


'''simple docstring'''


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
      from .tokenization_deberta_va import DebertaVaTokenizer
else:
      _UpperCamelCase          =     None

_UpperCamelCase          =     logging.get_logger(__name__)

_UpperCamelCase          =     {"""vocab_file""": """spm.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCamelCase          =     {
    """vocab_file""": {
        """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model""",
        """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model""",
        """microsoft/deberta-v2-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"""
        ),
        """microsoft/deberta-v2-xxlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"""
        ),
    }
}

_UpperCamelCase          =     {
    """microsoft/deberta-v2-xlarge""": 512,
    """microsoft/deberta-v2-xxlarge""": 512,
    """microsoft/deberta-v2-xlarge-mnli""": 512,
    """microsoft/deberta-v2-xxlarge-mnli""": 512,
}

_UpperCamelCase          =     {
    """microsoft/deberta-v2-xlarge""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xxlarge""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xlarge-mnli""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xxlarge-mnli""": {"""do_lower_case""": False},
}







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							VOCAB_FILES_NAMES
     lowerCamelCase__						=							PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase__						=							PRETRAINED_INIT_CONFIGURATION
     lowerCamelCase__						=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase__						=							DebertaVaTokenizer


     def __init__(      self ,    lowerCamelCase=None ,    lowerCamelCase=None ,    lowerCamelCase=False ,    lowerCamelCase=False ,    lowerCamelCase="[CLS]" ,    lowerCamelCase="[SEP]" ,    lowerCamelCase="[UNK]" ,    lowerCamelCase="[SEP]" ,    lowerCamelCase="[PAD]" ,    lowerCamelCase="[CLS]" ,    lowerCamelCase="[MASK]" ,    **lowerCamelCase ,    ):


          '''simple docstring'''





          super().__init__(
              lowerCamelCase ,    tokenizer_file=lowerCamelCase ,    do_lower_case=lowerCamelCase ,    bos_token=lowerCamelCase ,    eos_token=lowerCamelCase ,    unk_token=lowerCamelCase ,    sep_token=lowerCamelCase ,    pad_token=lowerCamelCase ,    cls_token=lowerCamelCase ,    mask_token=lowerCamelCase ,    split_by_punct=lowerCamelCase ,    **lowerCamelCase ,    )

          __A  :					Any						     =       do_lower_case
          __A  :					int						     =       split_by_punct
          __A  :					Optional[int]						     =       vocab_file
          __A  :					Union[str, Any]						     =       False if not self.vocab_file else True


     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  :					Dict						     =       [self.cls_token_id]
          __A  :					Optional[int]						     =       [self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep


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


          '''simple docstring'''





          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCamelCase ,    token_ids_a=lowerCamelCase ,    already_has_special_tokens=lowerCamelCase		)

          if token_ids_a is not None:
               return [1] + ([0] * len(lowerCamelCase		)) + [1] + ([0] * len(lowerCamelCase		)) + [1]
          return [1] + ([0] * len(lowerCamelCase		)) + [1]


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


          '''simple docstring'''





          __A  :					Optional[int]						     =       [self.sep_token_id]
          __A  :					Tuple						     =       [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 = None		):


          '''simple docstring'''





          if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."		)

          if not os.path.isdir(lowerCamelCase		):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
               return
          __A  :					Tuple						     =       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		):
               copyfile(self.vocab_file ,    lowerCamelCase		)

          return (out_vocab_file,)





'''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		)

