
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

lowercase_											=	"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")

        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""


def     a__			(    snake_case   ,	snake_case   ,	snake_case=8    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					List[Any]			=						height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  __SCREAMING_SNAKE_CASE     :					List[str]			=						width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor


def     a__			(    snake_case   ,	snake_case=512   ,	snake_case=512    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					List[Any]			=						pil_image.resize((w, h)   ,	resample=Image.BICUBIC   ,	reducing_gap=1    )
  __SCREAMING_SNAKE_CASE     :					Dict			=						np.array(pil_image.convert('''RGB'''    )    )
  __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						arr.astype(np.floataa    ) / 127.5 - 1
  __SCREAMING_SNAKE_CASE     :					int			=						np.transpose(snake_case   ,	[2, 0, 1]    )
  __SCREAMING_SNAKE_CASE     :					Dict			=						torch.from_numpy(snake_case    ).unsqueeze(0    )
  return image





class   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     def __init__(							self    :    List[str]      ,		_A    :    UNetaDConditionModel      ,		_A    :    DDPMScheduler      ,		_A    :    VQModel      ,		):







       """simple docstring"""




       super().__init__()

       self.register_modules(
           unet=_A      ,		scheduler=_A      ,		movq=_A      ,		)
       __SCREAMING_SNAKE_CASE     :					int			=						2 ** (len(self.movq.config.block_out_channels				) - 1)



     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    Dict      ,		_A    :    int      ,		_A    :    Tuple				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						min(int(num_inference_steps * strength				)      ,		_A				)

       __SCREAMING_SNAKE_CASE     :					str			=						max(num_inference_steps - init_timestep      ,		0				)
       __SCREAMING_SNAKE_CASE     :					Dict			=						self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start



     def 				UpperCAmelCase__    (							self    :    Optional[Any]      ,		_A    :    Tuple      ,		_A    :    List[str]      ,		_A    :    List[str]      ,		_A    :    Optional[int]      ,		_A    :    Optional[int]      ,		_A    :    List[Any]      ,		_A    :    int=None				):







       """simple docstring"""




       if not isinstance(_A      ,		(torch.Tensor, PIL.Image.Image, list)				):
         raise ValueError(
             F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A				)}'''				)

       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						image.to(device=_A      ,		dtype=_A				)

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						batch_size * num_images_per_prompt

       if image.shape[1] == 4:
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						image

       else:
         if isinstance(_A      ,		_A				) and len(_A				) != batch_size:
           raise ValueError(
               F'''You have passed a list of generators of length {len(_A				)}, but requested an effective batch'''
               F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''				)

         elif isinstance(_A      ,		_A				):
           __SCREAMING_SNAKE_CASE     :					str			=						[
               self.movq.encode(image[i : i + 1]				).latent_dist.sample(generator[i]				) for i in range(_A				)
           ]
           __SCREAMING_SNAKE_CASE     :					int			=						torch.cat(_A      ,		dim=0				)
         else:
           __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.movq.encode(_A				).latent_dist.sample(_A				)

         __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self.movq.config.scaling_factor * init_latents

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						torch.cat([init_latents]      ,		dim=0				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						init_latents.shape
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						randn_tensor(_A      ,		generator=_A      ,		device=_A      ,		dtype=_A				)

       # get latents
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						self.scheduler.add_noise(_A      ,		_A      ,		_A				)

       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						init_latents

       return latents



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]      ,		_A    :    str=0				):







       """simple docstring"""




       if is_accelerate_available():
         from accelerate import cpu_offload
       else:
         raise ImportError('''Please install accelerate via `pip install accelerate`'''				)

       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						torch.device(F'''cuda:{gpu_id}'''				)

       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						[
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
           cpu_offload(_A      ,		_A				)



     def 				UpperCAmelCase__    (							self    :    Optional[int]      ,		_A    :    Optional[int]=0				):







       """simple docstring"""




       if is_accelerate_available() and is_accelerate_version('''>='''      ,		'''0.17.0.dev0'''				):
         from accelerate import cpu_offload_with_hook
       else:
         raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''				)

       __SCREAMING_SNAKE_CASE     :					int			=						torch.device(F'''cuda:{gpu_id}'''				)

       if self.device.type != "cpu":
         self.to('''cpu'''      ,		silence_dtype_warnings=_A				)
         torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						None
       for cpu_offloaded_model in [self.unet, self.movq]:
         __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						cpu_offload_with_hook(_A      ,		_A      ,		prev_module_hook=_A				)

       # We'll offload the last model manually.
       __SCREAMING_SNAKE_CASE     :					int			=						hook



     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       if not hasattr(self.unet      ,		'''_hf_hook'''				):
         return self.device
       for module in self.unet.modules():
         if (
             hasattr(_A      ,		'''_hf_hook'''				)
             and hasattr(module._hf_hook      ,		'''execution_device'''				)
             and module._hf_hook.execution_device is not None
         ):
           return torch.device(module._hf_hook.execution_device				)
       return self.device




     @torch.no_grad()
     @replace_example_docstring(_A				)
     def __call__(							self    :    List[str]      ,		_A    :    Union[torch.FloatTensor, List[torch.FloatTensor]]      ,		_A    :    Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]      ,		_A    :    Union[torch.FloatTensor, List[torch.FloatTensor]]      ,		_A    :    torch.FloatTensor      ,		_A    :    int = 512      ,		_A    :    int = 512      ,		_A    :    int = 100      ,		_A    :    float = 4.0      ,		_A    :    float = 0.3      ,		_A    :    int = 1      ,		_A    :    Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,		_A    :    Optional[str] = "pil"      ,		_A    :    bool = True      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						self._execution_device

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						guidance_scale > 1.0

       if isinstance(_A      ,		_A				):
         __SCREAMING_SNAKE_CASE     :					Any			=						torch.cat(_A      ,		dim=0				)
       if isinstance(_A      ,		_A				):
         __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						torch.cat(_A      ,		dim=0				)
       if isinstance(_A      ,		_A				):
         __SCREAMING_SNAKE_CASE     :					str			=						torch.cat(_A      ,		dim=0				)

       __SCREAMING_SNAKE_CASE     :					str			=						image_embeds.shape[0]

       if do_classifier_free_guidance:
         __SCREAMING_SNAKE_CASE     :					Tuple			=						image_embeds.repeat_interleave(_A      ,		dim=0				)
         __SCREAMING_SNAKE_CASE     :					int			=						negative_image_embeds.repeat_interleave(_A      ,		dim=0				)
         __SCREAMING_SNAKE_CASE     :					int			=						hint.repeat_interleave(_A      ,		dim=0				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						torch.cat([negative_image_embeds, image_embeds]      ,		dim=0				).to(dtype=self.unet.dtype      ,		device=_A				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						torch.cat([hint, hint]      ,		dim=0				).to(dtype=self.unet.dtype      ,		device=_A				)

       if not isinstance(_A      ,		_A				):
         __SCREAMING_SNAKE_CASE     :					str			=						[image]
       if not all(isinstance(_A      ,		(PIL.Image.Image, torch.Tensor)				) for i in image				):
         raise ValueError(
             F'''Input is in incorrect format: {[type(_A				) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''				)

       __SCREAMING_SNAKE_CASE     :					Any			=						torch.cat([prepare_image(_A      ,		_A      ,		_A				) for i in image]      ,		dim=0				)
       __SCREAMING_SNAKE_CASE     :					Dict			=						image.to(dtype=image_embeds.dtype      ,		device=_A				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.movq.encode(_A				)['''latents''']
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						latents.repeat_interleave(_A      ,		dim=0				)
       self.scheduler.set_timesteps(_A      ,		device=_A				)
       __SCREAMING_SNAKE_CASE     :					Tuple			=						self.get_timesteps(_A      ,		_A      ,		_A				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						timesteps[:1].repeat(batch_size * num_images_per_prompt				)
       __SCREAMING_SNAKE_CASE     :					str			=						downscale_height_and_width(_A      ,		_A      ,		self.movq_scale_factor				)
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.prepare_latents(
           _A      ,		_A      ,		_A      ,		_A      ,		image_embeds.dtype      ,		_A      ,		_A				)
       for i, t in enumerate(self.progress_bar(_A				)				):
         # expand the latents if we are doing classifier free guidance
         __SCREAMING_SNAKE_CASE     :					Dict			=						torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

         __SCREAMING_SNAKE_CASE     :					List[str]			=						{'''image_embeds''': image_embeds, '''hint''': hint}
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.unet(
             sample=_A      ,		timestep=_A      ,		encoder_hidden_states=_A      ,		added_cond_kwargs=_A      ,		return_dict=_A      ,		)[0]

         if do_classifier_free_guidance:
           __SCREAMING_SNAKE_CASE     :					List[str]			=						noise_pred.split(latents.shape[1]      ,		dim=1				)
           __SCREAMING_SNAKE_CASE     :					List[str]			=						noise_pred.chunk(2				)
           __SCREAMING_SNAKE_CASE     :					Dict			=						variance_pred.chunk(2				)
           __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
           __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						torch.cat([noise_pred, variance_pred_text]      ,		dim=1				)

         if not (
             hasattr(self.scheduler.config      ,		'''variance_type'''				)
             and self.scheduler.config.variance_type in ["learned", "learned_range"]
         ):
           __SCREAMING_SNAKE_CASE     :					Any			=						noise_pred.split(latents.shape[1]      ,		dim=1				)

         # compute the previous noisy sample x_t -> x_t-1

         __SCREAMING_SNAKE_CASE     :					int			=						self.scheduler.step(
             _A      ,		_A      ,		_A      ,		generator=_A      ,		)[0]

       # post-processing
       __SCREAMING_SNAKE_CASE     :					Dict			=						self.movq.decode(_A      ,		force_not_quantize=_A				)['''sample''']

       if output_type not in ["pt", "np", "pil"]:
         raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''				)

       if output_type in ["np", "pil"]:
         __SCREAMING_SNAKE_CASE     :					Dict			=						image * 0.5 + 0.5
         __SCREAMING_SNAKE_CASE     :					int			=						image.clamp(0      ,		1				)
         __SCREAMING_SNAKE_CASE     :					Optional[int]			=						image.cpu().permute(0      ,		2      ,		3      ,		1				).float().numpy()

       if output_type == "pil":
         __SCREAMING_SNAKE_CASE     :					str			=						self.numpy_to_pil(_A				)

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=_A				)


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


lowercase_											=	logging.get_logger(__name__)

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

lowercase_											=	{
    """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"""
        ),
    },
}

lowercase_											=	{
    """microsoft/deberta-base""": 512,
    """microsoft/deberta-large""": 512,
    """microsoft/deberta-xlarge""": 512,
    """microsoft/deberta-base-mnli""": 512,
    """microsoft/deberta-large-mnli""": 512,
    """microsoft/deberta-xlarge-mnli""": 512,
}

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





class   __UpperCamelCase				(    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    :    List[str]      ,		_A    :    Tuple=None      ,		_A    :    List[Any]=None      ,		_A    :    Optional[int]=None      ,		_A    :    List[str]="replace"      ,		_A    :    List[Any]="[CLS]"      ,		_A    :    Optional[Any]="[SEP]"      ,		_A    :    Union[str, Any]="[SEP]"      ,		_A    :    List[str]="[CLS]"      ,		_A    :    Optional[Any]="[UNK]"      ,		_A    :    str="[PAD]"      ,		_A    :    List[str]="[MASK]"      ,		_A    :    Dict=False      ,		**_A    :    Optional[int]      ,		):







       """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      ,		)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						kwargs.pop('''add_bos_token'''      ,		_A				)

       __SCREAMING_SNAKE_CASE     :					int			=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
       if pre_tok_state.get('''add_prefix_space'''      ,		_A				) != add_prefix_space:
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						getattr(_A      ,		pre_tok_state.pop('''type'''				)				)
         __SCREAMING_SNAKE_CASE     :					Dict			=						add_prefix_space
         __SCREAMING_SNAKE_CASE     :					Any			=						pre_tok_class(**_A				)

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						add_prefix_space



     @property
     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """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 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    Union[str, Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						AddedToken(_A      ,		lstrip=_A      ,		rstrip=_A				) if isinstance(_A      ,		_A				) else value
       __SCREAMING_SNAKE_CASE     :					Tuple			=						value



     def 				UpperCAmelCase__    (							self    :    List[Any]      ,		_A    :    List[int]      ,		_A    :    Optional[List[int]] = None				):







       """simple docstring"""




       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       __SCREAMING_SNAKE_CASE     :					List[str]			=						[self.cls_token_id]
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						[self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep



     def 				UpperCAmelCase__    (							self    :    int      ,		_A    :    List[int]      ,		_A    :    Optional[List[int]] = None				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Tuple			=						[self.sep_token_id]
       __SCREAMING_SNAKE_CASE     :					List[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				) * [0] + len(token_ids_a + sep				) * [1]



     def 				UpperCAmelCase__    (							self    :    Tuple      ,		*_A    :    Optional[Any]      ,		**_A    :    Optional[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						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 				UpperCAmelCase__    (							self    :    Tuple      ,		*_A    :    Tuple      ,		**_A    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Dict			=						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 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    str      ,		_A    :    Optional[str] = None				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						self._tokenizer.model.save(_A      ,		name=_A				)
       return tuple(_A				)




     def 				UpperCAmelCase__    (							self    :    Tuple      ,		_A    :    "Conversation"				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						[]
       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:
         __SCREAMING_SNAKE_CASE     :					Any			=						input_ids[-self.model_max_length :]
       return input_ids
