


"""simple docstring"""



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class _lowerCAmelCase							( nn.Module      ):







   def __init__(      self		,      UpperCamelCase__ = 16		,      UpperCamelCase__ = 88		,      UpperCamelCase__ = None		,      UpperCamelCase__ = 1		,      UpperCamelCase__ = 0.0		,      UpperCamelCase__ = 32		,      UpperCamelCase__ = None		,      UpperCamelCase__ = False		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      UpperCamelCase__ = "geglu"		,      UpperCamelCase__ = None		,      )      ->						Optional[Any]:

       '''simple docstring'''




       super().__init__()
       snake_case       : Any    =       nn.ModuleList(
           [
               TransformeraDModel(
                   num_attention_heads=UpperCamelCase__		,      attention_head_dim=UpperCamelCase__		,      in_channels=UpperCamelCase__		,      num_layers=UpperCamelCase__		,      dropout=UpperCamelCase__		,      norm_num_groups=UpperCamelCase__		,      cross_attention_dim=UpperCamelCase__		,      attention_bias=UpperCamelCase__		,      sample_size=UpperCamelCase__		,      num_vector_embeds=UpperCamelCase__		,      activation_fn=UpperCamelCase__		,      num_embeds_ada_norm=UpperCamelCase__		,      )
               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
       snake_case       : Union[str, Any]    =       0.5

       # The shape of `encoder_hidden_states` is expected to be
       # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
       snake_case       : List[Any]    =       [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])`
       snake_case       : Tuple    =       [1, 0]







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      UpperCamelCase__ = True		,      )      ->						List[Any]:

       '''simple docstring'''




       snake_case       : Optional[int]    =       hidden_states

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

       snake_case       : List[Any]    =       encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
       snake_case       : int    =       output_states + input_states

       if not return_dict:
           return (output_states,)

       return TransformeraDModelOutput(sample=UpperCamelCase__		)









"""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     __lowerCAmelCase		(		lowercase    :    Optional[int]					)      ->		Union[str, Any]:


    """simple docstring"""





    snake_case   ,snake_case       : List[str]    =       image.size
    snake_case   ,snake_case       : int    =       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    snake_case       : Any    =       image.resize((w, h)		,				resample=PIL_INTERPOLATION["lanczos"]					)
    snake_case       : Dict    =       np.array(lowercase					).astype(np.floataa					) / 255.0
    snake_case       : List[str]    =       image[None].transpose(0		,				3		,				1		,				2					)
    snake_case       : List[Any]    =       torch.from_numpy(lowercase					)
    return 2.0 * image - 1.0







class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      )      ->						Optional[Any]:

       '''simple docstring'''




       super().__init__()
       self.register_modules(vqvae=UpperCamelCase__		,      unet=UpperCamelCase__		,      scheduler=UpperCamelCase__		)







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

       '''simple docstring'''




       if isinstance(UpperCamelCase__		,      PIL.Image.Image		):
           snake_case       : Tuple    =       1
       elif isinstance(UpperCamelCase__		,      torch.Tensor		):
           snake_case       : str    =       image.shape[0]
       else:
           raise ValueError(F'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCamelCase__		)}'		)

       if isinstance(UpperCamelCase__		,      PIL.Image.Image		):
           snake_case       : Optional[int]    =       preprocess(UpperCamelCase__		)

       snake_case   ,snake_case       : List[str]    =       image.shape[-2:]

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

       snake_case       : int    =       randn_tensor(UpperCamelCase__		,      generator=UpperCamelCase__		,      device=self.device		,      dtype=UpperCamelCase__		)

       snake_case       : List[Any]    =       image.to(device=self.device		,      dtype=UpperCamelCase__		)

       # set timesteps and move to the correct device
       self.scheduler.set_timesteps(UpperCamelCase__		,      device=self.device		)
       snake_case       : Optional[Any]    =       self.scheduler.timesteps

       # scale the initial noise by the standard deviation required by the scheduler
       snake_case       : Optional[int]    =       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]
       snake_case       : Optional[int]    =       "eta" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
       snake_case       : Union[str, Any]    =       {}
       if accepts_eta:
           snake_case       : List[str]    =       eta

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

       # decode the image latents with the VQVAE
       snake_case       : Optional[int]    =       self.vqvae.decode(UpperCamelCase__		).sample
       snake_case       : Tuple    =       torch.clamp(UpperCamelCase__		,      -1.0		,      1.0		)
       snake_case       : Union[str, Any]    =       image / 2 + 0.5
       snake_case       : Optional[Any]    =       image.cpu().permute(0		,      2		,      3		,      1		).numpy()

       if output_type == "pil":
           snake_case       : Union[str, Any]    =       self.numpy_to_pil(UpperCamelCase__		)

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=UpperCamelCase__		)





