
from .pipeline_kandinskya_a import KandinskyVaaPipeline
from .pipeline_kandinsky2_2_controlnet import KandinskyVaaControlnetPipeline
from .pipeline_kandinsky2_2_controlnet_img2img import KandinskyVaaControlnetImgaImgPipeline
from .pipeline_kandinsky2_2_img2img import KandinskyVaaImgaImgPipeline
from .pipeline_kandinsky2_2_inpainting import KandinskyVaaInpaintPipeline
from .pipeline_kandinsky2_2_prior import KandinskyVaaPriorPipeline
from .pipeline_kandinsky2_2_prior_emb2emb import KandinskyVaaPriorEmbaEmbPipeline


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				(    lowerCAmelCase__   ):






     """simple docstring"""







     def __init__(							self    :    Optional[int]      ,		_A    :    AutoencoderKL      ,		_A    :    CLIPTextModel      ,		_A    :    CLIPTokenizer      ,		_A    :    UNetaDConditionModel      ,		_A    :    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]      ,		):







       """simple docstring"""




       super().__init__()

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



     def 				UpperCAmelCase__    (							self    :    Optional[int]      ,		_A    :    Dict				):







       """simple docstring"""




       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Dict			=						self.vae.encode(tfms.ToTensor()(_A				).unsqueeze(0				).to(self.device				) * 2 - 1				)
         __SCREAMING_SNAKE_CASE     :					str			=						0.1_82_15 * latent.latent_dist.sample()
       return latent



     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						(1 / 0.1_82_15) * latent
       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Any			=						self.vae.decode(_A				).sample
       __SCREAMING_SNAKE_CASE     :					Any			=						(img / 2 + 0.5).clamp(0      ,		1				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						img.detach().cpu().permute(0      ,		2      ,		3      ,		1				).numpy()
       __SCREAMING_SNAKE_CASE     :					List[str]			=						(img * 255).round().astype('''uint8'''				)
       return Image.fromarray(img[0]				)



     def 				UpperCAmelCase__    (							self    :    Dict      ,		_A    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						self.tokenizer(
           _A      ,		padding='''max_length'''      ,		max_length=self.tokenizer.model_max_length      ,		truncation=_A      ,		return_tensors='''pt'''      ,		)

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.text_encoder(text_input.input_ids.to(self.device				)				)[0]

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

       __SCREAMING_SNAKE_CASE     :					str			=						self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]

       return torch.cat([uncond_embedding, text_embedding]				)




     def __call__(							self    :    Union[str, 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      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						steps - int(kmin * steps				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						steps - int(kmax * steps				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.prep_text(_A				)

       self.scheduler.set_timesteps(_A				)

       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Dict			=						img.size
       __SCREAMING_SNAKE_CASE     :					Dict			=						self.encode(_A				)

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

       __SCREAMING_SNAKE_CASE     :					Dict			=						self.scheduler.add_noise(
           _A      ,		_A      ,		timesteps=self.scheduler.timesteps[tmax]      ,		)

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

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.scheduler.scale_model_input(_A      ,		self.scheduler.timesteps[tmax]				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.unet(
             _A      ,		self.scheduler.timesteps[tmax]      ,		encoder_hidden_states=_A      ,		).sample

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

       __SCREAMING_SNAKE_CASE     :					Any			=						self.scheduler.step(_A      ,		self.scheduler.timesteps[tmax]      ,		_A				).prev_sample

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

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

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

           __SCREAMING_SNAKE_CASE     :					str			=						self.scheduler.scale_model_input(_A      ,		_A				)

           with torch.no_grad():
             __SCREAMING_SNAKE_CASE     :					int			=						self.unet(
                 _A      ,		_A      ,		encoder_hidden_states=_A      ,		).sample

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

           __SCREAMING_SNAKE_CASE     :					Any			=						self.scheduler.step(_A      ,		_A      ,		_A				).prev_sample

       return self.decode(_A				)
