log'''							,	prediction_type='''epsilon'''							,	num_train_timesteps=1000							,	)

      UpperCamelCase__					:    str          =   CLIPImageProcessor(crop_size=32							,	size=32			)

      UpperCamelCase__					:    List[str]          =   self.dummy_image_encoder

      return {
          "decoder": decoder,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "text_proj": text_proj,
          "feature_extractor": feature_extractor,
          "image_encoder": image_encoder,
          "super_res_first": super_res_first,
          "super_res_last": super_res_last,
          "decoder_scheduler": decoder_scheduler,
          "super_res_scheduler": super_res_scheduler,
      }


     def      UpperCAmelCase__	(      self							:     int							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     str=0							,	lowerCamelCase__							:     str=True			)  ->      Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   floats_tensor((1, 3, 32, 32)							,	rng=random.Random(__a			)			).to(__a			)
      if str(__a			).startswith('''mps'''			):
       UpperCamelCase__					:    List[str]          =   torch.manual_seed(__a			)
      else:
       UpperCamelCase__					:    int          =   torch.Generator(device=__a			).manual_seed(__a			)

      if pil_image:
       UpperCamelCase__					:    int          =   input_image * 0.5 + 0.5
       UpperCamelCase__					:    Any          =   input_image.clamp(0							,	1			)
       UpperCamelCase__					:    Optional[Any]          =   input_image.cpu().permute(0							,	2							,	3							,	1			).float().numpy()
       UpperCamelCase__					:    Tuple          =   DiffusionPipeline.numpy_to_pil(__a			)[0]

      return {
          "image": input_image,
          "generator": generator,
          "decoder_num_inference_steps": 2,
          "super_res_num_inference_steps": 2,
          "output_type": "np",
      }


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





      '''simple docstring'''






      UpperCamelCase__					:    int          =   '''cpu'''

      UpperCamelCase__					:    Optional[Any]          =   self.get_dummy_components()

      UpperCamelCase__					:    str          =   self.pipeline_class(**__a			)
      UpperCamelCase__					:    Any          =   pipe.to(__a			)

      pipe.set_progress_bar_config(disable=__a			)

      UpperCamelCase__					:    List[Any]          =   self.get_dummy_inputs(__a							,	pil_image=__a			)

      UpperCamelCase__					:    Tuple          =   pipe(**__a			)
      UpperCamelCase__					:    Dict          =   output.images

      UpperCamelCase__					:    Any          =   self.get_dummy_inputs(__a							,	pil_image=__a			)

      UpperCamelCase__					:    Optional[int]          =   pipe(
          **__a							,	return_dict=__a							,	)[0]

      UpperCamelCase__					:    Tuple          =   image[0, -3:, -3:, -1]
      UpperCamelCase__					:    Union[str, Any]          =   image_from_tuple[0, -3:, -3:, -1]

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

      UpperCamelCase__					:    Tuple          =   np.array(
          [
              0.9997,
              0.0002,
              0.9997,
              0.9997,
              0.9969,
              0.0023,
              0.9997,
              0.9969,
              0.9970,
          ]			)

      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      UpperCAmelCase__	(      self							:     int			)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   '''cpu'''

      UpperCamelCase__					:    int          =   self.get_dummy_components()

      UpperCamelCase__					:    Tuple          =   self.pipeline_class(**__a			)
      UpperCamelCase__					:    str          =   pipe.to(__a			)

      pipe.set_progress_bar_config(disable=__a			)

      UpperCamelCase__					:    str          =   self.get_dummy_inputs(__a							,	pil_image=__a			)

      UpperCamelCase__					:    List[str]          =   pipe(**__a			)
      UpperCamelCase__					:    List[Any]          =   output.images

      UpperCamelCase__					:    str          =   self.get_dummy_inputs(__a							,	pil_image=__a			)

      UpperCamelCase__					:    str          =   pipe(
          **__a							,	return_dict=__a							,	)[0]

      UpperCamelCase__					:    Tuple          =   image[0, -3:, -3:, -1]
      UpperCamelCase__					:    List[str]          =   image_from_tuple[0, -3:, -3:, -1]

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

      UpperCamelCase__					:    int          =   np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]			)

      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      UpperCAmelCase__	(      self							:     Dict			)  ->      int:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   '''cpu'''

      UpperCamelCase__					:    Dict          =   self.get_dummy_components()

      UpperCamelCase__					:    List[str]          =   self.pipeline_class(**__a			)
      UpperCamelCase__					:    Tuple          =   pipe.to(__a			)

      pipe.set_progress_bar_config(disable=__a			)

      UpperCamelCase__					:    str          =   self.get_dummy_inputs(__a							,	pil_image=__a			)
      UpperCamelCase__					:    Any          =   [
          pipeline_inputs['''image'''],
          pipeline_inputs['''image'''],
      ]

      UpperCamelCase__					:    List[Any]          =   pipe(**__a			)
      UpperCamelCase__					:    Any          =   output.images

      UpperCamelCase__					:    Dict          =   self.get_dummy_inputs(__a							,	pil_image=__a			)
      UpperCamelCase__					:    int          =   [
          tuple_pipeline_inputs['''image'''],
          tuple_pipeline_inputs['''image'''],
      ]

      UpperCamelCase__					:    Any          =   pipe(
          **__a							,	return_dict=__a							,	)[0]

      UpperCamelCase__					:    Optional[int]          =   image[0, -3:, -3:, -1]
      UpperCamelCase__					:    Tuple          =   image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (2, 64, 64, 3)

      UpperCamelCase__					:    Dict          =   np.array(
          [
              0.9997,
              0.9989,
              0.0008,
              0.0021,
              0.9960,
              0.0018,
              0.0014,
              0.0002,
              0.9933,
          ]			)

      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      UpperCAmelCase__	(      self							:     Dict			)  ->      List[str]:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   torch.device('''cpu'''			)

      class       __magic_name__	:
           A:		Optional[Any]			=      1



      UpperCamelCase__					:    str          =   self.get_dummy_components()

      UpperCamelCase__					:    Dict          =   self.pipeline_class(**__a			)
      UpperCamelCase__					:    int          =   pipe.to(__a			)

      pipe.set_progress_bar_config(disable=__a			)

      UpperCamelCase__					:    Any          =   torch.Generator(device=__a			).manual_seed(0			)
      UpperCamelCase__					:    Union[str, Any]          =   pipe.decoder.dtype
      UpperCamelCase__					:    int          =   1

      UpperCamelCase__					:    int          =   (
          batch_size,
          pipe.decoder.config.in_channels,
          pipe.decoder.config.sample_size,
          pipe.decoder.config.sample_size,
      )
      UpperCamelCase__					:    Optional[Any]          =   pipe.prepare_latents(
          __a							,	dtype=__a							,	device=__a							,	generator=__a							,	latents=__a							,	scheduler=DummyScheduler()			)

      UpperCamelCase__					:    Optional[int]          =   (
          batch_size,
          pipe.super_res_first.config.in_channels // 2,
          pipe.super_res_first.config.sample_size,
          pipe.super_res_first.config.sample_size,
      )
      UpperCamelCase__					:    List[str]          =   pipe.prepare_latents(
          __a							,	dtype=__a							,	device=__a							,	generator=__a							,	latents=__a							,	scheduler=DummyScheduler()			)

      UpperCamelCase__					:    Union[str, Any]          =   self.get_dummy_inputs(__a							,	pil_image=__a			)

      UpperCamelCase__					:    Tuple          =   pipe(
          **__a							,	decoder_latents=__a							,	super_res_latents=__a			).images

      UpperCamelCase__					:    str          =   self.get_dummy_inputs(__a							,	pil_image=__a			)
      # Don't pass image, instead pass embedding
      UpperCamelCase__					:    Union[str, Any]          =   pipeline_inputs.pop('''image'''			)
      UpperCamelCase__					:    Optional[int]          =   pipe.image_encoder(__a			).image_embeds

      UpperCamelCase__					:    List[str]          =   pipe(
          **__a							,	decoder_latents=__a							,	super_res_latents=__a							,	image_embeddings=__a							,	).images

      # make sure passing text embeddings manually is identical
      assert np.abs(img_out_a - img_out_a			).max() < 1E-4


     @skip_mps
     def      UpperCAmelCase__	(      self							:     Any			)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   torch_device == '''cpu'''

      # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
      UpperCamelCase__					:    Union[str, Any]          =   1E-2

      self._test_attention_slicing_forward_pass(
          test_max_difference=__a							,	expected_max_diff=__a			)


     @skip_mps
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   torch_device == '''cpu'''
      UpperCamelCase__					:    Any          =   True
      UpperCamelCase__					:    Any          =   [
          '''decoder_num_inference_steps''',
          '''super_res_num_inference_steps''',
      ]

      self._test_inference_batch_single_identical(
          test_max_difference=__a							,	relax_max_difference=__a							,	additional_params_copy_to_batched_inputs=__a							,	)


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





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   [
          '''decoder_num_inference_steps''',
          '''super_res_num_inference_steps''',
      ]

      if torch_device == "mps":
       # TODO: MPS errors with larger batch sizes
       UpperCamelCase__					:    Union[str, Any]          =   [2, 3]
       self._test_inference_batch_consistent(
           batch_sizes=__a							,	additional_params_copy_to_batched_inputs=__a							,	)
      else:
       self._test_inference_batch_consistent(
           additional_params_copy_to_batched_inputs=__a			)


     @skip_mps
     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      Optional[int]:





      '''simple docstring'''






      return super().test_dict_tuple_outputs_equivalent()


     @skip_mps
     def      UpperCAmelCase__	(      self							:     Any			)  ->      Dict:





      '''simple docstring'''






      return super().test_save_load_local()


     @skip_mps
     def      UpperCAmelCase__	(      self							:     int			)  ->      str:





      '''simple docstring'''






      return super().test_save_load_optional_components()



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


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





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'''			)
      UpperCamelCase__					:    Dict          =   load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy'''			)

      UpperCamelCase__					:    List[str]          =   UnCLIPImageVariationPipeline.from_pretrained(
          '''kakaobrain/karlo-v1-alpha-image-variations'''							,	torch_dtype=torch.floataa			)
      UpperCamelCase__					:    List[str]          =   pipeline.to(__a			)
      pipeline.set_progress_bar_config(disable=__a			)

      UpperCamelCase__					:    int          =   torch.Generator(device='''cpu'''			).manual_seed(0			)
      UpperCamelCase__					:    Dict          =   pipeline(
          __a							,	generator=__a							,	output_type='''np'''							,	)

      UpperCamelCase__					:    Any          =   output.images[0]

      assert image.shape == (256, 256, 3)

      assert_mean_pixel_difference(__a							,	__a							,	15			)






import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class       __magic_name__	(     __lowerCAmelCase					,				unittest.TestCase):
     A:		Optional[int]			=      BarthezTokenizer
     A:		Optional[int]			=      BarthezTokenizerFast
     A:		str			=      True
     A:		Optional[int]			=      True


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





      '''simple docstring'''






      super().setUp()

      UpperCamelCase__					:    Optional[Any]          =   BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''			)
      tokenizer.save_pretrained(self.tmpdirname			)
      tokenizer.save_pretrained(self.tmpdirname							,	legacy_format=lowerCamelCase__			)
      UpperCamelCase__					:    int          =   tokenizer


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





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   '''<pad>'''
      UpperCamelCase__					:    List[str]          =   1

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


     def      UpperCAmelCase__	(      self							:     int			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   list(self.get_tokenizer().get_vocab().keys()			)

      self.assertEqual(vocab_keys[0]							,	'''<s>'''			)
      self.assertEqual(vocab_keys[1]							,	'''<pad>'''			)
      self.assertEqual(vocab_keys[-1]							,	'''<mask>'''			)
      self.assertEqual(len(lowerCamelCase__			)							,	101122			)


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





      '''simple docstring'''






      self.assertEqual(self.get_tokenizer().vocab_size							,	101122			)


     @require_torch
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      UpperCamelCase__					:    Any          =   [0, 57, 3018, 70307, 91, 2]

      UpperCamelCase__					:    Optional[int]          =   self.tokenizer(
          lowerCamelCase__							,	max_length=len(lowerCamelCase__			)							,	padding=lowerCamelCase__							,	truncation=lowerCamelCase__							,	return_tensors='''pt'''			)
      self.assertIsInstance(lowerCamelCase__							,	lowerCamelCase__			)

      self.assertEqual((2, 6)							,	batch.input_ids.shape			)
      self.assertEqual((2, 6)							,	batch.attention_mask.shape			)
      UpperCamelCase__					:    List[Any]          =   batch.input_ids.tolist()[0]
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)


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





      '''simple docstring'''






      if not self.test_rust_tokenizer:
       return

      UpperCamelCase__					:    List[Any]          =   self.get_tokenizer()
      UpperCamelCase__					:    Optional[int]          =   self.get_rust_tokenizer()

      UpperCamelCase__					:    Any          =   '''I was born in 92000, and this is falsé.'''

      UpperCamelCase__					:    List[str]          =   tokenizer.tokenize(lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   rust_tokenizer.tokenize(lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   tokenizer.encode(lowerCamelCase__							,	add_special_tokens=lowerCamelCase__			)
      UpperCamelCase__					:    str          =   rust_tokenizer.encode(lowerCamelCase__							,	add_special_tokens=lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   self.get_rust_tokenizer()
      UpperCamelCase__					:    Optional[int]          =   tokenizer.encode(lowerCamelCase__			)
      UpperCamelCase__					:    str          =   rust_tokenizer.encode(lowerCamelCase__			)
      self.assertListEqual(lowerCamelCase__							,	lowerCamelCase__			)


     @slow
     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   {'''input_ids''': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '''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, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
      # fmt: on

      # moussaKam/mbarthez is a french model. So we also use french texts.
      UpperCamelCase__					:    int          =   [
          '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
          '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
          '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
          '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
          '''telles que la traduction et la synthèse de texte.''',
      ]

      self.tokenizer_integration_test_util(
          expected_encoding=lowerCamelCase__							,	model_name='''moussaKam/mbarthez'''							,	revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''							,	sequences=lowerCamelCase__							,	)


