
'''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,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class  UpperCAmelCase__						(       _A						,	_A						,	_A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		StableDiffusionInstructPixaPixPipeline
    lowerCAmelCase_				    =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
    lowerCAmelCase_				    =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    lowerCAmelCase_				    =		IMAGE_TO_IMAGE_IMAGE_PARAMS
    lowerCAmelCase_				    =		IMAGE_TO_IMAGE_IMAGE_PARAMS



    def   lowerCamelCase_  (	self      :       Any     ):
          torch.manual_seed(0     )
          _lowerCamelCase				:   Optional[int]								=   UNetaDConditionModel(
              block_out_channels=(3_2, 6_4),layers_per_block=2,sample_size=3_2,in_channels=8,out_channels=4,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),cross_attention_dim=3_2,)
          _lowerCamelCase				:   Union[str, Any]								=   PNDMScheduler(skip_prk_steps=__A     )
          torch.manual_seed(0     )
          _lowerCamelCase				:   Optional[Any]								=   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,)
          torch.manual_seed(0     )
          _lowerCamelCase				:   Optional[int]								=   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,)
          _lowerCamelCase				:   Optional[Any]								=   CLIPTextModel(__A     )
          _lowerCamelCase				:   Optional[int]								=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

          _lowerCamelCase				:   Optional[Any]								=   {
              "unet": unet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }
          return components



    def   lowerCamelCase_  (	self      :       Dict,__A      :       int,__A      :       Any=0     ):
          _lowerCamelCase				:   Tuple								=   floats_tensor((1, 3, 3_2, 3_2),rng=random.Random(__A     )     ).to(__A     )
          _lowerCamelCase				:   Tuple								=   image.cpu().permute(0,2,3,1     )[0]
          _lowerCamelCase				:   Union[str, Any]								=   Image.fromarray(np.uinta(__A     )     ).convert("RGB"     )
          if str(__A     ).startswith("mps"     ):
                _lowerCamelCase				:   Dict								=   torch.manual_seed(__A     )
          else:
                _lowerCamelCase				:   List[Any]								=   torch.Generator(device=__A     ).manual_seed(__A     )
          _lowerCamelCase				:   Tuple								=   {
              "prompt": "A painting of a squirrel eating a burger",
              "image": image,
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 6.0,
              "image_guidance_scale": 1,
              "output_type": "numpy",
          }
          return inputs



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          _lowerCamelCase				:   List[Any]								=   "cpu"  # ensure determinism for the device-dependent torch.Generator
          _lowerCamelCase				:   Union[str, Any]								=   self.get_dummy_components()
          _lowerCamelCase				:   List[Any]								=   StableDiffusionInstructPixaPixPipeline(**__A     )
          _lowerCamelCase				:   int								=   sd_pipe.to(__A     )
          sd_pipe.set_progress_bar_config(disable=__A     )

          _lowerCamelCase				:   Dict								=   self.get_dummy_inputs(__A     )
          _lowerCamelCase				:   Optional[Any]								=   sd_pipe(**__A     ).images
          _lowerCamelCase				:   Optional[Any]								=   image[0, -3:, -3:, -1]
          assert image.shape == (1, 3_2, 3_2, 3)
          _lowerCamelCase				:   Union[str, Any]								=   np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]     )

          assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3



    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   Tuple								=   "cpu"  # ensure determinism for the device-dependent torch.Generator
          _lowerCamelCase				:   Any								=   self.get_dummy_components()
          _lowerCamelCase				:   List[Any]								=   StableDiffusionInstructPixaPixPipeline(**__A     )
          _lowerCamelCase				:   Optional[Any]								=   sd_pipe.to(__A     )
          sd_pipe.set_progress_bar_config(disable=__A     )

          _lowerCamelCase				:   Optional[int]								=   self.get_dummy_inputs(__A     )
          _lowerCamelCase				:   List[str]								=   "french fries"
          _lowerCamelCase				:   int								=   sd_pipe(**__A,negative_prompt=__A     )
          _lowerCamelCase				:   Optional[int]								=   output.images
          _lowerCamelCase				:   Any								=   image[0, -3:, -3:, -1]

          assert image.shape == (1, 3_2, 3_2, 3)
          _lowerCamelCase				:   Optional[int]								=   np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]     )

          assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3



    def   lowerCamelCase_  (	self      :       Any     ):
          _lowerCamelCase				:   Optional[Any]								=   "cpu"  # ensure determinism for the device-dependent torch.Generator
          _lowerCamelCase				:   List[Any]								=   self.get_dummy_components()
          _lowerCamelCase				:   Optional[int]								=   StableDiffusionInstructPixaPixPipeline(**__A     )
          _lowerCamelCase				:   List[Any]								=   sd_pipe.to(__A     )
          sd_pipe.set_progress_bar_config(disable=__A     )

          _lowerCamelCase				:   Tuple								=   self.get_dummy_inputs(__A     )
          _lowerCamelCase				:   int								=   [inputs["prompt"]] * 2

          _lowerCamelCase				:   Optional[Any]								=   np.array(inputs["image"]     ).astype(np.floataa     ) / 255.0
          _lowerCamelCase				:   Union[str, Any]								=   torch.from_numpy(__A     ).unsqueeze(0     ).to(__A     )
          _lowerCamelCase				:   Tuple								=   image / 2 + 0.5
          _lowerCamelCase				:   Tuple								=   image.permute(0,3,1,2     )
          _lowerCamelCase				:   Dict								=   image.repeat(2,1,1,1     )

          _lowerCamelCase				:   List[Any]								=   sd_pipe(**__A     ).images
          _lowerCamelCase				:   Tuple								=   image[-1, -3:, -3:, -1]

          assert image.shape == (2, 3_2, 3_2, 3)
          _lowerCamelCase				:   str								=   np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]     )

          assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   int								=   "cpu"  # ensure determinism for the device-dependent torch.Generator
          _lowerCamelCase				:   Dict								=   self.get_dummy_components()
          _lowerCamelCase				:   Any								=   EulerAncestralDiscreteScheduler(
              beta_start=0.00085,beta_end=0.012,beta_schedule="scaled_linear"     )
          _lowerCamelCase				:   Tuple								=   StableDiffusionInstructPixaPixPipeline(**__A     )
          _lowerCamelCase				:   str								=   sd_pipe.to(__A     )
          sd_pipe.set_progress_bar_config(disable=__A     )

          _lowerCamelCase				:   Dict								=   self.get_dummy_inputs(__A     )
          _lowerCamelCase				:   Dict								=   sd_pipe(**__A     ).images
          _lowerCamelCase				:   int								=   image[0, -3:, -3:, -1]

          _lowerCamelCase				:   Optional[int]								=   [round(__A,4     ) for x in image_slice.flatten().tolist()]
          print(",".join([str(__A     ) for x in slice]     )     )

          assert image.shape == (1, 3_2, 3_2, 3)
          _lowerCamelCase				:   str								=   np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]     )

          assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          super().test_inference_batch_single_identical(expected_max_diff=3e-3     )



    def   lowerCamelCase_  (	self      :       Dict     ):
          _lowerCamelCase				:   Optional[Any]								=   self.get_dummy_components()
          _lowerCamelCase				:   List[Any]								=   StableDiffusionInstructPixaPixPipeline(**__A     )
          _lowerCamelCase				:   List[Any]								=   VaeImageProcessor(do_resize=__A,do_normalize=__A     )
          _lowerCamelCase				:   Any								=   pipe.to(__A     )
          pipe.set_progress_bar_config(disable=__A     )

          _lowerCamelCase				:   Union[str, Any]								=   pipe(**self.get_dummy_inputs_by_type(__A,input_image_type="pt"     )     )[0]

          _lowerCamelCase				:   Dict								=   components["vae"]
          _lowerCamelCase				:   Optional[Any]								=   self.get_dummy_inputs_by_type(__A,input_image_type="pt"     )

          for image_param in self.image_latents_params:
                if image_param in inputs.keys():
                      _lowerCamelCase				:   List[str]								=   vae.encode(inputs[image_param]     ).latent_dist.mode()

          _lowerCamelCase				:   Dict								=   pipe(**__A     )[0]

          _lowerCamelCase				:   str								=   np.abs(out - out_latents_inputs     ).max()
          self.assertLess(__A,1e-4,"passing latents as image input generate different result from passing image"     )


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



    def   lowerCamelCase_  (	self      :       Dict     ):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



    def   lowerCamelCase_  (	self      :       int,__A      :       Union[str, Any]=0     ):
          _lowerCamelCase				:   List[Any]								=   torch.manual_seed(__A     )
          _lowerCamelCase				:   Union[str, Any]								=   load_image(
              "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"     )
          _lowerCamelCase				:   List[Any]								=   {
              "prompt": "turn him into a cyborg",
              "image": image,
              "generator": generator,
              "num_inference_steps": 3,
              "guidance_scale": 7.5,
              "image_guidance_scale": 1.0,
              "output_type": "numpy",
          }
          return inputs



    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   Optional[Any]								=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
              "timbrooks/instruct-pix2pix",safety_checker=__A     )
          pipe.to(__A     )
          pipe.set_progress_bar_config(disable=__A     )
          pipe.enable_attention_slicing()

          _lowerCamelCase				:   int								=   self.get_inputs()
          _lowerCamelCase				:   List[str]								=   pipe(**__A     ).images
          _lowerCamelCase				:   Dict								=   image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 5_1_2, 5_1_2, 3)
          _lowerCamelCase				:   Dict								=   np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]     )

          assert np.abs(expected_slice - image_slice     ).max() < 1e-3



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   List[str]								=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
              "timbrooks/instruct-pix2pix",safety_checker=__A     )
          _lowerCamelCase				:   List[str]								=   LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
          pipe.to(__A     )
          pipe.set_progress_bar_config(disable=__A     )
          pipe.enable_attention_slicing()

          _lowerCamelCase				:   Optional[Any]								=   self.get_inputs()
          _lowerCamelCase				:   Dict								=   pipe(**__A     ).images
          _lowerCamelCase				:   Any								=   image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 5_1_2, 5_1_2, 3)
          _lowerCamelCase				:   Optional[Any]								=   np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]     )

          assert np.abs(expected_slice - image_slice     ).max() < 1e-3



    def   lowerCamelCase_  (	self      :       Dict     ):
          _lowerCamelCase				:   str								=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
              "timbrooks/instruct-pix2pix",safety_checker=__A     )
          _lowerCamelCase				:   Any								=   DDIMScheduler.from_config(pipe.scheduler.config     )
          pipe.to(__A     )
          pipe.set_progress_bar_config(disable=__A     )
          pipe.enable_attention_slicing()

          _lowerCamelCase				:   Tuple								=   self.get_inputs()
          _lowerCamelCase				:   str								=   pipe(**__A     ).images
          _lowerCamelCase				:   Union[str, Any]								=   image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 5_1_2, 5_1_2, 3)
          _lowerCamelCase				:   List[Any]								=   np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]     )

          assert np.abs(expected_slice - image_slice     ).max() < 1e-3



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase				:   str								=   0

          def callback_fn(__A      :       List[Any],__A      :       Optional[Any],__A      :       Dict     ) -> None:
                _lowerCamelCase				:   Union[str, Any]								=   True
                nonlocal number_of_steps
                number_of_steps += 1
                if step == 1:
                      _lowerCamelCase				:   Optional[int]								=   latents.detach().cpu().numpy()
                      assert latents.shape == (1, 4, 6_4, 6_4)
                      _lowerCamelCase				:   Any								=   latents[0, -3:, -3:, -1]
                      _lowerCamelCase				:   Optional[Any]								=   np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]     )

                      assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5e-2
                elif step == 2:
                      _lowerCamelCase				:   Tuple								=   latents.detach().cpu().numpy()
                      assert latents.shape == (1, 4, 6_4, 6_4)
                      _lowerCamelCase				:   List[str]								=   latents[0, -3:, -3:, -1]
                      _lowerCamelCase				:   Tuple								=   np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]     )

                      assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5e-2

          _lowerCamelCase				:   Any								=   False

          _lowerCamelCase				:   Tuple								=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
              "timbrooks/instruct-pix2pix",safety_checker=__A,torch_dtype=torch.floataa     )
          _lowerCamelCase				:   Optional[Any]								=   pipe.to(__A     )
          pipe.set_progress_bar_config(disable=__A     )
          pipe.enable_attention_slicing()

          _lowerCamelCase				:   Dict								=   self.get_inputs()
          pipe(**__A,callback=__A,callback_steps=1     )
          assert callback_fn.has_been_called
          assert number_of_steps == 3



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

          _lowerCamelCase				:   int								=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
              "timbrooks/instruct-pix2pix",safety_checker=__A,torch_dtype=torch.floataa     )
          _lowerCamelCase				:   int								=   pipe.to(__A     )
          pipe.set_progress_bar_config(disable=__A     )
          pipe.enable_attention_slicing(1     )
          pipe.enable_sequential_cpu_offload()

          _lowerCamelCase				:   Any								=   self.get_inputs()
          _lowerCamelCase				:   int								=   pipe(**__A     )

          _lowerCamelCase				:   Dict								=   torch.cuda.max_memory_allocated()
          # make sure that less than 2.2 GB is allocated
          assert mem_bytes < 2.2 * 1_0**9



    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   Any								=   self.get_inputs()
          # resize to resolution that is divisible by 8 but not 16 or 32
          _lowerCamelCase				:   Optional[int]								=   inputs["image"].resize((5_0_4, 5_0_4)     )

          _lowerCamelCase				:   List[Any]								=   "timbrooks/instruct-pix2pix"
          _lowerCamelCase				:   List[str]								=   StableDiffusionInstructPixaPixPipeline.from_pretrained(
              __A,safety_checker=__A,)
          pipe.to(__A     )
          pipe.set_progress_bar_config(disable=__A     )
          pipe.enable_attention_slicing()

          _lowerCamelCase				:   Optional[int]								=   pipe(**__A     )
          _lowerCamelCase				:   Any								=   output.images[0]

          _lowerCamelCase				:   Union[str, Any]								=   image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

          assert image.shape == (5_0_4, 5_0_4, 3)
          _lowerCamelCase				:   Optional[int]								=   np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]     )

          assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5e-3

'''simple docstring'''






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

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

UpperCAmelCase_       :   Any   =       logging.get_logger(__name__)


UpperCAmelCase_       :   Any   =       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

UpperCAmelCase_       :   List[str]   =       {
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

UpperCAmelCase_       :   List[Any]   =       {'facebook/blenderbot-3B': 128}

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		VOCAB_FILES_NAMES
    lowerCAmelCase_				    =		PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase_				    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase_				    =		['input_ids', 'attention_mask']
    lowerCAmelCase_				    =		BlenderbotTokenizer



    def __init__(	self      :       Dict,__A      :       Optional[Any]=None,__A      :       List[str]=None,__A      :       Optional[Any]=None,__A      :       List[Any]="replace",__A      :       List[Any]="<s>",__A      :       str="</s>",__A      :       List[str]="</s>",__A      :       List[Any]="<s>",__A      :       Union[str, Any]="<unk>",__A      :       Optional[Any]="<pad>",__A      :       Dict="<mask>",__A      :       Any=False,__A      :       Tuple=True,**__A      :       Dict,):
          super().__init__(
              __A,__A,tokenizer_file=__A,errors=__A,bos_token=__A,eos_token=__A,sep_token=__A,cls_token=__A,unk_token=__A,pad_token=__A,mask_token=__A,add_prefix_space=__A,trim_offsets=__A,**__A,)

          _lowerCamelCase				:   Tuple								=   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
          if pre_tok_state.get("add_prefix_space",__A     ) != add_prefix_space:
                _lowerCamelCase				:   Optional[Any]								=   getattr(__A,pre_tok_state.pop("type"     )     )
                _lowerCamelCase				:   List[Any]								=   add_prefix_space
                _lowerCamelCase				:   Optional[Any]								=   pre_tok_class(**__A     )

          _lowerCamelCase				:   Any								=   add_prefix_space

          _lowerCamelCase				:   Any								=   "post_processor"
          _lowerCamelCase				:   Optional[Any]								=   getattr(self.backend_tokenizer,__A,__A     )
          if tokenizer_component_instance:
                _lowerCamelCase				:   Tuple								=   json.loads(tokenizer_component_instance.__getstate__()     )

                # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                if "sep" in state:
                      _lowerCamelCase				:   List[Any]								=   tuple(state["sep"]     )
                if "cls" in state:
                      _lowerCamelCase				:   Optional[int]								=   tuple(state["cls"]     )

                _lowerCamelCase				:   List[str]								=   False

                if state.get("add_prefix_space",__A     ) != add_prefix_space:
                      _lowerCamelCase				:   List[str]								=   add_prefix_space
                      _lowerCamelCase				:   int								=   True

                if state.get("trim_offsets",__A     ) != trim_offsets:
                      _lowerCamelCase				:   List[str]								=   trim_offsets
                      _lowerCamelCase				:   Dict								=   True

                if changes_to_apply:
                      _lowerCamelCase				:   Tuple								=   getattr(__A,state.pop("type"     )     )
                      _lowerCamelCase				:   Union[str, Any]								=   component_class(**__A     )
                      setattr(self.backend_tokenizer,__A,__A     )



    @property
    # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          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   lowerCamelCase_  (	self      :       Optional[Any],__A      :       List[Any]     ):
          _lowerCamelCase				:   Any								=   AddedToken(__A,lstrip=__A,rstrip=__A     ) if isinstance(__A,__A     ) else value
          _lowerCamelCase				:   List[str]								=   value



    def   lowerCamelCase_  (	self      :       Optional[int],*__A      :       Optional[Any],**__A      :       List[str]     ):
          _lowerCamelCase				:   int								=   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   lowerCamelCase_  (	self      :       str,*__A      :       Union[str, Any],**__A      :       List[Any]     ):
          _lowerCamelCase				:   Optional[int]								=   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   lowerCamelCase_  (	self      :       str,__A      :       str,__A      :       Optional[str] = None     ):
          _lowerCamelCase				:   Optional[int]								=   self._tokenizer.model.save(__A,name=__A     )
          return tuple(__A     )



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          _lowerCamelCase				:   int								=   [self.sep_token_id]
          _lowerCamelCase				:   str								=   [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 + sep + token_ids_a + sep     ) * [0]



    def   lowerCamelCase_  (	self      :       Dict,__A      :       List[int],__A      :       Optional[List[int]] = None     ):
          return token_ids_a + [self.eos_token_id]



    def   lowerCamelCase_  (	self      :       List[str],__A      :       "Conversation"     ):
          _lowerCamelCase				:   Dict								=   []
          for is_user, text in conversation.iter_texts():
                if is_user:
                      # We need to space prefix as it's being done within blenderbot
                      inputs.append(" " + text     )
                else:
                      # Generated responses should contain them already.
                      inputs.append(__A     )

          _lowerCamelCase				:   List[Any]								=   "  ".join(__A     )
          _lowerCamelCase				:   List[str]								=   self.encode(__A     )
          if len(__A     ) > self.model_max_length:
                _lowerCamelCase				:   Tuple								=   input_ids[-self.model_max_length :]
                logger.warning(f'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'     )
          return input_ids