



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.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



class 						_snake_case (     unittest.TestCase			):





 """simple docstring"""



 def lowercase_						(  self   )		->							Tuple:




   """simple docstring"""







   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()
 @property
 def lowercase_						(  self   )		->							str:




   """simple docstring"""







   _A          =      1
   _A          =      3
   _A          =      (3_2, 3_2)

   _A          =      floats_tensor((batch_size, num_channels) + sizes		,      rng=random.Random(0   )   ).to(a   )
   return image
 @property
 def lowercase_						(  self   )		->							List[Any]:




   """simple docstring"""







   torch.manual_seed(0   )
   _A          =      UNetaDConditionModel(
       block_out_channels=(3_2, 6_4)		,      layers_per_block=2		,      sample_size=3_2		,      in_channels=4		,      out_channels=4		,      down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,      up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,      cross_attention_dim=3_2		,      )
   return model
 @property
 def lowercase_						(  self   )		->							Union[str, Any]:




   """simple docstring"""







   torch.manual_seed(0   )
   _A          =      AutoencoderKL(
       block_out_channels=[3_2, 6_4]		,      in_channels=3		,      out_channels=3		,      down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,      up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,      latent_channels=4		,      )
   return model
 @property
 def lowercase_						(  self   )		->							Optional[int]:




   """simple docstring"""







   torch.manual_seed(0   )
   _A          =      CLIPTextConfig(
       bos_token_id=0		,      eos_token_id=2		,      hidden_size=3_2		,      intermediate_size=3_7		,      layer_norm_eps=1e-05		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1_0_0_0		,      )
   return CLIPTextModel(a   )
 @property
 def lowercase_						(  self   )		->							str:




   """simple docstring"""







   def extract(*a		,      **a   ):



     class 						_snake_case :





      """simple docstring"""



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




        """simple docstring"""







        _A          =      torch.ones([0]   )



      def lowercase_						(  self		,      a   )		->							List[Any]:




        """simple docstring"""







        self.pixel_values.to(a   )
        return self







     return Out()

   return extract
 def lowercase_						(  self   )		->							Dict:




   """simple docstring"""







   _A          =      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
   _A          =      self.dummy_cond_unet
   _A          =      DDIMScheduler(
       beta_start=0.00_085		,      beta_end=0.012		,      beta_schedule='''scaled_linear'''		,      clip_sample=a		,      set_alpha_to_one=a		,      )

   _A          =      self.dummy_vae
   _A          =      self.dummy_text_encoder
   _A          =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

   # make sure here that pndm scheduler skips prk
   _A          =      StableDiffusionPipeline(
       unet=a		,      scheduler=a		,      vae=a		,      text_encoder=a		,      tokenizer=a		,      safety_checker=a		,      feature_extractor=self.dummy_extractor		,      )
   _A          =      sd_pipe.to(a   )
   sd_pipe.set_progress_bar_config(disable=a   )

   _A          =      '''A painting of a squirrel eating a burger'''

   _A          =      torch.Generator(device=a   ).manual_seed(0   )
   _A          =      sd_pipe([prompt]		,      generator=a		,      guidance_scale=6.0		,      num_inference_steps=2		,      output_type='''np'''   )
   _A          =      output.images

   _A          =      torch.Generator(device=a   ).manual_seed(0   )
   _A          =      sd_pipe(
       [prompt]		,      generator=a		,      guidance_scale=6.0		,      num_inference_steps=2		,      output_type='''np'''		,      return_dict=a		,      )[0]

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

   assert image.shape == (1, 6_4, 6_4, 3)
   _A          =      np.array([0.5_753, 0.6_114, 0.5_001, 0.5_034, 0.5_470, 0.4_729, 0.4_971, 0.4_867, 0.4_867]   )

   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 lowercase_						(  self   )		->							Dict:




   """simple docstring"""







   _A          =      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
   _A          =      self.dummy_cond_unet
   _A          =      PNDMScheduler(skip_prk_steps=a   )
   _A          =      self.dummy_vae
   _A          =      self.dummy_text_encoder
   _A          =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

   # make sure here that pndm scheduler skips prk
   _A          =      StableDiffusionPipeline(
       unet=a		,      scheduler=a		,      vae=a		,      text_encoder=a		,      tokenizer=a		,      safety_checker=a		,      feature_extractor=self.dummy_extractor		,      )
   _A          =      sd_pipe.to(a   )
   sd_pipe.set_progress_bar_config(disable=a   )

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

   _A          =      output.images

   _A          =      torch.Generator(device=a   ).manual_seed(0   )
   _A          =      sd_pipe(
       [prompt]		,      generator=a		,      guidance_scale=6.0		,      num_inference_steps=2		,      output_type='''np'''		,      return_dict=a		,      )[0]

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

   assert image.shape == (1, 6_4, 6_4, 3)
   _A          =      np.array([0.5_122, 0.5_712, 0.4_825, 0.5_053, 0.5_646, 0.4_769, 0.5_179, 0.4_894, 0.4_994]   )

   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 lowercase_						(  self   )		->							str:




   """simple docstring"""







   _A          =      StableDiffusionPipeline.from_pretrained(
       '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''		,      safety_checker=a   )
   assert isinstance(a		,      a   )
   assert isinstance(pipe.scheduler		,      a   )
   assert pipe.safety_checker is None

   _A          =      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(a   )
     _A          =      StableDiffusionPipeline.from_pretrained(a   )

   # sanity check that the pipeline still works
   assert pipe.safety_checker is None
   _A          =      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 lowercase_						(  self   )		->							str:




   """simple docstring"""







   _A          =      self.dummy_cond_unet
   _A          =      PNDMScheduler(skip_prk_steps=a   )
   _A          =      self.dummy_vae
   _A          =      self.dummy_text_encoder
   _A          =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

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

   # make sure here that pndm scheduler skips prk
   _A          =      StableDiffusionPipeline(
       unet=a		,      scheduler=a		,      vae=a		,      text_encoder=a		,      tokenizer=a		,      safety_checker=a		,      feature_extractor=self.dummy_extractor		,      )
   _A          =      sd_pipe.to(a   )
   sd_pipe.set_progress_bar_config(disable=a   )

   _A          =      '''A painting of a squirrel eating a burger'''
   _A          =      sd_pipe([prompt]		,      num_inference_steps=2		,      output_type='''np'''   ).images

   assert image.shape == (1, 6_4, 6_4, 3)







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





 """simple docstring"""



 def lowercase_						(  self   )		->							int:




   """simple docstring"""







   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()
 def lowercase_						(  self   )		->							List[str]:




   """simple docstring"""







   _A          =      '''cuda'''
   _A          =      StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''   )
   _A          =      pipe.to(a   )
   pipe.set_progress_bar_config(disable=a   )

   _A          =      '''a photo of a cat'''
   _A          =      {
       '''editing_prompt''': ['''sunglasses'''],
       '''reverse_editing_direction''': [False],
       '''edit_warmup_steps''': 1_0,
       '''edit_guidance_scale''': 6,
       '''edit_threshold''': 0.95,
       '''edit_momentum_scale''': 0.5,
       '''edit_mom_beta''': 0.6,
   }

   _A          =      3
   _A          =      7

   # no sega enabled
   _A          =      torch.Generator(a   )
   generator.manual_seed(a   )
   _A          =      pipe(
       [prompt]		,      generator=a		,      guidance_scale=a		,      num_inference_steps=5_0		,      output_type='''np'''		,      width=5_1_2		,      height=5_1_2		,      )

   _A          =      output.images
   _A          =      image[0, -3:, -3:, -1]
   _A          =      [
       0.34_673_113,
       0.38_492_733,
       0.37_597_352,
       0.34_086_335,
       0.35_650_748,
       0.35_579_205,
       0.3_384_763,
       0.34_340_236,
       0.3_573_271,
   ]

   assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

   # with sega enabled
   # generator = torch.manual_seed(seed)
   generator.manual_seed(a   )
   _A          =      pipe(
       [prompt]		,      generator=a		,      guidance_scale=a		,      num_inference_steps=5_0		,      output_type='''np'''		,      width=5_1_2		,      height=5_1_2		,      **a		,      )

   _A          =      output.images
   _A          =      image[0, -3:, -3:, -1]
   _A          =      [
       0.41_887_826,
       0.37_728_766,
       0.30_138_272,
       0.41_416_335,
       0.41_664_985,
       0.36_283_392,
       0.36_191_246,
       0.43_364_465,
       0.43_001_732,
   ]

   assert image.shape == (1, 5_1_2, 5_1_2, 3)

   assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
 def lowercase_						(  self   )		->							Union[str, Any]:




   """simple docstring"""







   _A          =      '''cuda'''
   _A          =      StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''   )
   _A          =      pipe.to(a   )
   pipe.set_progress_bar_config(disable=a   )

   _A          =      '''an image of a crowded boulevard, realistic, 4k'''
   _A          =      {
       '''editing_prompt''': '''crowd, crowded, people''',
       '''reverse_editing_direction''': True,
       '''edit_warmup_steps''': 1_0,
       '''edit_guidance_scale''': 8.3,
       '''edit_threshold''': 0.9,
       '''edit_momentum_scale''': 0.5,
       '''edit_mom_beta''': 0.6,
   }

   _A          =      9
   _A          =      7

   # no sega enabled
   _A          =      torch.Generator(a   )
   generator.manual_seed(a   )
   _A          =      pipe(
       [prompt]		,      generator=a		,      guidance_scale=a		,      num_inference_steps=5_0		,      output_type='''np'''		,      width=5_1_2		,      height=5_1_2		,      )

   _A          =      output.images
   _A          =      image[0, -3:, -3:, -1]
   _A          =      [
       0.43_497_998,
       0.91_814_065,
       0.7_540_739,
       0.55_580_205,
       0.8_467_265,
       0.5_389_691,
       0.62_574_506,
       0.58_897_763,
       0.50_926_757,
   ]

   assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

   # with sega enabled
   # generator = torch.manual_seed(seed)
   generator.manual_seed(a   )
   _A          =      pipe(
       [prompt]		,      generator=a		,      guidance_scale=a		,      num_inference_steps=5_0		,      output_type='''np'''		,      width=5_1_2		,      height=5_1_2		,      **a		,      )

   _A          =      output.images
   _A          =      image[0, -3:, -3:, -1]
   _A          =      [
       0.3_089_719,
       0.30_500_144,
       0.29_016_042,
       0.30_630_964,
       0.325_687,
       0.29_419_225,
       0.2_908_091,
       0.28_723_598,
       0.27_696_294,
   ]

   assert image.shape == (1, 5_1_2, 5_1_2, 3)

   assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
 def lowercase_						(  self   )		->							List[Any]:




   """simple docstring"""







   _A          =      '''cuda'''
   _A          =      StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''   )
   _A          =      pipe.to(a   )
   pipe.set_progress_bar_config(disable=a   )

   _A          =      '''a castle next to a river'''
   _A          =      {
       '''editing_prompt''': ['''boat on a river, boat''', '''monet, impression, sunrise'''],
       '''reverse_editing_direction''': False,
       '''edit_warmup_steps''': [1_5, 1_8],
       '''edit_guidance_scale''': 6,
       '''edit_threshold''': [0.9, 0.8],
       '''edit_momentum_scale''': 0.5,
       '''edit_mom_beta''': 0.6,
   }

   _A          =      4_8
   _A          =      7

   # no sega enabled
   _A          =      torch.Generator(a   )
   generator.manual_seed(a   )
   _A          =      pipe(
       [prompt]		,      generator=a		,      guidance_scale=a		,      num_inference_steps=5_0		,      output_type='''np'''		,      width=5_1_2		,      height=5_1_2		,      )

   _A          =      output.images
   _A          =      image[0, -3:, -3:, -1]
   _A          =      [
       0.75_163_555,
       0.76_037_145,
       0.61_785,
       0.9_189_673,
       0.8_627_701,
       0.85_189_694,
       0.8_512_813,
       0.87_012_076,
       0.8_312_857,
   ]

   assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

   # with sega enabled
   # generator = torch.manual_seed(seed)
   generator.manual_seed(a   )
   _A          =      pipe(
       [prompt]		,      generator=a		,      guidance_scale=a		,      num_inference_steps=5_0		,      output_type='''np'''		,      width=5_1_2		,      height=5_1_2		,      **a		,      )

   _A          =      output.images
   _A          =      image[0, -3:, -3:, -1]
   _A          =      [
       0.73_553_365,
       0.7_537_271,
       0.74_341_905,
       0.66_480_356,
       0.6_472_925,
       0.63_039_416,
       0.64_812_905,
       0.6_749_717,
       0.6_517_102,
   ]

   assert image.shape == (1, 5_1_2, 5_1_2, 3)

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



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




   """simple docstring"""







   _A          =      '''cuda'''
   _A          =      StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''		,      torch_dtype=torch.floataa   )
   _A          =      pipe.to(a   )
   pipe.set_progress_bar_config(disable=a   )

   _A          =      '''a photo of a cat'''
   _A          =      {
       '''editing_prompt''': ['''sunglasses'''],
       '''reverse_editing_direction''': [False],
       '''edit_warmup_steps''': 1_0,
       '''edit_guidance_scale''': 6,
       '''edit_threshold''': 0.95,
       '''edit_momentum_scale''': 0.5,
       '''edit_mom_beta''': 0.6,
   }

   _A          =      3
   _A          =      7

   # no sega enabled
   _A          =      torch.Generator(a   )
   generator.manual_seed(a   )
   _A          =      pipe(
       [prompt]		,      generator=a		,      guidance_scale=a		,      num_inference_steps=5_0		,      output_type='''np'''		,      width=5_1_2		,      height=5_1_2		,      )

   _A          =      output.images
   _A          =      image[0, -3:, -3:, -1]
   _A          =      [
       0.34_887_695,
       0.3_876_953,
       0.375,
       0.34_423_828,
       0.3_581_543,
       0.35_717_773,
       0.3_383_789,
       0.34_570_312,
       0.359_375,
   ]

   assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

   # with sega enabled
   # generator = torch.manual_seed(seed)
   generator.manual_seed(a   )
   _A          =      pipe(
       [prompt]		,      generator=a		,      guidance_scale=a		,      num_inference_steps=5_0		,      output_type='''np'''		,      width=5_1_2		,      height=5_1_2		,      **a		,      )

   _A          =      output.images
   _A          =      image[0, -3:, -3:, -1]
   _A          =      [
       0.42_285_156,
       0.36_914_062,
       0.29_077_148,
       0.42_041_016,
       0.41_918_945,
       0.35_498_047,
       0.3_618_164,
       0.4_423_828,
       0.43_115_234,
   ]

   assert image.shape == (1, 5_1_2, 5_1_2, 3)

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




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


__lowerCamelCase								=   logging.get_logger(__name__)

__lowerCamelCase								=   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase								=   {
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

__lowerCamelCase								=   {
    """microsoft/deberta-base""": 5_1_2,
    """microsoft/deberta-large""": 5_1_2,
    """microsoft/deberta-xlarge""": 5_1_2,
    """microsoft/deberta-base-mnli""": 5_1_2,
    """microsoft/deberta-large-mnli""": 5_1_2,
    """microsoft/deberta-xlarge-mnli""": 5_1_2,
}

__lowerCamelCase								=   {
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     VOCAB_FILES_NAMES
 lowerCamelCase_       =     PRETRAINED_VOCAB_FILES_MAP
 lowerCamelCase_       =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCamelCase_       =     ['''input_ids''', '''attention_mask''', '''token_type_ids''']
 lowerCamelCase_       =     DebertaTokenizer
 def __init__(  self		,      a=None		,      a=None		,      a=None		,      a="replace"		,      a="[CLS]"		,      a="[SEP]"		,      a="[SEP]"		,      a="[CLS]"		,      a="[UNK]"		,      a="[PAD]"		,      a="[MASK]"		,      a=False		,      **a		,      )		->							Any:




   """simple docstring"""







   super().__init__(
       a		,      a		,      tokenizer_file=a		,      errors=a		,      bos_token=a		,      eos_token=a		,      unk_token=a		,      sep_token=a		,      cls_token=a		,      pad_token=a		,      mask_token=a		,      add_prefix_space=a		,      **a		,      )
   _A          =      kwargs.pop('''add_bos_token'''		,      a   )

   _A          =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
   if pre_tok_state.get('''add_prefix_space'''		,      a   ) != add_prefix_space:
     _A          =      getattr(a		,      pre_tok_state.pop('''type'''   )   )
     _A          =      add_prefix_space
     _A          =      pre_tok_class(**a   )

   _A          =      add_prefix_space
 @property
 def lowercase_						(  self   )		->							str:




   """simple docstring"""







   if self._mask_token is None:
     if self.verbose:
       logger.error('''Using mask_token, but it is not set yet.'''   )
     return None
   return str(self._mask_token   )
 @mask_token.setter
 def lowercase_						(  self		,      a   )		->							Dict:




   """simple docstring"""







   _A          =      AddedToken(a		,      lstrip=a		,      rstrip=a   ) if isinstance(a		,      a   ) else value
   _A          =      value
 def lowercase_						(  self		,      a		,      a = None   )		->							List[int]:




   """simple docstring"""







   if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   _A          =      [self.cls_token_id]
   _A          =      [self.sep_token_id]
   return cls + token_ids_a + sep + token_ids_a + sep
 def lowercase_						(  self		,      a		,      a = None   )		->							List[int]:




   """simple docstring"""







   _A          =      [self.sep_token_id]
   _A          =      [self.cls_token_id]

   if token_ids_a is None:
     return len(cls + token_ids_a + sep   ) * [0]
   return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]
 def lowercase_						(  self		,      *a		,      **a   )		->							BatchEncoding:




   """simple docstring"""







   _A          =      kwargs.get('''is_split_into_words'''		,      a   )
   assert self.add_prefix_space or not is_split_into_words, (
       f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
       "to use it with pretokenized inputs."
   )

   return super()._batch_encode_plus(*a		,      **a   )
 def lowercase_						(  self		,      *a		,      **a   )		->							BatchEncoding:




   """simple docstring"""







   _A          =      kwargs.get('''is_split_into_words'''		,      a   )

   assert self.add_prefix_space or not is_split_into_words, (
       f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
       "to use it with pretokenized inputs."
   )

   return super()._encode_plus(*a		,      **a   )
 def lowercase_						(  self		,      a		,      a = None   )		->							Tuple[str]:




   """simple docstring"""







   _A          =      self._tokenizer.model.save(a		,      name=a   )
   return tuple(a   )



 def lowercase_						(  self		,      a   )		->							List[int]:




   """simple docstring"""







   _A          =      []
   for is_user, text in conversation.iter_texts():
     input_ids.extend(self.encode(a		,      add_special_tokens=a   ) + [self.eos_token_id]   )

   if len(a   ) > self.model_max_length:
     _A          =      input_ids[-self.model_max_length :]
   return input_ids