





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


_UpperCamelCase       = logging.get_logger(__name__)

_UpperCamelCase       = {
    '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json''',
}




class 				_lowerCamelCase (    a    ):



      """simple docstring"""






      UpperCAmelCase_						:      Optional[int]       ="mpnet"







      def __init__( self				,						UpperCAmelCase=30527				,						UpperCAmelCase=768				,						UpperCAmelCase=12				,						UpperCAmelCase=12				,						UpperCAmelCase=3072				,						UpperCAmelCase="gelu"				,						UpperCAmelCase=0.1				,						UpperCAmelCase=0.1				,						UpperCAmelCase=512				,						UpperCAmelCase=0.02				,						UpperCAmelCase=1E-12				,						UpperCAmelCase=32				,						UpperCAmelCase=1				,						UpperCAmelCase=0				,						UpperCAmelCase=2				,						**UpperCAmelCase				,						)       ->   Any:






        '''simple docstring'''






        super().__init__(pad_token_id=UpperCAmelCase				,						bos_token_id=UpperCAmelCase				,						eos_token_id=UpperCAmelCase				,						**UpperCAmelCase   )

        __snake_case   :				Tuple        =       vocab_size
        __snake_case   :				Union[str, Any]        =       hidden_size
        __snake_case   :				int        =       num_hidden_layers
        __snake_case   :				List[str]        =       num_attention_heads
        __snake_case   :				Optional[int]        =       hidden_act
        __snake_case   :				Any        =       intermediate_size
        __snake_case   :				Optional[int]        =       hidden_dropout_prob
        __snake_case   :				Union[str, Any]        =       attention_probs_dropout_prob
        __snake_case   :				Any        =       max_position_embeddings
        __snake_case   :				str        =       initializer_range
        __snake_case   :				Union[str, Any]        =       layer_norm_eps
        __snake_case   :				Any        =       relative_attention_num_buckets








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				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						)       ->   Optional[Any]:






        '''simple docstring'''






        super().__init__()

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







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   str:






        '''simple docstring'''






        with torch.no_grad():
          __snake_case   :				Tuple        =       self.vae.encode(tfms.ToTensor()(UpperCAmelCase   ).unsqueeze(0   ).to(self.device   ) * 2 - 1   )
          __snake_case   :				Optional[int]        =       0.18_215 * latent.latent_dist.sample()
        return latent







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   str:






        '''simple docstring'''






        __snake_case   :				Optional[Any]        =       (1 / 0.18_215) * latent
        with torch.no_grad():
          __snake_case   :				Any        =       self.vae.decode(UpperCAmelCase   ).sample
        __snake_case   :				Optional[int]        =       (img / 2 + 0.5).clamp(0				,						1   )
        __snake_case   :				Tuple        =       img.detach().cpu().permute(0				,						2				,						3				,						1   ).numpy()
        __snake_case   :				Dict        =       (img * 255).round().astype("uint8"   )
        return Image.fromarray(img[0]   )







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   Dict:






        '''simple docstring'''






        __snake_case   :				int        =       self.tokenizer(
            UpperCAmelCase				,						padding="max_length"				,						max_length=self.tokenizer.model_max_length				,						truncation=UpperCAmelCase				,						return_tensors="pt"				,						)

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

        __snake_case   :				Union[str, Any]        =       self.tokenizer(
            ""				,						padding="max_length"				,						max_length=self.tokenizer.model_max_length				,						truncation=UpperCAmelCase				,						return_tensors="pt"				,						)

        __snake_case   :				int        =       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				,						)       ->   Image.Image:






        '''simple docstring'''






        __snake_case   :				Tuple        =       steps - int(kmin * steps   )
        __snake_case   :				Optional[int]        =       steps - int(kmax * steps   )

        __snake_case   :				Tuple        =       self.prep_text(UpperCAmelCase   )

        self.scheduler.set_timesteps(UpperCAmelCase   )

        __snake_case    ,   __snake_case   :				Optional[Any]        =       img.size
        __snake_case   :				List[Any]        =       self.encode(UpperCAmelCase   )

        torch.manual_seed(UpperCAmelCase   )
        __snake_case   :				str        =       torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)				,						).to(self.device   )

        __snake_case   :				int        =       self.scheduler.add_noise(
            UpperCAmelCase				,						UpperCAmelCase				,						timesteps=self.scheduler.timesteps[tmax]				,						)

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

        __snake_case   :				Union[str, Any]        =       self.scheduler.scale_model_input(UpperCAmelCase				,						self.scheduler.timesteps[tmax]   )

        with torch.no_grad():
          __snake_case   :				int        =       self.unet(
              UpperCAmelCase				,						self.scheduler.timesteps[tmax]				,						encoder_hidden_states=UpperCAmelCase				,						).sample

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

        __snake_case   :				int        =       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
              __snake_case   :				int        =       self.scheduler.add_noise(
                  UpperCAmelCase				,						UpperCAmelCase				,						timesteps=UpperCAmelCase				,						)

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

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

            __snake_case   :				Tuple        =       self.scheduler.scale_model_input(UpperCAmelCase				,						UpperCAmelCase   )

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

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

            __snake_case   :				List[Any]        =       self.scheduler.step(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   ).prev_sample

        return self.decode(UpperCAmelCase   )

