


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.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu







class     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



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





       @property
       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						1
             lowerCamelCase__	:       Any					=						3
             lowerCamelCase__	:       Tuple					=						(32, 32)

             lowerCamelCase__	:       Union[str, Any]					=						floats_tensor((batch_size, num_channels) + sizes      ,   rng=random.Random(0 ) ).to(__lowerCamelCase )
             return image





       @property
       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             torch.manual_seed(0 )
             lowerCamelCase__	:       List[str]					=						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							:    Optional[Any] ):


             '''simple docstring'''



             torch.manual_seed(0 )
             lowerCamelCase__	:       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      ,   )
             return model





       @property
       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             torch.manual_seed(0 )
             lowerCamelCase__	:       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      ,   )
             return CLIPTextModel(__lowerCamelCase )





       @property
       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             def extract(*__lowerCamelCase							:    Union[str, Any]      ,   **__lowerCamelCase							:    int ):







                   class     _lowercase				:


                          """simple docstring"""




                          def __init__(     self							:    List[Any] ):


                                '''simple docstring'''



                                lowerCamelCase__	:       Optional[int]					=						torch.ones([0] )





                          def 		lowerCAmelCase  (     self							:    List[Any]      ,   __lowerCamelCase							:    Tuple ):


                                '''simple docstring'''



                                self.pixel_values.to(__lowerCamelCase )
                                return self



                   return Out()

             return extract





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						"cpu"  # ensure determinism for the device-dependent torch.Generator
             lowerCamelCase__	:       Union[str, Any]					=						self.dummy_cond_unet
             lowerCamelCase__	:       List[str]					=						DDIMScheduler(
                 beta_start=0.0_0_0_8_5      ,   beta_end=0.0_1_2      ,   beta_schedule="scaled_linear"      ,   clip_sample=__lowerCamelCase      ,   set_alpha_to_one=__lowerCamelCase      ,   )

             lowerCamelCase__	:       Dict					=						self.dummy_vae
             lowerCamelCase__	:       Any					=						self.dummy_text_encoder
             lowerCamelCase__	:       Optional[Any]					=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

             # make sure here that pndm scheduler skips prk
             lowerCamelCase__	:       str					=						StableDiffusionPipeline(
                 unet=__lowerCamelCase      ,   scheduler=__lowerCamelCase      ,   vae=__lowerCamelCase      ,   text_encoder=__lowerCamelCase      ,   tokenizer=__lowerCamelCase      ,   safety_checker=__lowerCamelCase      ,   feature_extractor=self.dummy_extractor      ,   )
             lowerCamelCase__	:       Dict					=						sd_pipe.to(__lowerCamelCase )
             sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       Optional[Any]					=						"A painting of a squirrel eating a burger"

             lowerCamelCase__	:       Tuple					=						torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
             lowerCamelCase__	:       Optional[Any]					=						sd_pipe([prompt]      ,   generator=__lowerCamelCase      ,   guidance_scale=6.0      ,   num_inference_steps=2      ,   output_type="np" )
             lowerCamelCase__	:       List[str]					=						output.images

             lowerCamelCase__	:       List[str]					=						torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
             lowerCamelCase__	:       Optional[int]					=						sd_pipe(
                 [prompt]      ,   generator=__lowerCamelCase      ,   guidance_scale=6.0      ,   num_inference_steps=2      ,   output_type="np"      ,   return_dict=__lowerCamelCase      ,   )[0]

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

             assert image.shape == (1, 64, 64, 3)
             lowerCamelCase__	:       Optional[int]					=						np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4] )

             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 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						"cpu"  # ensure determinism for the device-dependent torch.Generator
             lowerCamelCase__	:       List[Any]					=						self.dummy_cond_unet
             lowerCamelCase__	:       Tuple					=						PNDMScheduler(skip_prk_steps=__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						self.dummy_vae
             lowerCamelCase__	:       Any					=						self.dummy_text_encoder
             lowerCamelCase__	:       Any					=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

             # make sure here that pndm scheduler skips prk
             lowerCamelCase__	:       str					=						StableDiffusionPipeline(
                 unet=__lowerCamelCase      ,   scheduler=__lowerCamelCase      ,   vae=__lowerCamelCase      ,   text_encoder=__lowerCamelCase      ,   tokenizer=__lowerCamelCase      ,   safety_checker=__lowerCamelCase      ,   feature_extractor=self.dummy_extractor      ,   )
             lowerCamelCase__	:       Optional[int]					=						sd_pipe.to(__lowerCamelCase )
             sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       Dict					=						"A painting of a squirrel eating a burger"
             lowerCamelCase__	:       Union[str, Any]					=						torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
             lowerCamelCase__	:       Any					=						sd_pipe([prompt]      ,   generator=__lowerCamelCase      ,   guidance_scale=6.0      ,   num_inference_steps=2      ,   output_type="np" )

             lowerCamelCase__	:       int					=						output.images

             lowerCamelCase__	:       Optional[int]					=						torch.Generator(device=__lowerCamelCase ).manual_seed(0 )
             lowerCamelCase__	:       List[str]					=						sd_pipe(
                 [prompt]      ,   generator=__lowerCamelCase      ,   guidance_scale=6.0      ,   num_inference_steps=2      ,   output_type="np"      ,   return_dict=__lowerCamelCase      ,   )[0]

             lowerCamelCase__	:       int					=						image[0, -3:, -3:, -1]
             lowerCamelCase__	:       Union[str, Any]					=						image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             lowerCamelCase__	:       List[Any]					=						np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3] )

             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 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						StableDiffusionPipeline.from_pretrained(
                 "hf-internal-testing/tiny-stable-diffusion-lms-pipe"      ,   safety_checker=__lowerCamelCase )
             assert isinstance(__lowerCamelCase      ,   __lowerCamelCase )
             assert isinstance(pipe.scheduler      ,   __lowerCamelCase )
             assert pipe.safety_checker is None

             lowerCamelCase__	:       int					=						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(__lowerCamelCase )
                   lowerCamelCase__	:       str					=						StableDiffusionPipeline.from_pretrained(__lowerCamelCase )

             # sanity check that the pipeline still works
             assert pipe.safety_checker is None
             lowerCamelCase__	:       str					=						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 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						self.dummy_cond_unet
             lowerCamelCase__	:       Dict					=						PNDMScheduler(skip_prk_steps=__lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						self.dummy_vae
             lowerCamelCase__	:       int					=						self.dummy_text_encoder
             lowerCamelCase__	:       Dict					=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

             # put models in fp16
             lowerCamelCase__	:       List[str]					=						unet.half()
             lowerCamelCase__	:       Any					=						vae.half()
             lowerCamelCase__	:       Dict					=						bert.half()

             # make sure here that pndm scheduler skips prk
             lowerCamelCase__	:       Union[str, Any]					=						StableDiffusionPipeline(
                 unet=__lowerCamelCase      ,   scheduler=__lowerCamelCase      ,   vae=__lowerCamelCase      ,   text_encoder=__lowerCamelCase      ,   tokenizer=__lowerCamelCase      ,   safety_checker=__lowerCamelCase      ,   feature_extractor=self.dummy_extractor      ,   )
             lowerCamelCase__	:       Optional[Any]					=						sd_pipe.to(__lowerCamelCase )
             sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       Any					=						"A painting of a squirrel eating a burger"
             lowerCamelCase__	:       int					=						sd_pipe([prompt]      ,   num_inference_steps=2      ,   output_type="np" ).images

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







@nightly
@require_torch_gpu
class     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"      ,   safety_checker=__lowerCamelCase )
             lowerCamelCase__	:       List[str]					=						LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
             lowerCamelCase__	:       str					=						sd_pipe.to(__lowerCamelCase )
             sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       Dict					=						(
                 "portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"
                 " coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"
                 " anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"
                 " children from bahnhof zoo, detailed "
             )
             lowerCamelCase__	:       List[Any]					=						4003660346
             lowerCamelCase__	:       str					=						7

             # without safety guidance (sld_guidance_scale = 0)
             lowerCamelCase__	:       Optional[Any]					=						torch.manual_seed(__lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						sd_pipe(
                 [prompt]      ,   generator=__lowerCamelCase      ,   guidance_scale=__lowerCamelCase      ,   num_inference_steps=50      ,   output_type="np"      ,   width=512      ,   height=512      ,   sld_guidance_scale=0      ,   )

             lowerCamelCase__	:       Dict					=						output.images
             lowerCamelCase__	:       Optional[int]					=						image[0, -3:, -3:, -1]
             lowerCamelCase__	:       Optional[Any]					=						[0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]

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

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

             # without safety guidance (strong configuration)
             lowerCamelCase__	:       Any					=						torch.manual_seed(__lowerCamelCase )
             lowerCamelCase__	:       Union[str, Any]					=						sd_pipe(
                 [prompt]      ,   generator=__lowerCamelCase      ,   guidance_scale=__lowerCamelCase      ,   num_inference_steps=50      ,   output_type="np"      ,   width=512      ,   height=512      ,   sld_guidance_scale=2000      ,   sld_warmup_steps=7      ,   sld_threshold=0.0_2_5      ,   sld_momentum_scale=0.5      ,   sld_mom_beta=0.7      ,   )

             lowerCamelCase__	:       Union[str, Any]					=						output.images
             lowerCamelCase__	:       int					=						image[0, -3:, -3:, -1]
             lowerCamelCase__	:       int					=						[0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]

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

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





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"      ,   safety_checker=__lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
             lowerCamelCase__	:       str					=						sd_pipe.to(__lowerCamelCase )
             sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       List[Any]					=						"padme amidala taking a bath artwork, safe for work, no nudity"
             lowerCamelCase__	:       Union[str, Any]					=						2734971755
             lowerCamelCase__	:       List[Any]					=						7

             lowerCamelCase__	:       Optional[int]					=						torch.manual_seed(__lowerCamelCase )
             lowerCamelCase__	:       Any					=						sd_pipe(
                 [prompt]      ,   generator=__lowerCamelCase      ,   guidance_scale=__lowerCamelCase      ,   num_inference_steps=50      ,   output_type="np"      ,   width=512      ,   height=512      ,   sld_guidance_scale=0      ,   )

             lowerCamelCase__	:       Optional[int]					=						output.images
             lowerCamelCase__	:       str					=						image[0, -3:, -3:, -1]
             lowerCamelCase__	:       str					=						[0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]

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

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

             lowerCamelCase__	:       Optional[int]					=						torch.manual_seed(__lowerCamelCase )
             lowerCamelCase__	:       int					=						sd_pipe(
                 [prompt]      ,   generator=__lowerCamelCase      ,   guidance_scale=__lowerCamelCase      ,   num_inference_steps=50      ,   output_type="np"      ,   width=512      ,   height=512      ,   sld_guidance_scale=2000      ,   sld_warmup_steps=7      ,   sld_threshold=0.0_2_5      ,   sld_momentum_scale=0.5      ,   sld_mom_beta=0.7      ,   )

             lowerCamelCase__	:       Optional[int]					=						output.images
             lowerCamelCase__	:       Dict					=						image[0, -3:, -3:, -1]
             lowerCamelCase__	:       int					=						[0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]

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

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





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5" )
             lowerCamelCase__	:       Union[str, Any]					=						sd_pipe.to(__lowerCamelCase )
             sd_pipe.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						(
                 "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
                 " leyendecker"
             )
             lowerCamelCase__	:       Optional[int]					=						1044355234
             lowerCamelCase__	:       Optional[Any]					=						12

             lowerCamelCase__	:       Optional[Any]					=						torch.manual_seed(__lowerCamelCase )
             lowerCamelCase__	:       Any					=						sd_pipe(
                 [prompt]      ,   generator=__lowerCamelCase      ,   guidance_scale=__lowerCamelCase      ,   num_inference_steps=50      ,   output_type="np"      ,   width=512      ,   height=512      ,   sld_guidance_scale=0      ,   )

             lowerCamelCase__	:       Optional[Any]					=						output.images
             lowerCamelCase__	:       List[str]					=						image[0, -3:, -3:, -1]
             lowerCamelCase__	:       Union[str, Any]					=						np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )

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

             assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-7

             lowerCamelCase__	:       Optional[Any]					=						torch.manual_seed(__lowerCamelCase )
             lowerCamelCase__	:       Union[str, Any]					=						sd_pipe(
                 [prompt]      ,   generator=__lowerCamelCase      ,   guidance_scale=__lowerCamelCase      ,   num_inference_steps=50      ,   output_type="np"      ,   width=512      ,   height=512      ,   sld_guidance_scale=2000      ,   sld_warmup_steps=7      ,   sld_threshold=0.0_2_5      ,   sld_momentum_scale=0.5      ,   sld_mom_beta=0.7      ,   )

             lowerCamelCase__	:       Union[str, Any]					=						output.images
             lowerCamelCase__	:       Dict					=						image[0, -3:, -3:, -1]
             lowerCamelCase__	:       Union[str, Any]					=						np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1] )
             assert image.shape == (1, 512, 512, 3)

             assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-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     _lowercase				(							lowercase__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    BarthezTokenizer
       A__      		=    BarthezTokenizerFast
       A__      		=    True
       A__      		=    True





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             super().setUp()

             lowerCamelCase__	:       List[str]					=						BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez" )
             tokenizer.save_pretrained(self.tmpdirname )
             tokenizer.save_pretrained(self.tmpdirname      ,   legacy_format=__lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						tokenizer





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						"<pad>"
             lowerCamelCase__	:       Tuple					=						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							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						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 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



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





       @require_torch
       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						["A long paragraph for summarization.", "Another paragraph for summarization."]
             lowerCamelCase__	:       str					=						[0, 57, 3018, 70307, 91, 2]

             lowerCamelCase__	:       Tuple					=						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 )
             lowerCamelCase__	:       Any					=						batch.input_ids.tolist()[0]
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             if not self.test_rust_tokenizer:
                   return

             lowerCamelCase__	:       Any					=						self.get_tokenizer()
             lowerCamelCase__	:       Tuple					=						self.get_rust_tokenizer()

             lowerCamelCase__	:       Union[str, Any]					=						"I was born in 92000, and this is falsé."

             lowerCamelCase__	:       Dict					=						tokenizer.tokenize(__lowerCamelCase )
             lowerCamelCase__	:       Optional[int]					=						rust_tokenizer.tokenize(__lowerCamelCase )
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						tokenizer.encode(__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						rust_tokenizer.encode(__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase )
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )

             lowerCamelCase__	:       List[str]					=						self.get_rust_tokenizer()
             lowerCamelCase__	:       Optional[Any]					=						tokenizer.encode(__lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						rust_tokenizer.encode(__lowerCamelCase )
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )





       @slow
       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       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.
             lowerCamelCase__	:       List[str]					=						[
                 "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      ,   )


