




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(		lowercase__			):





     """simple docstring"""



     def __init__(   self			:							List[Any]    ,					A			:							AutoencoderKL    ,					A			:							CLIPTextModel    ,					A			:							CLIPTokenizer    ,					A			:							UNetaDConditionModel    ,					A			:							Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]    ,					)      ->      str:






         """simple docstring"""
         super().__init__()

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

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int    ,					A			:							Optional[Any]     )      ->      str:






         """simple docstring"""


         with torch.no_grad():
             a__  			=							self.vae.encode(tfms.ToTensor()(_snake_case     ).unsqueeze(0     ).to(self.device     ) * 2 - 1     )
             a__  			=							0.1_8_2_1_5 * latent.latent_dist.sample()
         return latent

     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[str]    ,					A			:							Union[str, Any]     )      ->      Optional[Any]:






         """simple docstring"""
         a__  			=							(1 / 0.1_8_2_1_5) * latent
         with torch.no_grad():
             a__  			=							self.vae.decode(_snake_case     ).sample
         a__  			=							(img / 2 + 0.5).clamp(0    ,					1     )
         a__  			=							img.detach().cpu().permute(0    ,					2    ,					3    ,					1     ).numpy()
         a__  			=							(img * 2_55).round().astype("""uint8"""     )
         return Image.fromarray(img[0]     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict    ,					A			:							List[Any]     )      ->      Union[str, Any]:






         """simple docstring"""
         a__  			=							self.tokenizer(
             _snake_case    ,					padding="""max_length"""    ,					max_length=self.tokenizer.model_max_length    ,					truncation=_snake_case    ,					return_tensors="""pt"""    ,					)

         a__  			=							self.text_encoder(text_input.input_ids.to(self.device     )     )[0]

         a__  			=							self.tokenizer(
             """"""    ,					padding="""max_length"""    ,					max_length=self.tokenizer.model_max_length    ,					truncation=_snake_case    ,					return_tensors="""pt"""    ,					)

         a__  			=							self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]

         return torch.cat([uncond_embedding, text_embedding]     )






     def __call__(   self			:							Any    ,					A			:							Image.Image    ,					A			:							str    ,					A			:							float = 0.3    ,					A			:							float = 0.6    ,					A			:							float = 0.5    ,					A			:							int = 42    ,					A			:							int = 50    ,					A			:							float = 7.5    ,					)      ->      Optional[int]:






         """simple docstring"""
         a__  			=							steps - int(kmin * steps     )
         a__  			=							steps - int(kmax * steps     )

         a__  			=							self.prep_text(_snake_case     )

         self.scheduler.set_timesteps(_snake_case     )

         a__			,		a__  			=							img.size
         a__  			=							self.encode(_snake_case     )

         torch.manual_seed(_snake_case     )
         a__  			=							torch.randn(
             (1, self.unet.config.in_channels, height // 8, width // 8)    ,					).to(self.device     )

         a__  			=							self.scheduler.add_noise(
             _snake_case    ,					_snake_case    ,					timesteps=self.scheduler.timesteps[tmax]    ,					)

         a__  			=							torch.cat([latents] * 2     )

         a__  			=							self.scheduler.scale_model_input(_snake_case    ,					self.scheduler.timesteps[tmax]     )

         with torch.no_grad():
             a__  			=							self.unet(
                 _snake_case    ,					self.scheduler.timesteps[tmax]    ,					encoder_hidden_states=_snake_case    ,					).sample

         a__			,		a__  			=							pred.chunk(2     )
         a__  			=							pred_uncond + guidance_scale * (pred_text - pred_uncond)

         a__  			=							self.scheduler.step(_snake_case    ,					self.scheduler.timesteps[tmax]    ,					_snake_case     ).prev_sample

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

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

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

                 a__  			=							self.scheduler.scale_model_input(_snake_case    ,					_snake_case     )

                 with torch.no_grad():
                     a__  			=							self.unet(
                         _snake_case    ,					_snake_case    ,					encoder_hidden_states=_snake_case    ,					).sample

                 a__			,		a__  			=							pred.chunk(2     )
                 a__  			=							pred_uncond + guidance_scale * (pred_text - pred_uncond)

                 a__  			=							self.scheduler.step(_snake_case    ,					_snake_case    ,					_snake_case     ).prev_sample

         return self.decode(_snake_case     )












def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 9 / 5) + 32  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) + 273.15  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 9 / 5) + 491.67  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) - 32) * 5 / 9  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(((float(__A			) - 32) * 5 / 9) + 273.15  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) + 459.67  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) - 273.15  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(((float(__A			) - 273.15) * 9 / 5) + 32  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 9 / 5)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) - 491.67) * 5 / 9  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round(float(__A			) - 459.67  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 5 / 9)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 1.25 + 273.15)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 2.25 + 32)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 1.25)  ,							__A			)


def   A		(__A  :       float  ,							__A  :       int = 2			)  -> float:







     """simple docstring"""


     return round((float(__A			) * 2.25 + 32 + 459.67)  ,							__A			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






