





'''simple docstring'''


import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class    lowerCAmelCase_		(		__magic_name__						,__magic_name__						,unittest.TestCase		):
 __lowerCamelCase						:							List[Any]         =					StableDiffusionDiffEditPipeline
 __lowerCamelCase						:							List[str]         =					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
 __lowerCamelCase						:							Optional[int]         =					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
 __lowerCamelCase						:							Tuple         =					frozenset(
     []		)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 __lowerCamelCase						:							Dict         =					frozenset([]		)




 def 							_snake_case   (	self		)    ->							int:
     torch.manual_seed(0		)
     _lowerCAmelCase				=      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			,     attention_head_dim=(2, 4)			,     use_linear_projection=_lowerCAmelCase			,     )
     _lowerCAmelCase				=      DDIMScheduler(
         beta_start=0.00085			,     beta_end=0.012			,     beta_schedule="scaled_linear"			,     clip_sample=_lowerCAmelCase			,     set_alpha_to_one=_lowerCAmelCase			,     )
     _lowerCAmelCase				=      DDIMInverseScheduler(
         beta_start=0.00085			,     beta_end=0.012			,     beta_schedule="scaled_linear"			,     clip_sample=_lowerCAmelCase			,     set_alpha_to_zero=_lowerCAmelCase			,     )
     torch.manual_seed(0		)
     _lowerCAmelCase				=      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			,     sample_size=128			,     )
     torch.manual_seed(0		)
     _lowerCAmelCase				=      CLIPTextConfig(
         bos_token_id=0			,     eos_token_id=2			,     hidden_size=32			,     intermediate_size=37			,     layer_norm_eps=1E-05			,     num_attention_heads=4			,     num_hidden_layers=5			,     pad_token_id=1			,     vocab_size=1000			,     hidden_act="gelu"			,     projection_dim=512			,     )
     _lowerCAmelCase				=      CLIPTextModel(_lowerCAmelCase		)
     _lowerCAmelCase				=      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

     _lowerCAmelCase				=      {
         "unet": unet,
         "scheduler": scheduler,
         "inverse_scheduler": inverse_scheduler,
         "vae": vae,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "safety_checker": None,
         "feature_extractor": None,
     }

     return components




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase=0		)    ->							str:
     _lowerCAmelCase				=      floats_tensor((1, 16, 16)			,     rng=random.Random(_lowerCAmelCase		)		).to(_lowerCAmelCase		)
     _lowerCAmelCase				=      floats_tensor((1, 2, 4, 16, 16)			,     rng=random.Random(_lowerCAmelCase		)		).to(_lowerCAmelCase		)
     if str(_lowerCAmelCase		).startswith("mps"		):
         _lowerCAmelCase				=      torch.manual_seed(_lowerCAmelCase		)
     else:
         _lowerCAmelCase				=      torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)
     _lowerCAmelCase				=      {
         "prompt": "a dog and a newt",
         "mask_image": mask,
         "image_latents": latents,
         "generator": generator,
         "num_inference_steps": 2,
         "inpaint_strength": 1.0,
         "guidance_scale": 6.0,
         "output_type": "numpy",
     }

     return inputs




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase=0		)    ->							List[str]:
     _lowerCAmelCase				=      floats_tensor((1, 3, 32, 32)			,     rng=random.Random(_lowerCAmelCase		)		).to(_lowerCAmelCase		)
     _lowerCAmelCase				=      image.cpu().permute(0			,     2			,     3			,     1		)[0]
     _lowerCAmelCase				=      Image.fromarray(np.uinta(_lowerCAmelCase		)		).convert("RGB"		)
     if str(_lowerCAmelCase		).startswith("mps"		):
         _lowerCAmelCase				=      torch.manual_seed(_lowerCAmelCase		)
     else:
         _lowerCAmelCase				=      torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)
     _lowerCAmelCase				=      {
         "image": image,
         "source_prompt": "a cat and a frog",
         "target_prompt": "a dog and a newt",
         "generator": generator,
         "num_inference_steps": 2,
         "num_maps_per_mask": 2,
         "mask_encode_strength": 1.0,
         "guidance_scale": 6.0,
         "output_type": "numpy",
     }

     return inputs




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase=0		)    ->							Tuple:
     _lowerCAmelCase				=      floats_tensor((1, 3, 32, 32)			,     rng=random.Random(_lowerCAmelCase		)		).to(_lowerCAmelCase		)
     _lowerCAmelCase				=      image.cpu().permute(0			,     2			,     3			,     1		)[0]
     _lowerCAmelCase				=      Image.fromarray(np.uinta(_lowerCAmelCase		)		).convert("RGB"		)
     if str(_lowerCAmelCase		).startswith("mps"		):
         _lowerCAmelCase				=      torch.manual_seed(_lowerCAmelCase		)
     else:
         _lowerCAmelCase				=      torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)
     _lowerCAmelCase				=      {
         "image": image,
         "prompt": "a cat and a frog",
         "generator": generator,
         "num_inference_steps": 2,
         "inpaint_strength": 1.0,
         "guidance_scale": 6.0,
         "decode_latents": True,
         "output_type": "numpy",
     }
     return inputs




 def 							_snake_case   (	self		)    ->							int:
     if not hasattr(self.pipeline_class			,     "_optional_components"		):
         return

     _lowerCAmelCase				=      self.get_dummy_components()
     _lowerCAmelCase				=      self.pipeline_class(**_lowerCAmelCase		)
     pipe.to(_lowerCAmelCase		)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

     # set all optional components to None and update pipeline config accordingly
     for optional_component in pipe._optional_components:
         setattr(_lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)
     pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}		)

     _lowerCAmelCase				=      self.get_dummy_inputs(_lowerCAmelCase		)
     _lowerCAmelCase				=      pipe(**_lowerCAmelCase		)[0]

     with tempfile.TemporaryDirectory() as tmpdir:
         pipe.save_pretrained(_lowerCAmelCase		)
         _lowerCAmelCase				=      self.pipeline_class.from_pretrained(_lowerCAmelCase		)
         pipe_loaded.to(_lowerCAmelCase		)
         pipe_loaded.set_progress_bar_config(disable=_lowerCAmelCase		)

     for optional_component in pipe._optional_components:
         self.assertTrue(
             getattr(_lowerCAmelCase			,     _lowerCAmelCase		) is None			,     f'''`{optional_component}` did not stay set to None after loading.'''			,     )

     _lowerCAmelCase				=      self.get_dummy_inputs(_lowerCAmelCase		)
     _lowerCAmelCase				=      pipe_loaded(**_lowerCAmelCase		)[0]

     _lowerCAmelCase				=      np.abs(output - output_loaded		).max()
     self.assertLess(_lowerCAmelCase			,     1E-4		)




 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase				=      "cpu"

     _lowerCAmelCase				=      self.get_dummy_components()
     _lowerCAmelCase				=      self.pipeline_class(**_lowerCAmelCase		)
     pipe.to(_lowerCAmelCase		)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

     _lowerCAmelCase				=      self.get_dummy_mask_inputs(_lowerCAmelCase		)
     _lowerCAmelCase				=      pipe.generate_mask(**_lowerCAmelCase		)
     _lowerCAmelCase				=      mask[0, -3:, -3:]

     self.assertEqual(mask.shape			,     (1, 16, 16)		)
     _lowerCAmelCase				=      np.array([0] * 9		)
     _lowerCAmelCase				=      np.abs(mask_slice.flatten() - expected_slice		).max()
     self.assertLessEqual(_lowerCAmelCase			,     1E-3		)
     self.assertEqual(mask[0, -3, -4]			,     0		)




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     _lowerCAmelCase				=      "cpu"

     _lowerCAmelCase				=      self.get_dummy_components()
     _lowerCAmelCase				=      self.pipeline_class(**_lowerCAmelCase		)
     pipe.to(_lowerCAmelCase		)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

     _lowerCAmelCase				=      self.get_dummy_inversion_inputs(_lowerCAmelCase		)
     _lowerCAmelCase				=      pipe.invert(**_lowerCAmelCase		).images
     _lowerCAmelCase				=      image[0, -1, -3:, -3:]

     self.assertEqual(image.shape			,     (2, 32, 32, 3)		)
     _lowerCAmelCase				=      np.array(
         [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]			,     )
     _lowerCAmelCase				=      np.abs(image_slice.flatten() - expected_slice		).max()
     self.assertLessEqual(_lowerCAmelCase			,     1E-3		)




 def 							_snake_case   (	self		)    ->							Any:
     super().test_inference_batch_single_identical(expected_max_diff=5E-3		)





 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      "cpu"

     _lowerCAmelCase				=      self.get_dummy_components()

     _lowerCAmelCase				=      {"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
     _lowerCAmelCase				=      DPMSolverMultistepScheduler(**_lowerCAmelCase		)
     _lowerCAmelCase				=      DPMSolverMultistepInverseScheduler(**_lowerCAmelCase		)

     _lowerCAmelCase				=      self.pipeline_class(**_lowerCAmelCase		)
     pipe.to(_lowerCAmelCase		)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

     _lowerCAmelCase				=      self.get_dummy_inversion_inputs(_lowerCAmelCase		)
     _lowerCAmelCase				=      pipe.invert(**_lowerCAmelCase		).images
     _lowerCAmelCase				=      image[0, -1, -3:, -3:]

     self.assertEqual(image.shape			,     (2, 32, 32, 3)		)
     _lowerCAmelCase				=      np.array(
         [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]			,     )
     _lowerCAmelCase				=      np.abs(image_slice.flatten() - expected_slice		).max()
     self.assertLessEqual(_lowerCAmelCase			,     1E-3		)







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




 def 							_snake_case   (	self		)    ->							Tuple:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




 @classmethod
 def 							_snake_case   (	cls		)    ->							Tuple:
     _lowerCAmelCase				=      load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"		)

     _lowerCAmelCase				=      raw_image.convert("RGB"		).resize((768, 768)		)

     _lowerCAmelCase				=      raw_image




 def 							_snake_case   (	self		)    ->							str:
     _lowerCAmelCase				=      torch.manual_seed(0		)

     _lowerCAmelCase				=      StableDiffusionDiffEditPipeline.from_pretrained(
         "stabilityai/stable-diffusion-2-1"			,     safety_checker=_lowerCAmelCase			,     torch_dtype=torch.floataa		)
     _lowerCAmelCase				=      DDIMScheduler.from_config(pipe.scheduler.config		)
     _lowerCAmelCase				=      DDIMInverseScheduler.from_config(pipe.scheduler.config		)
     pipe.enable_model_cpu_offload()
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

     _lowerCAmelCase				=      "a bowl of fruit"
     _lowerCAmelCase				=      "a bowl of pears"

     _lowerCAmelCase				=      pipe.generate_mask(
         image=self.raw_image			,     source_prompt=_lowerCAmelCase			,     target_prompt=_lowerCAmelCase			,     generator=_lowerCAmelCase			,     )

     _lowerCAmelCase				=      pipe.invert(
         prompt=_lowerCAmelCase			,     image=self.raw_image			,     inpaint_strength=0.7			,     generator=_lowerCAmelCase		).latents

     _lowerCAmelCase				=      pipe(
         prompt=_lowerCAmelCase			,     mask_image=_lowerCAmelCase			,     image_latents=_lowerCAmelCase			,     generator=_lowerCAmelCase			,     negative_prompt=_lowerCAmelCase			,     inpaint_strength=0.7			,     output_type="numpy"			,     ).images[0]

     _lowerCAmelCase				=      (
         np.array(
             load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/diffedit/pears.png"		).resize((768, 768)		)		)
         / 255
     )
     assert np.abs((expected_image - image).max()		) < 5E-1





 def 							_snake_case   (	self		)    ->							Optional[Any]:
     _lowerCAmelCase				=      torch.manual_seed(0		)

     _lowerCAmelCase				=      StableDiffusionDiffEditPipeline.from_pretrained(
         "stabilityai/stable-diffusion-2-1"			,     safety_checker=_lowerCAmelCase			,     torch_dtype=torch.floataa		)
     _lowerCAmelCase				=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
     _lowerCAmelCase				=      DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config		)
     pipe.enable_model_cpu_offload()
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

     _lowerCAmelCase				=      "a bowl of fruit"
     _lowerCAmelCase				=      "a bowl of pears"

     _lowerCAmelCase				=      pipe.generate_mask(
         image=self.raw_image			,     source_prompt=_lowerCAmelCase			,     target_prompt=_lowerCAmelCase			,     generator=_lowerCAmelCase			,     )

     _lowerCAmelCase				=      pipe.invert(
         prompt=_lowerCAmelCase			,     image=self.raw_image			,     inpaint_strength=0.7			,     generator=_lowerCAmelCase			,     num_inference_steps=25			,     ).latents

     _lowerCAmelCase				=      pipe(
         prompt=_lowerCAmelCase			,     mask_image=_lowerCAmelCase			,     image_latents=_lowerCAmelCase			,     generator=_lowerCAmelCase			,     negative_prompt=_lowerCAmelCase			,     inpaint_strength=0.7			,     num_inference_steps=25			,     output_type="numpy"			,     ).images[0]

     _lowerCAmelCase				=      (
         np.array(
             load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/diffedit/pears.png"		).resize((768, 768)		)		)
         / 255
     )
     assert np.abs((expected_image - image).max()		) < 5E-1









'''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


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE	 =							{"vocab_file": "spiece.model"}

_SCREAMING_SNAKE_CASE	 =							{
    "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",
    }
}

_SCREAMING_SNAKE_CASE	 =							{
    "AI-Sweden/gpt-sw3-126m": 20_48,
    "AI-Sweden/gpt-sw3-350m": 20_48,
    "AI-Sweden/gpt-sw3-1.6b": 20_48,
    "AI-Sweden/gpt-sw3-6.7b": 20_48,
    "AI-Sweden/gpt-sw3-20b": 20_48,
}

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					VOCAB_FILES_NAMES
 __lowerCamelCase						:							List[Any]         =					PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase						:							Tuple         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase						:							List[str]         =					["input_ids", "attention_mask"]




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase=False			,     _lowerCAmelCase=False			,     _lowerCAmelCase=False			,     _lowerCAmelCase=None			,     _lowerCAmelCase=None			,     _lowerCAmelCase=None			,     _lowerCAmelCase=None			,     _lowerCAmelCase = None			,     **_lowerCAmelCase			,     )    ->							None:
     _lowerCAmelCase				=      {} if sp_model_kwargs is None else sp_model_kwargs

     _lowerCAmelCase				=      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"		)
         _lowerCAmelCase				=      "None"

     # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
     _lowerCAmelCase				=      "<|endoftext|>" if eos_token is None else eos_token
     _lowerCAmelCase				=      "<unk>" if unk_token is None else unk_token
     if "gpt-sw3-7b" in name_or_path:
         _lowerCAmelCase				=      unk_token if pad_token is None else pad_token
         _lowerCAmelCase				=      eos_token if bos_token is None else bos_token
     else:
         _lowerCAmelCase				=      "<pad>" if pad_token is None else pad_token
         _lowerCAmelCase				=      "<s>" if bos_token is None else bos_token

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

     _lowerCAmelCase				=      do_lower_case
     _lowerCAmelCase				=      remove_space
     _lowerCAmelCase				=      keep_accents
     _lowerCAmelCase				=      vocab_file

     _lowerCAmelCase				=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
     self.sp_model.Load(_lowerCAmelCase		)

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

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




 def __getstate__(	self		)    ->							Any:
     _lowerCAmelCase				=      self.__dict__.copy()
     _lowerCAmelCase				=      None
     return state




 def __setstate__(	self			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      d

     # for backward compatibility
     if not hasattr(self			,     "sp_model_kwargs"		):
         _lowerCAmelCase				=      {}

     _lowerCAmelCase				=      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 							_snake_case   (	self		)    ->							int:
     return len(self.sp_model		)




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      self.non_printing_characters_re.sub(""			,     _lowerCAmelCase		)

     # Normalize whitespaces
     _lowerCAmelCase				=      "".join([char if char not in self.whitespaces else " " for char in text]		)

     # NFC Unicode normalization
     _lowerCAmelCase				=      unicodedata.normalize("NFC"			,     _lowerCAmelCase		)
     return text




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     **_lowerCAmelCase		)    ->							List[str]:
     _lowerCAmelCase				=      self.preprocess_text(_lowerCAmelCase		)
     return self.sp_model.encode(_lowerCAmelCase			,     out_type=_lowerCAmelCase		)




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							int:
     return self.sp_model.PieceToId(_lowerCAmelCase		)




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							str:
     return self.sp_model.IdToPiece(_lowerCAmelCase		)




 @staticmethod
 def 							_snake_case   (	_lowerCAmelCase		)    ->							str:
     return out_string




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      []
     _lowerCAmelCase				=      ""
     _lowerCAmelCase				=      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(_lowerCAmelCase		) + token
             _lowerCAmelCase				=      True
             _lowerCAmelCase				=      []
         else:
             current_sub_tokens.append(_lowerCAmelCase		)
             _lowerCAmelCase				=      False
     out_string += self.sp_model.decode(_lowerCAmelCase		)

     return out_string




 def 							_snake_case   (	self		)    ->							Dict[str, int]:
     _lowerCAmelCase				=      {self.convert_ids_to_tokens(_lowerCAmelCase		): i for i in range(self.vocab_size		)}
     vocab.update(self.added_tokens_encoder		)
     return vocab




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = None		)    ->							Tuple[str]:
     if not os.path.isdir(_lowerCAmelCase		):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
         return
     _lowerCAmelCase				=      os.path.join(
         _lowerCAmelCase			,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

     return (out_vocab_file,)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = False		)    ->							Union[List[int], List[List[int]], "torch.Tensor"]:

     if isinstance(_lowerCAmelCase			,     _lowerCAmelCase		):
         _lowerCAmelCase				=      self.preprocess_text(_lowerCAmelCase		)
         _lowerCAmelCase				=      self.sp_model.encode(_lowerCAmelCase		)
     else:
         _lowerCAmelCase				=      [self.preprocess_text(_lowerCAmelCase		) for t in text]
         _lowerCAmelCase				=      self.sp_model.encode(_lowerCAmelCase		)

     if return_tensors is True or return_tensors == "pt":
         _lowerCAmelCase				=      torch.tensor(_lowerCAmelCase		)

     return token_ids




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							str:
     return self.sp_model.decode(_lowerCAmelCase		)





 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							List[int]:
     _lowerCAmelCase				=      [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
     _lowerCAmelCase				=      (
         f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(_lowerCAmelCase		) + f'''{self.bos_token}Bot:'''
     )
     return self.encode(text=_lowerCAmelCase		)


