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 				__lowerCAmelCase					(    a_  ):



 '''simple docstring'''

 def __init__(     self    :						Dict    ,     lowerCamelCase    :						AutoencoderKL    ,     lowerCamelCase    :						CLIPTextModel    ,     lowerCamelCase    :						CLIPTokenizer    ,     lowerCamelCase    :						UNetaDConditionModel    ,     lowerCamelCase    :						Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]    ,     )	->							Union[str, Any]:
        super().__init__()

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

 def    __snake_case   (     self    :						int    ,     lowerCamelCase    :						Union[str, Any]					)	->							List[Any]:
        with torch.no_grad():
               _UpperCamelCase       :    Union[str, Any]					  =					self.vae.encode(tfms.ToTensor()(lowerCamelCase					).unsqueeze(0					).to(self.device					) * 2 - 1					)
               _UpperCamelCase       :    Optional[int]					  =					0.1_8_2_1_5 * latent.latent_dist.sample()
        return latent

 def    __snake_case   (     self    :						Optional[Any]    ,     lowerCamelCase    :						Optional[Any]					)	->							Union[str, Any]:
        _UpperCamelCase       :    List[Any]					  =					(1 / 0.1_8_2_1_5) * latent
        with torch.no_grad():
               _UpperCamelCase       :    Optional[Any]					  =					self.vae.decode(lowerCamelCase					).sample
        _UpperCamelCase       :    int					  =					(img / 2 + 0.5).clamp(0    ,     1					)
        _UpperCamelCase       :    Dict					  =					img.detach().cpu().permute(0    ,     2    ,     3    ,     1					).numpy()
        _UpperCamelCase       :    str					  =					(img * 2_5_5).round().astype('uint8'					)
        return Image.fromarray(img[0]					)

 def    __snake_case   (     self    :						Union[str, Any]    ,     lowerCamelCase    :						List[str]					)	->							Optional[Any]:
        _UpperCamelCase       :    List[Any]					  =					self.tokenizer(
            lowerCamelCase    ,     padding='max_length'    ,     max_length=self.tokenizer.model_max_length    ,     truncation=lowerCamelCase    ,     return_tensors='pt'    ,     )

        _UpperCamelCase       :    int					  =					self.text_encoder(text_input.input_ids.to(self.device					)					)[0]

        _UpperCamelCase       :    List[Any]					  =					self.tokenizer(
            ''    ,     padding='max_length'    ,     max_length=self.tokenizer.model_max_length    ,     truncation=lowerCamelCase    ,     return_tensors='pt'    ,     )

        _UpperCamelCase       :    Optional[Any]					  =					self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]

        return torch.cat([uncond_embedding, text_embedding]					)

 def __call__(     self    :						List[str]    ,     lowerCamelCase    :						Image.Image    ,     lowerCamelCase    :						str    ,     lowerCamelCase    :						float = 0.3    ,     lowerCamelCase    :						float = 0.6    ,     lowerCamelCase    :						float = 0.5    ,     lowerCamelCase    :						int = 4_2    ,     lowerCamelCase    :						int = 5_0    ,     lowerCamelCase    :						float = 7.5    ,     )	->							Image.Image:
        _UpperCamelCase       :    Dict					  =					steps - int(kmin * steps					)
        _UpperCamelCase       :    Dict					  =					steps - int(kmax * steps					)

        _UpperCamelCase       :    List[str]					  =					self.prep_text(lowerCamelCase					)

        self.scheduler.set_timesteps(lowerCamelCase					)

        _UpperCamelCase  , _UpperCamelCase       :    Optional[Any]					  =					img.size
        _UpperCamelCase       :    int					  =					self.encode(lowerCamelCase					)

        torch.manual_seed(lowerCamelCase					)
        _UpperCamelCase       :    Any					  =					torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)    ,     ).to(self.device					)

        _UpperCamelCase       :    Tuple					  =					self.scheduler.add_noise(
            lowerCamelCase    ,     lowerCamelCase    ,     timesteps=self.scheduler.timesteps[tmax]    ,     )

        _UpperCamelCase       :    int					  =					torch.cat([latents] * 2					)

        _UpperCamelCase       :    List[Any]					  =					self.scheduler.scale_model_input(lowerCamelCase    ,     self.scheduler.timesteps[tmax]					)

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

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

        _UpperCamelCase       :    Dict					  =					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
                             _UpperCamelCase       :    Optional[Any]					  =					self.scheduler.add_noise(
                                 lowerCamelCase    ,     lowerCamelCase    ,     timesteps=lowerCamelCase    ,     )

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

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

                      _UpperCamelCase       :    Optional[Any]					  =					self.scheduler.scale_model_input(lowerCamelCase    ,     lowerCamelCase					)

                      with torch.no_grad():
                             _UpperCamelCase       :    str					  =					self.unet(
                                 lowerCamelCase    ,     lowerCamelCase    ,     encoder_hidden_states=lowerCamelCase    ,     ).sample

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

                      _UpperCamelCase       :    Union[str, Any]					  =					self.scheduler.step(lowerCamelCase    ,     lowerCamelCase    ,     lowerCamelCase					).prev_sample

        return self.decode(lowerCamelCase					)




def SCREAMING_SNAKE_CASE			(       lowercase_:		int			):
       if not isinstance(lowercase_						,  lowercase_			):
              _UpperCamelCase       :    Optional[int]					  =					(
                  'operation can not be conducted on a object of type '
                  F"{type(lowercase_			).__name__}"
              )
              raise TypeError(lowercase_			)
       _UpperCamelCase       :    List[Any]					  =					''
       for _ in range(0						,  32			):
              bit_string += str(number % 2			)
              _UpperCamelCase       :    Dict					  =					number >> 1
       return bit_string


def SCREAMING_SNAKE_CASE			(       lowercase_:		int			):
       if number < 0:
              raise ValueError('the value of input must be positive'			)
       elif isinstance(lowercase_						,  lowercase_			):
              raise TypeError('Input value must be a \'int\' type'			)
       elif isinstance(lowercase_						,  lowercase_			):
              raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''			)
       _UpperCamelCase       :    List[Any]					  =					0
       # iterator over [1 to 32],since we are dealing with 32 bit integer
       for _ in range(1						,  33			):
              # left shift the bits by unity
              _UpperCamelCase       :    Tuple					  =					result << 1
              # get the end bit
              _UpperCamelCase       :    List[Any]					  =					number % 2
              # right shift the bits by unity
              _UpperCamelCase       :    Dict					  =					number >> 1
              # add that bit to our ans
              _UpperCamelCase       :    int					  =					result | end_bit
       return get_reverse_bit_string(lowercase_			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()



