


# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()

class       __magic_name__	(     __lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase					,				unittest.TestCase):
     A:		Union[str, Any]			=      StableDiffusionControlNetImgaImgPipeline
     A:		Optional[int]			=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
     A:		Union[str, Any]			=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     A:		List[Any]			=      IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"})
     A:		List[Any]			=      IMAGE_TO_IMAGE_IMAGE_PARAMS


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





      '''simple docstring'''






      torch.manual_seed(0			)
      UpperCamelCase__					:    Dict          =   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							,	)
      torch.manual_seed(0			)
      UpperCamelCase__					:    Optional[int]          =   ControlNetModel(
          block_out_channels=(32, 64)							,	layers_per_block=2							,	in_channels=4							,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')							,	cross_attention_dim=32							,	conditioning_embedding_out_channels=(16, 32)							,	)
      torch.manual_seed(0			)
      UpperCamelCase__					:    Optional[Any]          =   DDIMScheduler(
          beta_start=0.0_0085							,	beta_end=0.012							,	beta_schedule='''scaled_linear'''							,	clip_sample=lowerCamelCase__							,	set_alpha_to_one=lowerCamelCase__							,	)
      torch.manual_seed(0			)
      UpperCamelCase__					:    List[str]          =   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							,	)
      torch.manual_seed(0			)
      UpperCamelCase__					:    Tuple          =   CLIPTextConfig(
          bos_token_id=0							,	eos_token_id=2							,	hidden_size=32							,	intermediate_size=37							,	layer_norm_eps=1E-0_5							,	num_attention_heads=4							,	num_hidden_layers=5							,	pad_token_id=1							,	vocab_size=1000							,	)
      UpperCamelCase__					:    Any          =   CLIPTextModel(lowerCamelCase__			)
      UpperCamelCase__					:    int          =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

      UpperCamelCase__					:    List[str]          =   {
          '''unet''': unet,
          '''controlnet''': controlnet,
          '''scheduler''': scheduler,
          '''vae''': vae,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''safety_checker''': None,
          '''feature_extractor''': None,
      }
      return components


     def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     Union[str, Any]							,	lowerCamelCase__							:     List[Any]=0			)  ->      List[Any]:





      '''simple docstring'''






      if str(lowerCamelCase__			).startswith('''mps'''			):
       UpperCamelCase__					:    Tuple          =   torch.manual_seed(lowerCamelCase__			)
      else:
       UpperCamelCase__					:    Dict          =   torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   2
      UpperCamelCase__					:    Dict          =   randn_tensor(
          (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)							,	generator=lowerCamelCase__							,	device=torch.device(lowerCamelCase__			)							,	)
      UpperCamelCase__					:    List[Any]          =   floats_tensor(control_image.shape							,	rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   image.cpu().permute(0							,	2							,	3							,	1			)[0]
      UpperCamelCase__					:    List[Any]          =   Image.fromarray(np.uinta(lowerCamelCase__			)			).convert('''RGB'''			).resize((64, 64)			)
      UpperCamelCase__					:    List[Any]          =   {
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''guidance_scale''': 6.0,
          '''output_type''': '''numpy''',
          '''image''': image,
          '''control_image''': control_image,
      }

      return inputs


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





      '''simple docstring'''






      return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3			)


     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available()							,	reason='''XFormers attention is only available with CUDA and `xformers` installed'''							,	)
     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Union[str, Any]:





      '''simple docstring'''






      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)


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





      '''simple docstring'''






      self._test_inference_batch_single_identical(expected_max_diff=2E-3			)



class       __magic_name__	(     __lowerCAmelCase					,				__lowerCAmelCase					,				unittest.TestCase):
     A:		Dict			=      StableDiffusionControlNetImgaImgPipeline
     A:		Optional[Any]			=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
     A:		int			=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     A:		Union[str, Any]			=      frozenset([])  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess


     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      Dict:





      '''simple docstring'''






      torch.manual_seed(0			)
      UpperCamelCase__					:    Any          =   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							,	)
      torch.manual_seed(0			)

      def init_weights(lowerCamelCase__							:     int			):
       if isinstance(lowerCamelCase__							,	torch.nn.Convad			):
        torch.nn.init.normal(m.weight			)
        m.bias.data.fill_(1.0			)

      UpperCamelCase__					:    Union[str, Any]          =   ControlNetModel(
          block_out_channels=(32, 64)							,	layers_per_block=2							,	in_channels=4							,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')							,	cross_attention_dim=32							,	conditioning_embedding_out_channels=(16, 32)							,	)
      controlneta.controlnet_down_blocks.apply(lowerCamelCase__			)

      torch.manual_seed(0			)
      UpperCamelCase__					:    Union[str, Any]          =   ControlNetModel(
          block_out_channels=(32, 64)							,	layers_per_block=2							,	in_channels=4							,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')							,	cross_attention_dim=32							,	conditioning_embedding_out_channels=(16, 32)							,	)
      controlneta.controlnet_down_blocks.apply(lowerCamelCase__			)

      torch.manual_seed(0			)
      UpperCamelCase__					:    int          =   DDIMScheduler(
          beta_start=0.0_0085							,	beta_end=0.012							,	beta_schedule='''scaled_linear'''							,	clip_sample=lowerCamelCase__							,	set_alpha_to_one=lowerCamelCase__							,	)
      torch.manual_seed(0			)
      UpperCamelCase__					:    List[Any]          =   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							,	)
      torch.manual_seed(0			)
      UpperCamelCase__					:    Union[str, Any]          =   CLIPTextConfig(
          bos_token_id=0							,	eos_token_id=2							,	hidden_size=32							,	intermediate_size=37							,	layer_norm_eps=1E-0_5							,	num_attention_heads=4							,	num_hidden_layers=5							,	pad_token_id=1							,	vocab_size=1000							,	)
      UpperCamelCase__					:    Union[str, Any]          =   CLIPTextModel(lowerCamelCase__			)
      UpperCamelCase__					:    Optional[int]          =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

      UpperCamelCase__					:    List[str]          =   MultiControlNetModel([controlneta, controlneta]			)

      UpperCamelCase__					:    List[str]          =   {
          '''unet''': unet,
          '''controlnet''': controlnet,
          '''scheduler''': scheduler,
          '''vae''': vae,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''safety_checker''': None,
          '''feature_extractor''': None,
      }
      return components


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     Optional[int]							,	lowerCamelCase__							:     Tuple=0			)  ->      Dict:





      '''simple docstring'''






      if str(lowerCamelCase__			).startswith('''mps'''			):
       UpperCamelCase__					:    Tuple          =   torch.manual_seed(lowerCamelCase__			)
      else:
       UpperCamelCase__					:    Union[str, Any]          =   torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)

      UpperCamelCase__					:    str          =   2

      UpperCamelCase__					:    Tuple          =   [
          randn_tensor(
              (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)							,	generator=lowerCamelCase__							,	device=torch.device(lowerCamelCase__			)							,	),
          randn_tensor(
              (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)							,	generator=lowerCamelCase__							,	device=torch.device(lowerCamelCase__			)							,	),
      ]

      UpperCamelCase__					:    List[str]          =   floats_tensor(control_image[0].shape							,	rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   image.cpu().permute(0							,	2							,	3							,	1			)[0]
      UpperCamelCase__					:    Optional[int]          =   Image.fromarray(np.uinta(lowerCamelCase__			)			).convert('''RGB'''			).resize((64, 64)			)
      UpperCamelCase__					:    List[Any]          =   {
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''guidance_scale''': 6.0,
          '''output_type''': '''numpy''',
          '''image''': image,
          '''control_image''': control_image,
      }

      return inputs


     def      UpperCAmelCase__	(      self							:     int			)  ->      int:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   self.get_dummy_components()
      UpperCamelCase__					:    List[Any]          =   self.pipeline_class(**lowerCamelCase__			)
      pipe.to(lowerCamelCase__			)

      UpperCamelCase__					:    Union[str, Any]          =   10.0
      UpperCamelCase__					:    Optional[int]          =   4

      UpperCamelCase__					:    Any          =   self.get_dummy_inputs(lowerCamelCase__			)
      UpperCamelCase__					:    Dict          =   steps
      UpperCamelCase__					:    Optional[Any]          =   scale
      UpperCamelCase__					:    Union[str, Any]          =   pipe(**lowerCamelCase__			)[0]

      UpperCamelCase__					:    List[Any]          =   self.get_dummy_inputs(lowerCamelCase__			)
      UpperCamelCase__					:    List[str]          =   steps
      UpperCamelCase__					:    int          =   scale
      UpperCamelCase__					:    List[Any]          =   pipe(**lowerCamelCase__							,	control_guidance_start=0.1							,	control_guidance_end=0.2			)[0]

      UpperCamelCase__					:    Dict          =   self.get_dummy_inputs(lowerCamelCase__			)
      UpperCamelCase__					:    Dict          =   steps
      UpperCamelCase__					:    Optional[Any]          =   scale
      UpperCamelCase__					:    List[Any]          =   pipe(**lowerCamelCase__							,	control_guidance_start=[0.1, 0.3]							,	control_guidance_end=[0.2, 0.7]			)[0]

      UpperCamelCase__					:    Tuple          =   self.get_dummy_inputs(lowerCamelCase__			)
      UpperCamelCase__					:    Optional[int]          =   steps
      UpperCamelCase__					:    Optional[Any]          =   scale
      UpperCamelCase__					:    Optional[int]          =   pipe(**lowerCamelCase__							,	control_guidance_start=0.4							,	control_guidance_end=[0.5, 0.8]			)[0]

      # make sure that all outputs are different
      assert np.sum(np.abs(output_a - output_a			)			) > 1E-3
      assert np.sum(np.abs(output_a - output_a			)			) > 1E-3
      assert np.sum(np.abs(output_a - output_a			)			) > 1E-3


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





      '''simple docstring'''






      return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3			)


     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available()							,	reason='''XFormers attention is only available with CUDA and `xformers` installed'''							,	)
     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      List[Any]:





      '''simple docstring'''






      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)


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





      '''simple docstring'''






      self._test_inference_batch_single_identical(expected_max_diff=2E-3			)


     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   self.get_dummy_components()
      UpperCamelCase__					:    int          =   self.pipeline_class(**lowerCamelCase__			)
      pipe.to(lowerCamelCase__			)
      pipe.set_progress_bar_config(disable=lowerCamelCase__			)
      with tempfile.TemporaryDirectory() as tmpdir:
       try:
        # save_pretrained is not implemented for Multi-ControlNet
        pipe.save_pretrained(lowerCamelCase__			)
       except NotImplementedError:
        pass



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


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





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''			)

      UpperCamelCase__					:    int          =   StableDiffusionControlNetImgaImgPipeline.from_pretrained(
          '''runwayml/stable-diffusion-v1-5'''							,	safety_checker=lowerCamelCase__							,	controlnet=lowerCamelCase__			)
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=lowerCamelCase__			)

      UpperCamelCase__					:    Optional[int]          =   torch.Generator(device='''cpu'''			).manual_seed(0			)
      UpperCamelCase__					:    int          =   '''evil space-punk bird'''
      UpperCamelCase__					:    Optional[int]          =   load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''			).resize((512, 512)			)
      UpperCamelCase__					:    List[Any]          =   load_image(
          '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''			).resize((512, 512)			)

      UpperCamelCase__					:    Tuple          =   pipe(
          lowerCamelCase__							,	lowerCamelCase__							,	control_image=lowerCamelCase__							,	generator=lowerCamelCase__							,	output_type='''np'''							,	num_inference_steps=50							,	strength=0.6							,	)

      UpperCamelCase__					:    Dict          =   output.images[0]

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

      UpperCamelCase__					:    Optional[Any]          =   load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy'''			)

      assert np.abs(expected_image - image			).max() < 9E-2






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__							,	)


