


'''simple docstring'''


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


_UpperCamelCase          =     logging.get_logger(__name__)

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

_UpperCamelCase          =     {
    """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"""
        ),
    },
}

_UpperCamelCase          =     {
    """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,
}

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







class        __magic_name__  (					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 ,    lowerCamelCase=None ,    lowerCamelCase=None ,    lowerCamelCase=None ,    lowerCamelCase="replace" ,    lowerCamelCase="[CLS]" ,    lowerCamelCase="[SEP]" ,    lowerCamelCase="[SEP]" ,    lowerCamelCase="[CLS]" ,    lowerCamelCase="[UNK]" ,    lowerCamelCase="[PAD]" ,    lowerCamelCase="[MASK]" ,    lowerCamelCase=False ,    **lowerCamelCase ,    ):


          '''simple docstring'''





          super().__init__(
              lowerCamelCase ,    lowerCamelCase ,    tokenizer_file=lowerCamelCase ,    errors=lowerCamelCase ,    bos_token=lowerCamelCase ,    eos_token=lowerCamelCase ,    unk_token=lowerCamelCase ,    sep_token=lowerCamelCase ,    cls_token=lowerCamelCase ,    pad_token=lowerCamelCase ,    mask_token=lowerCamelCase ,    add_prefix_space=lowerCamelCase ,    **lowerCamelCase ,    )
          __A  :					Union[str, Any]						     =       kwargs.pop("add_bos_token" ,    lowerCamelCase		)

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

          __A  :					Optional[Any]						     =       add_prefix_space


     @property
     def lowerCAmelCase__     (      self		):


          '''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 lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Tuple						     =       AddedToken(lowerCamelCase ,    lstrip=lowerCamelCase ,    rstrip=lowerCamelCase		) if isinstance(lowerCamelCase ,    lowerCamelCase		) else value
          __A  :					Dict						     =       value


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None		):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None		):


          '''simple docstring'''





          __A  :					str						     =       [self.sep_token_id]
          __A  :					Dict						     =       [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 lowerCAmelCase__     (      self ,    *lowerCamelCase ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					int						     =       kwargs.get("is_split_into_words" ,    lowerCamelCase		)
          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(*lowerCamelCase ,    **lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    *lowerCamelCase ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					Optional[int]						     =       kwargs.get("is_split_into_words" ,    lowerCamelCase		)

          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(*lowerCamelCase ,    **lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None		):


          '''simple docstring'''





          __A  :					Dict						     =       self._tokenizer.model.save(lowerCamelCase ,    name=lowerCamelCase		)
          return tuple(lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       []
          for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(lowerCamelCase ,    add_special_tokens=lowerCamelCase		) + [self.eos_token_id]		)

          if len(lowerCamelCase		) > self.model_max_length:
               __A  :					List[str]						     =       input_ids[-self.model_max_length :]
          return input_ids





'''simple docstring'''


from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     def __init__(      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    ):


          '''simple docstring'''





          super().__init__()

          self.register_modules(vae=lowerCamelCase ,    text_encoder=lowerCamelCase ,    tokenizer=lowerCamelCase ,    unet=lowerCamelCase ,    scheduler=lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          with torch.no_grad():
               __A  :					Optional[Any]						     =       self.vae.encode(tfms.ToTensor()(lowerCamelCase		).unsqueeze(0		).to(self.device		) * 2 - 1		)
               __A  :					int						     =       0.1_8215 * latent.latent_dist.sample()
          return latent


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Optional[int]						     =       (1 / 0.1_8215) * latent
          with torch.no_grad():
               __A  :					List[Any]						     =       self.vae.decode(lowerCamelCase		).sample
          __A  :					int						     =       (img / 2 + 0.5).clamp(0 ,    1		)
          __A  :					List[str]						     =       img.detach().cpu().permute(0 ,    2 ,    3 ,    1		).numpy()
          __A  :					List[str]						     =       (img * 255).round().astype("uint8"		)
          return Image.fromarray(img[0]		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					str						     =       self.tokenizer(
              lowerCamelCase ,    padding="max_length" ,    max_length=self.tokenizer.model_max_length ,    truncation=lowerCamelCase ,    return_tensors="pt" ,    )

          __A  :					str						     =       self.text_encoder(text_input.input_ids.to(self.device		)		)[0]

          __A  :					List[str]						     =       self.tokenizer(
              "" ,    padding="max_length" ,    max_length=self.tokenizer.model_max_length ,    truncation=lowerCamelCase ,    return_tensors="pt" ,    )

          __A  :					Union[str, Any]						     =       self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

          return torch.cat([uncond_embedding, text_embedding]		)


     def __call__(      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = 0.3 ,    lowerCamelCase = 0.6 ,    lowerCamelCase = 0.5 ,    lowerCamelCase = 42 ,    lowerCamelCase = 50 ,    lowerCamelCase = 7.5 ,    ):


          '''simple docstring'''





          __A  :					Optional[int]						     =       steps - int(kmin * steps		)
          __A  :					Union[str, Any]						     =       steps - int(kmax * steps		)

          __A  :					Any						     =       self.prep_text(lowerCamelCase		)

          self.scheduler.set_timesteps(lowerCamelCase		)

          __A     ,__A  :					List[str]						     =       img.size
          __A  :					str						     =       self.encode(lowerCamelCase		)

          torch.manual_seed(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       torch.randn(
              (1, self.unet.config.in_channels, height // 8, width // 8) ,    ).to(self.device		)

          __A  :					Dict						     =       self.scheduler.add_noise(
              lowerCamelCase ,    lowerCamelCase ,    timesteps=self.scheduler.timesteps[tmax] ,    )

          __A  :					str						     =       torch.cat([latents] * 2		)

          __A  :					Optional[int]						     =       self.scheduler.scale_model_input(lowerCamelCase ,    self.scheduler.timesteps[tmax]		)

          with torch.no_grad():
               __A  :					Union[str, Any]						     =       self.unet(
                   lowerCamelCase ,    self.scheduler.timesteps[tmax] ,    encoder_hidden_states=lowerCamelCase ,    ).sample

          __A     ,__A  :					int						     =       pred.chunk(2		)
          __A  :					int						     =       pred_uncond + guidance_scale * (pred_text - pred_uncond)

          __A  :					Tuple						     =       self.scheduler.step(lowerCamelCase ,    self.scheduler.timesteps[tmax] ,    lowerCamelCase		).prev_sample

          for i, t in enumerate(tqdm(self.scheduler.timesteps		)		):
               if i > tmax:
                    if i < tmin:  # layout generation phase
                         __A  :					List[Any]						     =       self.scheduler.add_noise(
                             lowerCamelCase ,    lowerCamelCase ,    timesteps=lowerCamelCase ,    )

                         __A  :					Tuple						     =       (mix_factor * latents) + (
                             1 - mix_factor
                         ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                         __A  :					Any						     =       torch.cat([input] * 2		)

                    else:  # content generation phase
                         __A  :					Any						     =       torch.cat([latents] * 2		)

                    __A  :					Dict						     =       self.scheduler.scale_model_input(lowerCamelCase ,    lowerCamelCase		)

                    with torch.no_grad():
                         __A  :					Dict						     =       self.unet(
                             lowerCamelCase ,    lowerCamelCase ,    encoder_hidden_states=lowerCamelCase ,    ).sample

                    __A     ,__A  :					str						     =       pred.chunk(2		)
                    __A  :					Optional[Any]						     =       pred_uncond + guidance_scale * (pred_text - pred_uncond)

                    __A  :					Any						     =       self.scheduler.step(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		).prev_sample

          return self.decode(lowerCamelCase		)

