'''simple docstring'''




import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


__UpperCAmelCase												=logging.get_logger(__name__)  # pylint: disable=invalid-name




class    a__				(       UpperCAmelCase__			):






     def __init__(			self		:		Optional[int]  ,    a		:		WhisperForConditionalGeneration  ,    a		:		WhisperProcessor  ,    a		:		AutoencoderKL  ,    a		:		CLIPTextModel  ,    a		:		CLIPTokenizer  ,    a		:		UNetaDConditionModel  ,    a		:		Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]  ,    a		:		StableDiffusionSafetyChecker  ,    a		:		CLIPImageProcessor  ,    ):





         """simple docstring"""



         super().__init__()

         if safety_checker is None:
             logger.warning(
                 f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
                 ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
                 ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
                 ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
                 ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
                 ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'''   )

         self.register_modules(
             speech_model=a  ,    speech_processor=a  ,    vae=a  ,    text_encoder=a  ,    tokenizer=a  ,    unet=a  ,    scheduler=a  ,    feature_extractor=a  ,    )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]  ,    a		:		Optional[Union[str, int]] = "auto"   ):





         """simple docstring"""



         if slice_size == "auto":
             __lowerCamelCase          =  self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]   ):





         """simple docstring"""



         self.enable_attention_slicing(a   )






     @torch.no_grad()
     def __call__(			self		:		Optional[int]  ,    a		:		Union[str, Any]  ,    a		:		Union[str, Any]=1_60_00  ,    a		:		int = 5_12  ,    a		:		int = 5_12  ,    a		:		int = 50  ,    a		:		float = 7.5  ,    a		:		Optional[Union[str, List[str]]] = None  ,    a		:		Optional[int] = 1  ,    a		:		float = 0.0  ,    a		:		Optional[torch.Generator] = None  ,    a		:		Optional[torch.FloatTensor] = None  ,    a		:		Optional[str] = "pil"  ,    a		:		bool = True  ,    a		:		Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,    a		:		int = 1  ,    **a		:		Tuple  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  self.speech_processor.feature_extractor(
             a  ,    return_tensors='''pt'''  ,    sampling_rate=a   ).input_features.to(self.device   )
         __lowerCamelCase          =  self.speech_model.generate(a  ,    max_length=48_00_00   )

         __lowerCamelCase          =  self.speech_processor.tokenizer.batch_decode(a  ,    skip_special_tokens=a  ,    normalize=a   )[
             0
         ]

         if isinstance(a  ,    a   ):
             __lowerCamelCase          =  1
         elif isinstance(a  ,    a   ):
             __lowerCamelCase          =  len(a   )
         else:
             raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(a   )}"""   )

         if height % 8 != 0 or width % 8 != 0:
             raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""   )

         if (callback_steps is None) or (
             callback_steps is not None and (not isinstance(a  ,    a   ) or callback_steps <= 0)
         ):
             raise ValueError(
                 f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                 f""" {type(a   )}."""   )

         # get prompt text embeddings
         __lowerCamelCase          =  self.tokenizer(
             a  ,    padding='''max_length'''  ,    max_length=self.tokenizer.model_max_length  ,    return_tensors='''pt'''  ,    )
         __lowerCamelCase          =  text_inputs.input_ids

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             __lowerCamelCase          =  self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]   )
             logger.warning(
                 '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                 f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""   )
             __lowerCamelCase          =  text_input_ids[:, : self.tokenizer.model_max_length]
         __lowerCamelCase          =  self.text_encoder(text_input_ids.to(self.device   )   )[0]

         # duplicate text embeddings for each generation per prompt, using mps friendly method
         __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase          =  text_embeddings.shape
         __lowerCamelCase          =  text_embeddings.repeat(1  ,    a  ,    1   )
         __lowerCamelCase          =  text_embeddings.view(bs_embed * num_images_per_prompt  ,    a  ,    -1   )

         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         __lowerCamelCase          =  guidance_scale > 1.0
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance:
             __lowerCamelCase          =  42
             if negative_prompt is None:
                 __lowerCamelCase          =  [''''''] * batch_size
             elif type(a   ) is not type(a   ):
                 raise TypeError(
                     f"""`negative_prompt` should be the same type to `prompt`, but got {type(a   )} !="""
                     f""" {type(a   )}."""   )
             elif isinstance(a  ,    a   ):
                 __lowerCamelCase          =  [negative_prompt]
             elif batch_size != len(a   ):
                 raise ValueError(
                     f"""`negative_prompt`: {negative_prompt} has batch size {len(a   )}, but `prompt`:"""
                     f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                     ''' the batch size of `prompt`.'''   )
             else:
                 __lowerCamelCase          =  negative_prompt

             __lowerCamelCase          =  text_input_ids.shape[-1]
             __lowerCamelCase          =  self.tokenizer(
                 a  ,    padding='''max_length'''  ,    max_length=a  ,    truncation=a  ,    return_tensors='''pt'''  ,    )
             __lowerCamelCase          =  self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             __lowerCamelCase          =  uncond_embeddings.shape[1]
             __lowerCamelCase          =  uncond_embeddings.repeat(1  ,    a  ,    1   )
             __lowerCamelCase          =  uncond_embeddings.view(batch_size * num_images_per_prompt  ,    a  ,    -1   )

             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             __lowerCamelCase          =  torch.cat([uncond_embeddings, text_embeddings]   )

         # get the initial random noise unless the user supplied it

         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
         __lowerCamelCase          =  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         __lowerCamelCase          =  text_embeddings.dtype
         if latents is None:
             if self.device.type == "mps":
                 # randn does not exist on mps
                 __lowerCamelCase          =  torch.randn(a  ,    generator=a  ,    device='''cpu'''  ,    dtype=a   ).to(
                     self.device   )
             else:
                 __lowerCamelCase          =  torch.randn(a  ,    generator=a  ,    device=self.device  ,    dtype=a   )
         else:
             if latents.shape != latents_shape:
                 raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""   )
             __lowerCamelCase          =  latents.to(self.device   )

         # set timesteps
         self.scheduler.set_timesteps(a   )

         # Some schedulers like PNDM have timesteps as arrays
         # It's more optimized to move all timesteps to correct device beforehand
         __lowerCamelCase          =  self.scheduler.timesteps.to(self.device   )

         # scale the initial noise by the standard deviation required by the scheduler
         __lowerCamelCase          =  latents * self.scheduler.init_noise_sigma

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
         __lowerCamelCase          =  '''eta''' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
         __lowerCamelCase          =  {}
         if accepts_eta:
             __lowerCamelCase          =  eta

         for i, t in enumerate(self.progress_bar(a   )   ):
             # expand the latents if we are doing classifier free guidance
             __lowerCamelCase          =  torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
             __lowerCamelCase          =  self.scheduler.scale_model_input(a  ,    a   )

             # predict the noise residual
             __lowerCamelCase          =  self.unet(a  ,    a  ,    encoder_hidden_states=a   ).sample

             # perform guidance
             if do_classifier_free_guidance:
                 __lowerCamelCase     ,  __lowerCamelCase          =  noise_pred.chunk(2   )
                 __lowerCamelCase          =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

             # compute the previous noisy sample x_t -> x_t-1
             __lowerCamelCase          =  self.scheduler.step(a  ,    a  ,    a  ,    **a   ).prev_sample

             # call the callback, if provided
             if callback is not None and i % callback_steps == 0:
                 callback(a  ,    a  ,    a   )

         __lowerCamelCase          =  1 / 0.1_82_15 * latents
         __lowerCamelCase          =  self.vae.decode(a   ).sample

         __lowerCamelCase          =  (image / 2 + 0.5).clamp(0  ,    1   )

         # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         __lowerCamelCase          =  image.cpu().permute(0  ,    2  ,    3  ,    1   ).float().numpy()

         if output_type == "pil":
             __lowerCamelCase          =  self.numpy_to_pil(a   )

         if not return_dict:
             return image

         return StableDiffusionPipelineOutput(images=a  ,    nsfw_content_detected=a   )

'''simple docstring'''




import unittest

from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCAmelCase												=get_tests_dir("fixtures/spiece.model")




@require_sentencepiece
@require_tokenizers
class    a__				(       UpperCAmelCase__							,       unittest.TestCase			):
     lowerCamelCase  :					Optional[Any]							=DebertaVaTokenizer
     lowerCamelCase  :					Optional[int]							=DebertaVaTokenizerFast
     lowerCamelCase  :					Optional[Any]							=True
     lowerCamelCase  :					Tuple							=True






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         super().setUp()

         # We have a SentencePiece fixture for testing
         __lowerCamelCase          =  DebertaVaTokenizer(a  ,    unk_token='''<unk>'''   )
         tokenizer.save_pretrained(self.tmpdirname   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]  ,    a		:		Dict   ):





         """simple docstring"""



         __lowerCamelCase          =  '''this is a test'''
         __lowerCamelCase          =  '''this is a test'''
         return input_text, output_text






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]   ):





         """simple docstring"""



         __lowerCamelCase          =  '''<pad>'''
         __lowerCamelCase          =  0

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  list(self.get_tokenizer().get_vocab().keys()   )
         self.assertEqual(vocab_keys[0]  ,    '''<pad>'''   )
         self.assertEqual(vocab_keys[1]  ,    '''<unk>'''   )
         self.assertEqual(vocab_keys[-1]  ,    '''[PAD]'''   )
         self.assertEqual(len(a   )  ,    3_00_01   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





         """simple docstring"""



         self.assertEqual(self.get_tokenizer().vocab_size  ,    3_00_00   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  ''' \tHeLLo!how  \n Are yoU?  '''
         __lowerCamelCase          =  ['''▁hello''', '''!''', '''how''', '''▁are''', '''▁you''', '''?''']
         # fmt: on

         __lowerCamelCase          =  DebertaVaTokenizer(a  ,    do_lower_case=a   )
         __lowerCamelCase          =  tokenizer.convert_ids_to_tokens(tokenizer.encode(a  ,    add_special_tokens=a   )   )

         self.assertListEqual(a  ,    a   )

         __lowerCamelCase          =  DebertaVaTokenizerFast(a  ,    do_lower_case=a   )
         __lowerCamelCase          =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a  ,    add_special_tokens=a   )   )

         self.assertListEqual(a  ,    a   )






     @unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.'''   )
     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict   ):





         """simple docstring"""



         pass






     @unittest.skip('''There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.'''   )
     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         pass






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  '''I was born in 92000, and this is falsé.'''
         __lowerCamelCase          =  ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
         # fmt: on

         __lowerCamelCase          =  DebertaVaTokenizer(a  ,    split_by_punct=a   )
         __lowerCamelCase          =  tokenizer.convert_ids_to_tokens(tokenizer.encode(a  ,    add_special_tokens=a   )   )

         self.assertListEqual(a  ,    a   )

         __lowerCamelCase          =  DebertaVaTokenizerFast(a  ,    split_by_punct=a   )
         __lowerCamelCase          =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a  ,    add_special_tokens=a   )   )

         self.assertListEqual(a  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         __lowerCamelCase          =  '''I was born in 92000, and this is falsé.'''
         __lowerCamelCase          =  ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
         # fmt: on

         __lowerCamelCase          =  DebertaVaTokenizer(a  ,    do_lower_case=a  ,    split_by_punct=a   )
         __lowerCamelCase          =  tokenizer.convert_ids_to_tokens(tokenizer.encode(a  ,    add_special_tokens=a   )   )
         self.assertListEqual(a  ,    a   )

         __lowerCamelCase          =  DebertaVaTokenizerFast(a  ,    do_lower_case=a  ,    split_by_punct=a   )
         __lowerCamelCase          =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a  ,    add_special_tokens=a   )   )
         self.assertListEqual(a  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         __lowerCamelCase          =  '''I was born in 92000, and this is falsé.'''
         __lowerCamelCase          =  ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
         # fmt: on

         __lowerCamelCase          =  DebertaVaTokenizer(a  ,    do_lower_case=a  ,    split_by_punct=a   )
         __lowerCamelCase          =  tokenizer.convert_ids_to_tokens(tokenizer.encode(a  ,    add_special_tokens=a   )   )

         self.assertListEqual(a  ,    a   )

         __lowerCamelCase          =  DebertaVaTokenizerFast(a  ,    do_lower_case=a  ,    split_by_punct=a   )
         __lowerCamelCase          =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a  ,    add_special_tokens=a   )   )

         self.assertListEqual(a  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  '''I was born in 92000, and this is falsé.'''
         __lowerCamelCase          =  ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', '''▁''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''▁''', '''.''', ]
         # fmt: on

         __lowerCamelCase          =  DebertaVaTokenizer(a  ,    do_lower_case=a  ,    split_by_punct=a   )
         __lowerCamelCase          =  tokenizer.convert_ids_to_tokens(tokenizer.encode(a  ,    add_special_tokens=a   )   )

         self.assertListEqual(a  ,    a   )

         __lowerCamelCase          =  DebertaVaTokenizerFast(a  ,    do_lower_case=a  ,    split_by_punct=a   )
         __lowerCamelCase          =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a  ,    add_special_tokens=a   )   )

         self.assertListEqual(a  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  ''' \tHeLLo!how  \n Are yoU?  '''
         __lowerCamelCase          =  ['''▁''', '''<unk>''', '''e''', '''<unk>''', '''o''', '''!''', '''how''', '''▁''', '''<unk>''', '''re''', '''▁yo''', '''<unk>''', '''?''']
         # fmt: on

         __lowerCamelCase          =  DebertaVaTokenizer(a  ,    do_lower_case=a  ,    split_by_punct=a   )
         __lowerCamelCase          =  tokenizer.convert_ids_to_tokens(tokenizer.encode(a  ,    add_special_tokens=a   )   )

         self.assertListEqual(a  ,    a   )

         __lowerCamelCase          =  DebertaVaTokenizerFast(a  ,    do_lower_case=a  ,    split_by_punct=a   )
         __lowerCamelCase          =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a  ,    add_special_tokens=a   )   )

         self.assertListEqual(a  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.get_tokenizer()
         __lowerCamelCase          =  self.get_rust_tokenizer()

         __lowerCamelCase          =  '''I was born in 92000, and this is falsé.'''

         __lowerCamelCase          =  tokenizer.convert_ids_to_tokens(tokenizer.encode(a  ,    add_special_tokens=a   )   )
         __lowerCamelCase          =  rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(a  ,    add_special_tokens=a   )   )
         self.assertListEqual(a  ,    a   )

         __lowerCamelCase          =  tokenizer.encode(a  ,    add_special_tokens=a   )
         __lowerCamelCase          =  rust_tokenizer.encode(a  ,    add_special_tokens=a   )
         self.assertListEqual(a  ,    a   )

         __lowerCamelCase          =  self.get_rust_tokenizer()
         __lowerCamelCase          =  tokenizer.encode(a   )
         __lowerCamelCase          =  rust_tokenizer.encode(a   )
         self.assertListEqual(a  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  '''This is a test'''
         __lowerCamelCase          =  [13, 1, 43_98, 25, 21, 12_89]
         __lowerCamelCase          =  ['''▁''', '''T''', '''his''', '''▁is''', '''▁a''', '''▁test''']
         __lowerCamelCase          =  ['''▁''', '''<unk>''', '''his''', '''▁is''', '''▁a''', '''▁test''']

         __lowerCamelCase          =  DebertaVaTokenizer(a  ,    keep_accents=a   )
         __lowerCamelCase          =  DebertaVaTokenizerFast(a  ,    keep_accents=a   )

         __lowerCamelCase          =  tokenizer.encode(a  ,    add_special_tokens=a   )
         self.assertListEqual(a  ,    a   )
         __lowerCamelCase          =  tokenizer.tokenize(a   )
         self.assertListEqual(a  ,    a   )
         __lowerCamelCase          =  tokenizer.convert_ids_to_tokens(a   )
         self.assertListEqual(a  ,    a   )

         __lowerCamelCase          =  rust_tokenizer.encode(a  ,    add_special_tokens=a   )
         self.assertListEqual(a  ,    a   )
         __lowerCamelCase          =  rust_tokenizer.tokenize(a   )
         self.assertListEqual(a  ,    a   )
         __lowerCamelCase          =  rust_tokenizer.convert_ids_to_tokens(a   )
         self.assertListEqual(a  ,    a   )

         # fmt: off
         __lowerCamelCase          =  '''I was born in 92000, and this is falsé.'''
         __lowerCamelCase          =  [13, 1, 23, 3_86, 19, 5_61, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]
         __lowerCamelCase          =  ['''▁''', '''I''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''', ]
         __lowerCamelCase          =  ['''▁''', '''<unk>''', '''▁was''', '''▁born''', '''▁in''', '''▁9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''', ]
         # fmt: on

         __lowerCamelCase          =  tokenizer.encode(a  ,    add_special_tokens=a   )
         self.assertListEqual(a  ,    a   )
         __lowerCamelCase          =  tokenizer.tokenize(a   )
         self.assertListEqual(a  ,    a   )
         __lowerCamelCase          =  tokenizer.convert_ids_to_tokens(a   )
         self.assertListEqual(a  ,    a   )

         __lowerCamelCase          =  rust_tokenizer.encode(a  ,    add_special_tokens=a   )
         self.assertListEqual(a  ,    a   )
         __lowerCamelCase          =  rust_tokenizer.tokenize(a   )
         self.assertListEqual(a  ,    a   )
         __lowerCamelCase          =  rust_tokenizer.convert_ids_to_tokens(a   )
         self.assertListEqual(a  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         __lowerCamelCase          =  DebertaVaTokenizer(a   )

         __lowerCamelCase          =  tokenizer.encode('''sequence builders'''   )
         __lowerCamelCase          =  tokenizer.encode('''multi-sequence build'''   )

         __lowerCamelCase          =  tokenizer.build_inputs_with_special_tokens(a   )
         __lowerCamelCase          =  tokenizer.build_inputs_with_special_tokens(a  ,    a   )

         self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]  ,    a   )
         self.assertEqual(
             [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id]  ,    a  ,    )






     @slow
     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         __lowerCamelCase          =  {'''input_ids''': [[1, 3_98_67, 36, 1_93_90, 4_86, 27, 3_50_52, 8_14_36, 18, 6_06_85, 12_25, 7, 3_50_52, 8_14_36, 18, 93_67, 1_68_99, 18, 1_59_37, 53, 5_94, 7_73, 18, 1_62_87, 3_04_65, 36, 1_59_37, 6, 4_11_39, 38, 3_69_79, 6_07_63, 1_91, 6, 3_41_32, 99, 6, 5_05_38, 3_90, 4_32_30, 6, 3_41_32, 27_79, 2_08_50, 14, 6_99, 10_72, 11_94, 36, 3_82, 1_09_01, 53, 7, 6_99, 10_72, 20_84, 36, 2_04_22, 6_30, 53, 19, 1_05, 30_49, 18_96, 10_53, 1_68_99, 15_06, 11, 3_79_78, 42_43, 7, 12_37, 3_18_69, 2_00, 1_65_66, 6_54, 6, 3_50_52, 8_14_36, 7, 5_56_30, 1_35_93, 4, 2], [1, 26, 1_50_11, 13, 6_67, 8, 10_53, 18, 2_36_11, 12_37, 7_23_56, 1_28_20, 34, 10_41_34, 12_09, 35, 1_33_13, 66_27, 21, 2_02, 3_47, 7, 1_64, 23_99, 11, 46, 44_85, 4, 2, 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], [1, 5, 12_32, 28_64, 1_57_85, 1_49_51, 1_05, 5, 85_81, 12_50, 4, 2, 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]], '''token_type_ids''': [[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, 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, 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, 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, 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], [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, 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=a  ,    model_name='''microsoft/deberta-v2-xlarge'''  ,    revision='''ad6e42c1532ddf3a15c39246b63f5559d558b670'''  ,    )
