





"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


a__   :       Union[str, Any]   						=			logging.get_logger(__name__)



class        lowercase_			(       __UpperCAmelCase							):
							snake_case__			:							Optional[int]										=       """speech-encoder-decoder"""
							snake_case__			:							Any										=       True

							def __init__(     self				,   **__SCREAMING_SNAKE_CASE       ):






								'''simple docstring'''

								super().__init__(**lowerCamelCase__       )
								if "encoder" not in kwargs or "decoder" not in kwargs:
									raise ValueError(
									    F"A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and"
									    F" `decoder` sub-configurations are passed, but only {kwargs}"       )

								snake_case__      							=			kwargs.pop("encoder"       )
								snake_case__      							=			encoder_config.pop("model_type"       )
								snake_case__      							=			kwargs.pop("decoder"       )
								snake_case__      							=			decoder_config.pop("model_type"       )

								snake_case__      							=			AutoConfig.for_model(lowerCamelCase__				,   **lowerCamelCase__       )
								snake_case__      							=			AutoConfig.for_model(lowerCamelCase__				,   **lowerCamelCase__       )
								snake_case__      							=			True

							@classmethod
							def 						_SCREAMING_SNAKE_CASE(     cls				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       ):






								'''simple docstring'''

								logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"       )
								snake_case__      							=			True
								snake_case__      							=			True

								return cls(encoder=encoder_config.to_dict()				,   decoder=decoder_config.to_dict()				,   **lowerCamelCase__       )

							def 						_SCREAMING_SNAKE_CASE(     self       ):






								'''simple docstring'''

								snake_case__      							=			copy.deepcopy(self.__dict__       )
								snake_case__      							=			self.encoder.to_dict()
								snake_case__      							=			self.decoder.to_dict()
								snake_case__      							=			self.__class__.model_type
								return output








"""simple docstring"""


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        lowercase_			(       __lowerCAmelCase							):

       def __init__(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        super().__init__()

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

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        with torch.no_grad():
         snake_case__      							=			self.vae.encode(tfms.ToTensor()(__SCREAMING_SNAKE_CASE       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
         snake_case__      							=			0.18215 * latent.latent_dist.sample()
        return latent

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			(1 / 0.18215) * latent
        with torch.no_grad():
         snake_case__      							=			self.vae.decode(__SCREAMING_SNAKE_CASE       ).sample
        snake_case__      							=			(img / 2 + 0.5).clamp(0				,   1       )
        snake_case__      							=			img.detach().cpu().permute(0				,   2				,   3				,   1       ).numpy()
        snake_case__      							=			(img * 255).round().astype("uint8"       )
        return Image.fromarray(img[0]       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			self.tokenizer(
            __SCREAMING_SNAKE_CASE				,   padding="max_length"				,   max_length=self.tokenizer.model_max_length				,   truncation=__SCREAMING_SNAKE_CASE				,   return_tensors="pt"				,   )

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

        snake_case__      							=			self.tokenizer(
            ""				,   padding="max_length"				,   max_length=self.tokenizer.model_max_length				,   truncation=__SCREAMING_SNAKE_CASE				,   return_tensors="pt"				,   )

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

        return torch.cat([uncond_embedding, text_embedding]       )

       def __call__(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE = 0.3				,   __SCREAMING_SNAKE_CASE = 0.6				,   __SCREAMING_SNAKE_CASE = 0.5				,   __SCREAMING_SNAKE_CASE = 42				,   __SCREAMING_SNAKE_CASE = 50				,   __SCREAMING_SNAKE_CASE = 7.5				,   ):






        '''simple docstring'''

        snake_case__      							=			steps - int(kmin * steps       )
        snake_case__      							=			steps - int(kmax * steps       )

        snake_case__      							=			self.prep_text(__SCREAMING_SNAKE_CASE       )

        self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )

        snake_case__							,				snake_case__      							=			img.size
        snake_case__      							=			self.encode(__SCREAMING_SNAKE_CASE       )

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

        snake_case__      							=			self.scheduler.add_noise(
            __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   timesteps=self.scheduler.timesteps[tmax]				,   )

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

        snake_case__      							=			self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   self.scheduler.timesteps[tmax]       )

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

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

        snake_case__      							=			self.scheduler.step(__SCREAMING_SNAKE_CASE				,   self.scheduler.timesteps[tmax]				,   __SCREAMING_SNAKE_CASE       ).prev_sample

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

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

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

          snake_case__      							=			self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

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

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

          snake_case__      							=			self.scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ).prev_sample

        return self.decode(__SCREAMING_SNAKE_CASE       )

