



'''simple docstring'''





import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







    def lowerCamelCase			(       self   :	str		):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




    @property
    def lowerCamelCase			(       self   :	Any		):
          snake_case__    :       Tuple       		= 1
          snake_case__    :       Optional[int]       		= 3
          snake_case__    :       List[str]       		= (32, 32)

          snake_case__    :       Optional[Any]       		= floats_tensor((batch_size, num_channels) + sizes					,	rng=random.Random(0		)		).to(lowercase__		)
          return image




    @property
    def lowerCamelCase			(       self   :	List[Any]		):
          torch.manual_seed(0		)
          snake_case__    :       Tuple       		= UNetaDConditionModel(
              block_out_channels=(32, 32, 64)					,	layers_per_block=2					,	sample_size=32					,	in_channels=7					,	out_channels=4					,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""", """CrossAttnDownBlock2D""")					,	up_block_types=("""CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """UpBlock2D""")					,	cross_attention_dim=32					,	attention_head_dim=8					,	use_linear_projection=lowercase__					,	only_cross_attention=(True, True, False)					,	num_class_embeds=100					,	)
          return model




    @property
    def lowerCamelCase			(       self   :	Any		):
          torch.manual_seed(0		)
          snake_case__    :       Tuple       		= AutoencoderKL(
              block_out_channels=[32, 32, 64]					,	in_channels=3					,	out_channels=3					,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D""", """DownEncoderBlock2D"""]					,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""]					,	latent_channels=4					,	)
          return model




    @property
    def lowerCamelCase			(       self   :	Optional[int]		):
          torch.manual_seed(0		)
          snake_case__    :       int       		= 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=1_000					,	hidden_act="""gelu"""					,	projection_dim=512					,	)
          return CLIPTextModel(lowercase__		)




    def lowerCamelCase			(       self   :	str		):
          snake_case__    :       str       		= """cpu"""  # ensure determinism for the device-dependent torch.Generator
          snake_case__    :       Optional[Any]       		= self.dummy_cond_unet_upscale
          snake_case__    :       Dict       		= DDPMScheduler()
          snake_case__    :       List[str]       		= DDIMScheduler(prediction_type="""v_prediction"""		)
          snake_case__    :       Optional[int]       		= self.dummy_vae
          snake_case__    :       Tuple       		= self.dummy_text_encoder
          snake_case__    :       Optional[Any]       		= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

          snake_case__    :       List[Any]       		= self.dummy_image.cpu().permute(0					,	2					,	3					,	1		)[0]
          snake_case__    :       str       		= Image.fromarray(np.uinta(lowercase__		)		).convert("""RGB"""		).resize((64, 64)		)

          # make sure here that pndm scheduler skips prk
          snake_case__    :       List[Any]       		= StableDiffusionUpscalePipeline(
              unet=lowercase__					,	low_res_scheduler=lowercase__					,	scheduler=lowercase__					,	vae=lowercase__					,	text_encoder=lowercase__					,	tokenizer=lowercase__					,	max_noise_level=350					,	)
          snake_case__    :       int       		= sd_pipe.to(lowercase__		)
          sd_pipe.set_progress_bar_config(disable=lowercase__		)

          snake_case__    :       str       		= """A painting of a squirrel eating a burger"""
          snake_case__    :       int       		= torch.Generator(device=lowercase__		).manual_seed(0		)
          snake_case__    :       Optional[int]       		= sd_pipe(
              [prompt]					,	image=lowercase__					,	generator=lowercase__					,	guidance_scale=6.0					,	noise_level=20					,	num_inference_steps=2					,	output_type="""np"""					,	)

          snake_case__    :       Optional[Any]       		= output.images

          snake_case__    :       int       		= torch.Generator(device=lowercase__		).manual_seed(0		)
          snake_case__    :       str       		= sd_pipe(
              [prompt]					,	image=lowercase__					,	generator=lowercase__					,	guidance_scale=6.0					,	noise_level=20					,	num_inference_steps=2					,	output_type="""np"""					,	return_dict=lowercase__					,	)[0]

          snake_case__    :       int       		= image[0, -3:, -3:, -1]
          snake_case__    :       List[Any]       		= image_from_tuple[0, -3:, -3:, -1]

          snake_case__    :       Tuple       		= low_res_image.size[0] * 4
          assert image.shape == (1, expected_height_width, expected_height_width, 3)
          snake_case__    :       List[str]       		= np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]		)

          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[int]		):
          snake_case__    :       str       		= """cpu"""  # ensure determinism for the device-dependent torch.Generator
          snake_case__    :       Dict       		= self.dummy_cond_unet_upscale
          snake_case__    :       List[Any]       		= DDPMScheduler()
          snake_case__    :       Union[str, Any]       		= DDIMScheduler(prediction_type="""v_prediction"""		)
          snake_case__    :       Union[str, Any]       		= self.dummy_vae
          snake_case__    :       Union[str, Any]       		= self.dummy_text_encoder
          snake_case__    :       Union[str, Any]       		= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

          snake_case__    :       Any       		= self.dummy_image.cpu().permute(0					,	2					,	3					,	1		)[0]
          snake_case__    :       Dict       		= Image.fromarray(np.uinta(lowercase__		)		).convert("""RGB"""		).resize((64, 64)		)

          # make sure here that pndm scheduler skips prk
          snake_case__    :       Tuple       		= StableDiffusionUpscalePipeline(
              unet=lowercase__					,	low_res_scheduler=lowercase__					,	scheduler=lowercase__					,	vae=lowercase__					,	text_encoder=lowercase__					,	tokenizer=lowercase__					,	max_noise_level=350					,	)
          snake_case__    :       Dict       		= sd_pipe.to(lowercase__		)
          sd_pipe.set_progress_bar_config(disable=lowercase__		)

          snake_case__    :       Optional[Any]       		= """A painting of a squirrel eating a burger"""
          snake_case__    :       Any       		= sd_pipe(
              2 * [prompt]					,	image=2 * [low_res_image]					,	guidance_scale=6.0					,	noise_level=20					,	num_inference_steps=2					,	output_type="""np"""					,	)
          snake_case__    :       Optional[Any]       		= output.images
          assert image.shape[0] == 2

          snake_case__    :       Union[str, Any]       		= torch.Generator(device=lowercase__		).manual_seed(0		)
          snake_case__    :       Union[str, Any]       		= sd_pipe(
              [prompt]					,	image=lowercase__					,	generator=lowercase__					,	num_images_per_prompt=2					,	guidance_scale=6.0					,	noise_level=20					,	num_inference_steps=2					,	output_type="""np"""					,	)
          snake_case__    :       Optional[int]       		= output.images
          assert image.shape[0] == 2




    @unittest.skipIf(torch_device != """cuda"""					,	"""This test requires a GPU"""		)
    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       int       		= self.dummy_cond_unet_upscale
          snake_case__    :       Any       		= DDPMScheduler()
          snake_case__    :       Optional[int]       		= DDIMScheduler(prediction_type="""v_prediction"""		)
          snake_case__    :       int       		= self.dummy_vae
          snake_case__    :       Union[str, Any]       		= self.dummy_text_encoder
          snake_case__    :       Tuple       		= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

          snake_case__    :       List[Any]       		= self.dummy_image.cpu().permute(0					,	2					,	3					,	1		)[0]
          snake_case__    :       int       		= Image.fromarray(np.uinta(lowercase__		)		).convert("""RGB"""		).resize((64, 64)		)

          # put models in fp16, except vae as it overflows in fp16
          snake_case__    :       Tuple       		= unet.half()
          snake_case__    :       int       		= text_encoder.half()

          # make sure here that pndm scheduler skips prk
          snake_case__    :       List[str]       		= StableDiffusionUpscalePipeline(
              unet=lowercase__					,	low_res_scheduler=lowercase__					,	scheduler=lowercase__					,	vae=lowercase__					,	text_encoder=lowercase__					,	tokenizer=lowercase__					,	max_noise_level=350					,	)
          snake_case__    :       Union[str, Any]       		= sd_pipe.to(lowercase__		)
          sd_pipe.set_progress_bar_config(disable=lowercase__		)

          snake_case__    :       List[Any]       		= """A painting of a squirrel eating a burger"""
          snake_case__    :       List[str]       		= torch.manual_seed(0		)
          snake_case__    :       List[str]       		= sd_pipe(
              [prompt]					,	image=lowercase__					,	generator=lowercase__					,	num_inference_steps=2					,	output_type="""np"""					,	).images

          snake_case__    :       str       		= low_res_image.size[0] * 4
          assert image.shape == (1, expected_height_width, expected_height_width, 3)







@slow
@require_torch_gpu
class        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







    def lowerCamelCase			(       self   :	Any		):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




    def lowerCamelCase			(       self   :	Optional[Any]		):
          snake_case__    :       List[Any]       		= load_image(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
              """/sd2-upscale/low_res_cat.png"""		)
          snake_case__    :       Union[str, Any]       		= load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
              """/upsampled_cat.npy"""		)

          snake_case__    :       Union[str, Any]       		= """stabilityai/stable-diffusion-x4-upscaler"""
          snake_case__    :       int       		= StableDiffusionUpscalePipeline.from_pretrained(lowercase__		)
          pipe.to(lowercase__		)
          pipe.set_progress_bar_config(disable=lowercase__		)
          pipe.enable_attention_slicing()

          snake_case__    :       Any       		= """a cat sitting on a park bench"""

          snake_case__    :       int       		= torch.manual_seed(0		)
          snake_case__    :       Union[str, Any]       		= pipe(
              prompt=lowercase__					,	image=lowercase__					,	generator=lowercase__					,	output_type="""np"""					,	)
          snake_case__    :       int       		= output.images[0]

          assert image.shape == (512, 512, 3)
          assert np.abs(expected_image - image		).max() < 1E-3




    def lowerCamelCase			(       self   :	str		):
          snake_case__    :       List[Any]       		= load_image(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
              """/sd2-upscale/low_res_cat.png"""		)
          snake_case__    :       Optional[int]       		= load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
              """/upsampled_cat_fp16.npy"""		)

          snake_case__    :       Dict       		= """stabilityai/stable-diffusion-x4-upscaler"""
          snake_case__    :       int       		= StableDiffusionUpscalePipeline.from_pretrained(
              lowercase__					,	torch_dtype=torch.floataa					,	)
          pipe.to(lowercase__		)
          pipe.set_progress_bar_config(disable=lowercase__		)
          pipe.enable_attention_slicing()

          snake_case__    :       Optional[Any]       		= """a cat sitting on a park bench"""

          snake_case__    :       Tuple       		= torch.manual_seed(0		)
          snake_case__    :       int       		= pipe(
              prompt=lowercase__					,	image=lowercase__					,	generator=lowercase__					,	output_type="""np"""					,	)
          snake_case__    :       Optional[int]       		= output.images[0]

          assert image.shape == (512, 512, 3)
          assert np.abs(expected_image - image		).max() < 5E-1




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

          snake_case__    :       Union[str, Any]       		= load_image(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
              """/sd2-upscale/low_res_cat.png"""		)

          snake_case__    :       List[str]       		= """stabilityai/stable-diffusion-x4-upscaler"""
          snake_case__    :       Dict       		= StableDiffusionUpscalePipeline.from_pretrained(
              lowercase__					,	torch_dtype=torch.floataa					,	)
          pipe.to(lowercase__		)
          pipe.set_progress_bar_config(disable=lowercase__		)
          pipe.enable_attention_slicing(1		)
          pipe.enable_sequential_cpu_offload()

          snake_case__    :       List[Any]       		= """a cat sitting on a park bench"""

          snake_case__    :       Optional[int]       		= torch.manual_seed(0		)
          snake_case__    :       Dict       		= pipe(
              prompt=lowercase__					,	image=lowercase__					,	generator=lowercase__					,	num_inference_steps=5					,	output_type="""np"""					,	)

          snake_case__    :       int       		= torch.cuda.max_memory_allocated()
          # make sure that less than 2.9 GB is allocated
          assert mem_bytes < 2.9 * 10**9






'''simple docstring'''


import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
 import torch


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


lowerCAmelCase_      :  List[str]				     =  logging.get_logger(__name__)
lowerCAmelCase_      :  List[str]				     =  {'vocab_file': 'spiece.model'}

lowerCAmelCase_      :  Union[str, Any]				     =  {
    'vocab_file': {
        'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
    }
}

lowerCAmelCase_      :  str				     =  {
    'AI-Sweden/gpt-sw3-126m': 2048,
    'AI-Sweden/gpt-sw3-350m': 2048,
    'AI-Sweden/gpt-sw3-1.6b': 2048,
    'AI-Sweden/gpt-sw3-6.7b': 2048,
    'AI-Sweden/gpt-sw3-20b': 2048,
}







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       List[Any]   							=					VOCAB_FILES_NAMES
      __magic_name__  :       Dict   							=					PRETRAINED_VOCAB_FILES_MAP
      __magic_name__  :       Optional[int]   							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __magic_name__  :       List[str]   							=					['''input_ids''', '''attention_mask''']






      def __init__( self   :  Union[str, Any]						,						lowercase__   :  Optional[Any]						,						lowercase__   :  Optional[Any]=False						,						lowercase__   :  Any=False						,						lowercase__   :  Optional[Any]=False						,						lowercase__   :  Dict=None						,						lowercase__   :  Any=None						,						lowercase__   :  List[str]=None						,						lowercase__   :  int=None						,						lowercase__   :  Optional[Dict[str, Any]] = None						,						**lowercase__   :  List[str]						,						):


           '''simple docstring'''

           a_     :							Any     						=  {} if sp_model_kwargs is None else sp_model_kwargs

           a_     :							Optional[Any]     						=  kwargs.get("""name_or_path"""       )
           if name_or_path is None:
                logger.warning(
                    """name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
                    """ you are testing the model, this can safely be ignored"""       )
                a_     :							List[Any]     						=  """None"""

           # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
           a_     :							Union[str, Any]     						=  """<|endoftext|>""" if eos_token is None else eos_token
           a_     :							Dict     						=  """<unk>""" if unk_token is None else unk_token
           if "gpt-sw3-7b" in name_or_path:
                a_     :							Tuple     						=  unk_token if pad_token is None else pad_token
                a_     :							Any     						=  eos_token if bos_token is None else bos_token
           else:
                a_     :							Optional[int]     						=  """<pad>""" if pad_token is None else pad_token
                a_     :							str     						=  """<s>""" if bos_token is None else bos_token

           super().__init__(
               do_lower_case=lowercase__						,						remove_space=lowercase__						,						keep_accents=lowercase__						,						bos_token=lowercase__						,						eos_token=lowercase__						,						unk_token=lowercase__						,						pad_token=lowercase__						,						sp_model_kwargs=self.sp_model_kwargs						,						**lowercase__						,						)

           a_     :							Optional[int]     						=  do_lower_case
           a_     :							Any     						=  remove_space
           a_     :							List[str]     						=  keep_accents
           a_     :							int     						=  vocab_file

           a_     :							Any     						=  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(lowercase__       )

           # Used for whitespace normalization in input texts
           # fmt : off
           a_     :							Dict     						=  {""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
           # fmt : on

           # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
           a_     :							Tuple     						=  re.compile(
               F"[{''.join(map(lowercase__						,						list(range(0						,						9       )       ) + list(range(11						,						32       )       ) + list(range(127						,						160       )       ) + [160, 173, 8203]       )       )}]"       )






      def __getstate__( self   :  str       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  self.__dict__.copy()
           a_     :							Tuple     						=  None
           return state






      def __setstate__( self   :  Tuple						,						lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							List[str]     						=  d

           # for backward compatibility
           if not hasattr(self						,						"""sp_model_kwargs"""       ):
                a_     :							Union[str, Any]     						=  {}

           a_     :							int     						=  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(self.vocab_file       )






      @property
      # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           return len(self.sp_model       )






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


           '''simple docstring'''

           a_     :							Optional[int]     						=  self.non_printing_characters_re.sub(""""""						,						lowercase__       )

           # Normalize whitespaces
           a_     :							List[Any]     						=  """""".join([char if char not in self.whitespaces else """ """ for char in text]       )

           # NFC Unicode normalization
           a_     :							str     						=  unicodedata.normalize("""NFC"""						,						lowercase__       )
           return text






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  str						,						**lowercase__   :  Dict       ):


           '''simple docstring'''

           a_     :							Dict     						=  self.preprocess_text(lowercase__       )
           return self.sp_model.encode(lowercase__						,						out_type=lowercase__       )






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  str       ):


           '''simple docstring'''

           return self.sp_model.PieceToId(lowercase__       )






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  int       ):


           '''simple docstring'''

           return self.sp_model.IdToPiece(lowercase__       )






      @staticmethod
      def 							lowercase_						( lowercase__   :  str       ):


           '''simple docstring'''

           return out_string






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  List[str]       ):


           '''simple docstring'''

           a_     :							Dict     						=  []
           a_     :							int     						=  """"""
           a_     :							Union[str, Any]     						=  False
           for token in tokens:
                # make sure that special tokens are not decoded using sentencepiece model
                if token in self.all_special_tokens:
                     # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                     if not prev_is_special:
                          out_string += " "

                     out_string += self.sp_model.decode(lowercase__       ) + token
                     a_     :							int     						=  True
                     a_     :							Optional[int]     						=  []
                else:
                     current_sub_tokens.append(lowercase__       )
                     a_     :							List[str]     						=  False
           out_string += self.sp_model.decode(lowercase__       )

           return out_string






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  {self.convert_ids_to_tokens(lowercase__       ): i for i in range(self.vocab_size       )}
           vocab.update(self.added_tokens_encoder       )
           return vocab






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  str						,						lowercase__   :  Optional[str] = None       ):


           '''simple docstring'''

           if not os.path.isdir(lowercase__       ):
                logger.error(F"Vocabulary path ({save_directory}) should be a directory"       )
                return
           a_     :							Any     						=  os.path.join(
               lowercase__						,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

           if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowercase__       ) and os.path.isfile(self.vocab_file       ):
                copyfile(self.vocab_file						,						lowercase__       )
           elif not os.path.isfile(self.vocab_file       ):
                with open(lowercase__						,						"""wb"""       ) as fi:
                     a_     :							int     						=  self.sp_model.serialized_model_proto()
                     fi.write(lowercase__       )

           return (out_vocab_file,)






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  Union[str, List[str]]						,						lowercase__   :  Union[str, bool] = False       ):


           '''simple docstring'''

           if isinstance(lowercase__						,						lowercase__       ):
                a_     :							List[str]     						=  self.preprocess_text(lowercase__       )
                a_     :							str     						=  self.sp_model.encode(lowercase__       )
           else:
                a_     :							Optional[Any]     						=  [self.preprocess_text(lowercase__       ) for t in text]
                a_     :							Any     						=  self.sp_model.encode(lowercase__       )

           if return_tensors is True or return_tensors == "pt":
                a_     :							Tuple     						=  torch.tensor(lowercase__       )

           return token_ids






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  Union[int, List[int]]       ):


           '''simple docstring'''

           return self.sp_model.decode(lowercase__       )






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  "Conversation"       ):


           '''simple docstring'''

           a_     :							Dict     						=  [F"User: {text}" if is_user else F"Bot: {text}" for is_user, text in conversation.iter_texts()]
           a_     :							Any     						=  (
               F"{self.eos_token}{self.bos_token}" + F"{self.bos_token}".join(lowercase__       ) + F"{self.bos_token}Bot:"
           )
           return self.encode(text=lowercase__       )
