






'''simple docstring'''



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class 							__SCREAMING_SNAKE_CASE       (							nn.Module     ):




      def __init__(  self ,   lowerCamelCase = 16 ,   lowerCamelCase = 88 ,   lowerCamelCase = None ,   lowerCamelCase = 1 ,   lowerCamelCase = 0.0 ,   lowerCamelCase = 32 ,   lowerCamelCase = None ,   lowerCamelCase = False ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = "geglu" ,   lowerCamelCase = None ,   )							->List[str]:



         '''simple docstring'''
         super().__init__()
         __a				     =    nn.ModuleList(
             [
                 TransformeraDModel(
                     num_attention_heads=lowerCamelCase ,   attention_head_dim=lowerCamelCase ,   in_channels=lowerCamelCase ,   num_layers=lowerCamelCase ,   dropout=lowerCamelCase ,   norm_num_groups=lowerCamelCase ,   cross_attention_dim=lowerCamelCase ,   attention_bias=lowerCamelCase ,   sample_size=lowerCamelCase ,   num_vector_embeds=lowerCamelCase ,   activation_fn=lowerCamelCase ,   num_embeds_ada_norm=lowerCamelCase ,   )
                 for _ in range(2      )
             ]      )

         # Variables that can be set by a pipeline:

         # The ratio of transformer1 to transformer2's output states to be combined during inference
         __a				     =    0.5

         # The shape of `encoder_hidden_states` is expected to be
         # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
         __a				     =    [77, 257]

         # Which transformer to use to encode which condition.
         # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
         __a				     =    [1, 0]






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase=None ,   lowerCamelCase=None ,   lowerCamelCase=None ,   lowerCamelCase = True ,   )							->Any:



         '''simple docstring'''
         __a				     =    hidden_states

         __a				     =    []
         __a				     =    0
         # attention_mask is not used yet
         for i in range(2      ):
            # for each of the two transformers, pass the corresponding condition tokens
            __a				     =    encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
            __a				     =    self.transformer_index_for_condition[i]
            __a				     =    self.transformers[transformer_index](
                lowerCamelCase ,   encoder_hidden_states=lowerCamelCase ,   timestep=lowerCamelCase ,   cross_attention_kwargs=lowerCamelCase ,   return_dict=lowerCamelCase ,   )[0]
            encoded_states.append(encoded_state - input_states      )
            tokens_start += self.condition_lengths[i]

         __a				     =    encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
         __a				     =    output_states + input_states

         if not return_dict:
            return (output_states,)

         return TransformeraDModelOutput(sample=lowerCamelCase      )







'''simple docstring'''



import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[Any]    )  ->			Dict:

   """simple docstring"""
   __a     ,			__a				     =    image.size
   __a     ,			__a				     =    (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
   __a				     =    image.resize((w, h),					resample=PIL_INTERPOLATION['lanczos']    )
   __a				     =    np.array(SCREAMING_SNAKE_CASE__    ).astype(np.floataa    ) / 2_5_5.0
   __a				     =    image[None].transpose(0,					3,					1,					2    )
   __a				     =    torch.from_numpy(SCREAMING_SNAKE_CASE__    )
   return 2.0 * image - 1.0



class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   )							->Dict:



         '''simple docstring'''
         super().__init__()
         self.register_modules(vqvae=lowerCamelCase ,   unet=lowerCamelCase ,   scheduler=lowerCamelCase      )






      @torch.no_grad()
      def __call__(  self ,   lowerCamelCase = None ,   lowerCamelCase = 1 ,   lowerCamelCase = 100 ,   lowerCamelCase = 0.0 ,   lowerCamelCase = None ,   lowerCamelCase = "pil" ,   lowerCamelCase = True ,   )							->Union[Tuple, ImagePipelineOutput]:



         '''simple docstring'''
         if isinstance(lowerCamelCase ,   PIL.Image.Image      ):
            __a				     =    1
         elif isinstance(lowerCamelCase ,   torch.Tensor      ):
            __a				     =    image.shape[0]
         else:
            raise ValueError(F"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCamelCase      )}"""      )

         if isinstance(lowerCamelCase ,   PIL.Image.Image      ):
            __a				     =    preprocess(lowerCamelCase      )

         __a     ,			__a				     =    image.shape[-2:]

         # in_channels should be 6: 3 for latents, 3 for low resolution image
         __a				     =    (batch_size, self.unet.config.in_channels // 2, height, width)
         __a				     =    next(self.unet.parameters()      ).dtype

         __a				     =    randn_tensor(lowerCamelCase ,   generator=lowerCamelCase ,   device=self.device ,   dtype=lowerCamelCase      )

         __a				     =    image.to(device=self.device ,   dtype=lowerCamelCase      )

         # set timesteps and move to the correct device
         self.scheduler.set_timesteps(lowerCamelCase ,   device=self.device      )
         __a				     =    self.scheduler.timesteps

         # scale the initial noise by the standard deviation required by the scheduler
         __a				     =    latents * self.scheduler.init_noise_sigma

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
         __a				     =    'eta' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
         __a				     =    {}
         if accepts_eta:
            __a				     =    eta

         for t in self.progress_bar(lowerCamelCase      ):
            # concat latents and low resolution image in the channel dimension.
            __a				     =    torch.cat([latents, image] ,   dim=1      )
            __a				     =    self.scheduler.scale_model_input(lowerCamelCase ,   lowerCamelCase      )
            # predict the noise residual
            __a				     =    self.unet(lowerCamelCase ,   lowerCamelCase      ).sample
            # compute the previous noisy sample x_t -> x_t-1
            __a				     =    self.scheduler.step(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   **lowerCamelCase      ).prev_sample

         # decode the image latents with the VQVAE
         __a				     =    self.vqvae.decode(lowerCamelCase      ).sample
         __a				     =    torch.clamp(lowerCamelCase ,   -1.0 ,   1.0      )
         __a				     =    image / 2 + 0.5
         __a				     =    image.cpu().permute(0 ,   2 ,   3 ,   1      ).numpy()

         if output_type == "pil":
            __a				     =    self.numpy_to_pil(lowerCamelCase      )

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=lowerCamelCase      )