


'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	UNetaDModel
     lowerCamelCase				:	KarrasVeScheduler





     def __init__( self       :      str    ,      UpperCAmelCase__       :      UNetaDModel    ,      UpperCAmelCase__       :      KarrasVeScheduler    )				->  int:
        super().__init__()
        self.register_modules(unet=UpperCAmelCase__    ,      scheduler=UpperCAmelCase__    )





     @torch.no_grad()
     def __call__( self       :      Dict    ,      UpperCAmelCase__       :      int = 1    ,      UpperCAmelCase__       :      int = 5_0    ,      UpperCAmelCase__       :      Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,      UpperCAmelCase__       :      Optional[str] = "pil"    ,      UpperCAmelCase__       :      bool = True    ,      **UpperCAmelCase__       :      Dict    ,      )				->  Union[Tuple, ImagePipelineOutput]:
        lowerCAmelCase     =			self.unet.config.sample_size
        lowerCAmelCase     =			(batch_size, 3, img_size, img_size)

        lowerCAmelCase     =			self.unet

        # sample x_0 ~ N(0, sigma_0^2 * I)
        lowerCAmelCase     =			randn_tensor(UpperCAmelCase__    ,      generator=UpperCAmelCase__    ,      device=self.device    ) * self.scheduler.init_noise_sigma

        self.scheduler.set_timesteps(UpperCAmelCase__    )

        for t in self.progress_bar(self.scheduler.timesteps    ):
           # here sigma_t == t_i from the paper
           lowerCAmelCase     =			self.scheduler.schedule[t]
           lowerCAmelCase     =			self.scheduler.schedule[t - 1] if t > 0 else 0

           # 1. Select temporarily increased noise level sigma_hat
           # 2. Add new noise to move from sample_i to sample_hat
           lowerCAmelCase     ,  lowerCAmelCase     =			self.scheduler.add_noise_to_input(UpperCAmelCase__    ,      UpperCAmelCase__    ,      generator=UpperCAmelCase__    )

           # 3. Predict the noise residual given the noise magnitude `sigma_hat`
           # The model inputs and output are adjusted by following eq. (213) in [1].
           lowerCAmelCase     =			(sigma_hat / 2) * model((sample_hat + 1) / 2    ,      sigma_hat / 2    ).sample

           # 4. Evaluate dx/dt at sigma_hat
           # 5. Take Euler step from sigma to sigma_prev
           lowerCAmelCase     =			self.scheduler.step(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )

           if sigma_prev != 0:
              # 6. Apply 2nd order correction
              # The model inputs and output are adjusted by following eq. (213) in [1].
              lowerCAmelCase     =			(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2    ,      sigma_prev / 2    ).sample
              lowerCAmelCase     =			self.scheduler.step_correct(
                  UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      step_output.prev_sample    ,      step_output['derivative']    ,      )
           lowerCAmelCase     =			step_output.prev_sample

        lowerCAmelCase     =			(sample / 2 + 0.5).clamp(0    ,      1    )
        lowerCAmelCase     =			sample.cpu().permute(0    ,      2    ,      3    ,      1    ).numpy()
        if output_type == "pil":
           lowerCAmelCase     =			self.numpy_to_pil(UpperCAmelCase__    )

        if not return_dict:
           return (image,)

        return ImagePipelineOutput(images=UpperCAmelCase__    )











import copy

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


_lowerCamelCase	:		Any							       =   logging.get_logger(__name__)


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''encoder-decoder'''
    UpperCAmelCase__							  =      True


    def __init__(			self      :	List[str]					, **UpperCAmelCase__      :	Union[str, Any])						->List[Any]:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)
           assert (
               "encoder" in kwargs and "decoder" in kwargs
           ), "Config has to be initialized with encoder and decoder config"
           A__       =    kwargs.pop('''encoder''')
           A__       =    encoder_config.pop('''model_type''')
           A__       =    kwargs.pop('''decoder''')
           A__       =    decoder_config.pop('''model_type''')

           from ..auto.configuration_auto import AutoConfig

           A__       =    AutoConfig.for_model(UpperCAmelCase__					, **UpperCAmelCase__)
           A__       =    AutoConfig.for_model(UpperCAmelCase__					, **UpperCAmelCase__)
           A__       =    True


    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	Union[str, Any]					, UpperCAmelCase__      :	PretrainedConfig					, UpperCAmelCase__      :	PretrainedConfig					, **UpperCAmelCase__      :	Union[str, Any])						->PretrainedConfig:





           '''simple docstring'''

           logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''')
           A__       =    True
           A__       =    True

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





    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Optional[Any]:





           '''simple docstring'''

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


