"""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 					UpperCAmelCase		(_UpperCAmelCase						):



    """simple docstring"""

    def __init__(     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    ):
       super().__init__()

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






    def _snake_case    (     self					,    _UpperCAmelCase							):
       with torch.no_grad():
          lowercase__:     Optional[int]      =   self.vae.encode(tfms.ToTensor()(_UpperCAmelCase							).unsqueeze(0							).to(self.device							) * 2 - 1							)
          lowercase__:     Any      =   0.18_215 * latent.latent_dist.sample()
       return latent






    def _snake_case    (     self					,    _UpperCAmelCase							):
       lowercase__:     Union[str, Any]      =   (1 / 0.18_215) * latent
       with torch.no_grad():
          lowercase__:     int      =   self.vae.decode(_UpperCAmelCase							).sample
       lowercase__:     Any      =   (img / 2 + 0.5).clamp(0					,    1							)
       lowercase__:     Dict      =   img.detach().cpu().permute(0					,    2					,    3					,    1							).numpy()
       lowercase__:     Union[str, Any]      =   (img * 255).round().astype('''uint8'''							)
       return Image.fromarray(img[0]							)






    def _snake_case    (     self					,    _UpperCAmelCase							):
       lowercase__:     int      =   self.tokenizer(
           _UpperCAmelCase					,    padding='''max_length'''					,    max_length=self.tokenizer.model_max_length					,    truncation=_UpperCAmelCase					,    return_tensors='''pt'''					,    )

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

       lowercase__:     Tuple      =   self.tokenizer(
           ''''''					,    padding='''max_length'''					,    max_length=self.tokenizer.model_max_length					,    truncation=_UpperCAmelCase					,    return_tensors='''pt'''					,    )

       lowercase__:     Tuple      =   self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

       return torch.cat([uncond_embedding, text_embedding]							)






    def __call__(     self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = 0.3					,    _UpperCAmelCase = 0.6					,    _UpperCAmelCase = 0.5					,    _UpperCAmelCase = 42					,    _UpperCAmelCase = 50					,    _UpperCAmelCase = 7.5					,    ):
       lowercase__:     Union[str, Any]      =   steps - int(kmin * steps							)
       lowercase__:     int      =   steps - int(kmax * steps							)

       lowercase__:     int      =   self.prep_text(_UpperCAmelCase							)

       self.scheduler.set_timesteps(_UpperCAmelCase							)

       lowercase__,				lowercase__:     List[str]      =   img.size
       lowercase__:     str      =   self.encode(_UpperCAmelCase							)

       torch.manual_seed(_UpperCAmelCase							)
       lowercase__:     Dict      =   torch.randn(
           (1, self.unet.config.in_channels, height // 8, width // 8)					,    ).to(self.device							)

       lowercase__:     List[str]      =   self.scheduler.add_noise(
           _UpperCAmelCase					,    _UpperCAmelCase					,    timesteps=self.scheduler.timesteps[tmax]					,    )

       lowercase__:     Any      =   torch.cat([latents] * 2							)

       lowercase__:     Optional[Any]      =   self.scheduler.scale_model_input(_UpperCAmelCase					,    self.scheduler.timesteps[tmax]							)

       with torch.no_grad():
          lowercase__:     List[Any]      =   self.unet(
              _UpperCAmelCase					,    self.scheduler.timesteps[tmax]					,    encoder_hidden_states=_UpperCAmelCase					,    ).sample

       lowercase__,				lowercase__:     Union[str, Any]      =   pred.chunk(2							)
       lowercase__:     Dict      =   pred_uncond + guidance_scale * (pred_text - pred_uncond)

       lowercase__:     Union[str, Any]      =   self.scheduler.step(_UpperCAmelCase					,    self.scheduler.timesteps[tmax]					,    _UpperCAmelCase							).prev_sample

       for i, t in enumerate(tqdm(self.scheduler.timesteps							)							):
          if i > tmax:
             if i < tmin:  # layout generation phase
                lowercase__:     Tuple      =   self.scheduler.add_noise(
                    _UpperCAmelCase					,    _UpperCAmelCase					,    timesteps=_UpperCAmelCase					,    )

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

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

             lowercase__:     str      =   self.scheduler.scale_model_input(_UpperCAmelCase					,    _UpperCAmelCase							)

             with torch.no_grad():
                lowercase__:     List[str]      =   self.unet(
                    _UpperCAmelCase					,    _UpperCAmelCase					,    encoder_hidden_states=_UpperCAmelCase					,    ).sample

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

             lowercase__:     int      =   self.scheduler.step(_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase							).prev_sample

       return self.decode(_UpperCAmelCase							)

"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A         =				logging.get_logger(__name__)

__A         =				{
    "uw-madison/yoso-4096": "https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}


class 					UpperCAmelCase		(_UpperCAmelCase						):



    """simple docstring"""

    _UpperCAmelCase			:Tuple							=				"yoso"






    def __init__(     self					,    _UpperCAmelCase=50265					,    _UpperCAmelCase=768					,    _UpperCAmelCase=12					,    _UpperCAmelCase=12					,    _UpperCAmelCase=3072					,    _UpperCAmelCase="gelu"					,    _UpperCAmelCase=0.1					,    _UpperCAmelCase=0.1					,    _UpperCAmelCase=4096					,    _UpperCAmelCase=1					,    _UpperCAmelCase=0.02					,    _UpperCAmelCase=1e-1_2					,    _UpperCAmelCase="absolute"					,    _UpperCAmelCase=True					,    _UpperCAmelCase=9					,    _UpperCAmelCase=64					,    _UpperCAmelCase=None					,    _UpperCAmelCase=True					,    _UpperCAmelCase=True					,    _UpperCAmelCase=1					,    _UpperCAmelCase=0					,    _UpperCAmelCase=2					,    **_UpperCAmelCase					,    ):
       super().__init__(pad_token_id=_UpperCAmelCase					,    bos_token_id=_UpperCAmelCase					,    eos_token_id=_UpperCAmelCase					,    **_UpperCAmelCase							)

       lowercase__:     int      =   vocab_size
       lowercase__:     List[str]      =   max_position_embeddings
       lowercase__:     Tuple      =   hidden_size
       lowercase__:     Optional[Any]      =   num_hidden_layers
       lowercase__:     Optional[int]      =   num_attention_heads
       lowercase__:     List[Any]      =   intermediate_size
       lowercase__:     Union[str, Any]      =   hidden_act
       lowercase__:     int      =   hidden_dropout_prob
       lowercase__:     Optional[Any]      =   attention_probs_dropout_prob
       lowercase__:     List[Any]      =   initializer_range
       lowercase__:     Optional[Any]      =   type_vocab_size
       lowercase__:     int      =   layer_norm_eps
       lowercase__:     Optional[Any]      =   position_embedding_type
       lowercase__:     int      =   use_expectation
       lowercase__:     List[str]      =   hash_code_len
       lowercase__:     Union[str, Any]      =   num_hash
       lowercase__:     Any      =   conv_window
       lowercase__:     Dict      =   use_fast_hash
       lowercase__:     Dict      =   lsh_backward
