


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__ ( A__			):






      def __init__(     self					:   Optional[int],		__lowerCamelCase					:   AutoencoderKL,		__lowerCamelCase					:   CLIPTextModel,		__lowerCamelCase					:   CLIPTokenizer,		__lowerCamelCase					:   UNetaDConditionModel,		__lowerCamelCase					:   Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler],		)  -> Tuple:
           super().__init__()

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






      def 	__lowercase(     self					:   Dict,		__lowerCamelCase					:   Dict   )  -> Any:






           with torch.no_grad():
                UpperCamelCase__							:  Any								=     self.vae.encode(tfms.ToTensor()(__lowerCamelCase   ).unsqueeze(0   ).to(self.device   ) * 2 - 1   )
                UpperCamelCase__							:  Tuple								=     0.1_8215 * latent.latent_dist.sample()
           return latent






      def 	__lowercase(     self					:   Any,		__lowerCamelCase					:   Any   )  -> Dict:
           UpperCamelCase__							:  List[Any]								=     (1 / 0.1_8215) * latent
           with torch.no_grad():
                UpperCamelCase__							:  Tuple								=     self.vae.decode(__lowerCamelCase   ).sample
           UpperCamelCase__							:  List[Any]								=     (img / 2 + 0.5).clamp(0,		1   )
           UpperCamelCase__							:  Optional[Any]								=     img.detach().cpu().permute(0,		2,		3,		1   ).numpy()
           UpperCamelCase__							:  Tuple								=     (img * 2_55).round().astype('''uint8'''   )
           return Image.fromarray(img[0]   )






      def 	__lowercase(     self					:   List[str],		__lowerCamelCase					:   str   )  -> Tuple:
           UpperCamelCase__							:  List[Any]								=     self.tokenizer(
               __lowerCamelCase,		padding='''max_length''',		max_length=self.tokenizer.model_max_length,		truncation=__lowerCamelCase,		return_tensors='''pt''',		)

           UpperCamelCase__							:  List[Any]								=     self.text_encoder(text_input.input_ids.to(self.device   )   )[0]

           UpperCamelCase__							:  Union[str, Any]								=     self.tokenizer(
               '''''',		padding='''max_length''',		max_length=self.tokenizer.model_max_length,		truncation=__lowerCamelCase,		return_tensors='''pt''',		)

           UpperCamelCase__							:  Dict								=     self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

           return torch.cat([uncond_embedding, text_embedding]   )






      def __call__(     self					:   List[Any],		__lowerCamelCase					:   Image.Image,		__lowerCamelCase					:   str,		__lowerCamelCase					:   float = 0.3,		__lowerCamelCase					:   float = 0.6,		__lowerCamelCase					:   float = 0.5,		__lowerCamelCase					:   int = 42,		__lowerCamelCase					:   int = 50,		__lowerCamelCase					:   float = 7.5,		)  -> List[str]:
           UpperCamelCase__							:  Dict								=     steps - int(kmin * steps   )
           UpperCamelCase__							:  Optional[int]								=     steps - int(kmax * steps   )

           UpperCamelCase__							:  List[str]								=     self.prep_text(__lowerCamelCase   )

           self.scheduler.set_timesteps(__lowerCamelCase   )

           UpperCamelCase__							:  Optional[int]								=     img.size
           UpperCamelCase__							:  str								=     self.encode(__lowerCamelCase   )

           torch.manual_seed(__lowerCamelCase   )
           UpperCamelCase__							:  Optional[int]								=     torch.randn(
               (1, self.unet.config.in_channels, height // 8, width // 8),		).to(self.device   )

           UpperCamelCase__							:  List[Any]								=     self.scheduler.add_noise(
               __lowerCamelCase,		__lowerCamelCase,		timesteps=self.scheduler.timesteps[tmax],		)

           UpperCamelCase__							:  Tuple								=     torch.cat([latents] * 2   )

           UpperCamelCase__							:  Optional[int]								=     self.scheduler.scale_model_input(__lowerCamelCase,		self.scheduler.timesteps[tmax]   )

           with torch.no_grad():
                UpperCamelCase__							:  str								=     self.unet(
                    __lowerCamelCase,		self.scheduler.timesteps[tmax],		encoder_hidden_states=__lowerCamelCase,		).sample

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

           UpperCamelCase__							:  List[str]								=     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__							:  Any								=     self.scheduler.add_noise(
                              __lowerCamelCase,		__lowerCamelCase,		timesteps=__lowerCamelCase,		)

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

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

                     UpperCamelCase__							:  Union[str, Any]								=     self.scheduler.scale_model_input(__lowerCamelCase,		__lowerCamelCase   )

                     with torch.no_grad():
                          UpperCamelCase__							:  Optional[int]								=     self.unet(
                              __lowerCamelCase,		__lowerCamelCase,		encoder_hidden_states=__lowerCamelCase,		).sample

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

                     UpperCamelCase__							:  List[str]								=     self.scheduler.step(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase   ).prev_sample

           return self.decode(__lowerCamelCase   )








import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


_SCREAMING_SNAKE_CASE :					List[str]			=	logging.get_logger(__name__)

class  UpperCamelCase__ ( __lowerCamelCase			):






      def __init__(     self					:   List[str],		*__lowerCamelCase					:   Optional[int],		**__lowerCamelCase					:   Dict   )  -> None:
           warnings.warn(
               '''The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
               ''' Please use MobileNetV1ImageProcessor instead.''',		__lowerCamelCase,		)
           super().__init__(*__lowerCamelCase,		**__lowerCamelCase   )




