






'''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        _SCREAMING_SNAKE_CASE  (				A  :       Any  )    -> int:







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





class a_    (    UpperCamelCase_							):







   def __init__(self					,   __a					,   __a					,   __a					,   )			->      Any:
      """simple docstring"""






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







   @torch.no_grad()
   def __call__(self					,   __a = None					,   __a = 1					,   __a = 1_0_0					,   __a = 0.0					,   __a = None					,   __a = "pil"					,   __a = True					,   )			->      Union[Tuple, ImagePipelineOutput]:
      """simple docstring"""






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

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

      __snake_case				:      List[str]     							= image.shape[-2:]

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

      __snake_case				:      Tuple     							= randn_tensor(__a					,   generator=__a					,   device=self.device					,   dtype=__a)

      __snake_case				:      Optional[int]     							= image.to(device=self.device					,   dtype=__a)

      # set timesteps and move to the correct device
      self.scheduler.set_timesteps(__a					,   device=self.device)
      __snake_case				:      Optional[int]     							= self.scheduler.timesteps

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

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

      # decode the image latents with the VQVAE
      __snake_case				:      Tuple     							= self.vqvae.decode(__a).sample
      __snake_case				:      Any     							= torch.clamp(__a					,   -1.0					,   1.0)
      __snake_case				:      Dict     							= image / 2 + 0.5
      __snake_case				:      List[str]     							= image.cpu().permute(0					,   2					,   3					,   1).numpy()

      if output_type == "pil":
         __snake_case				:      Optional[int]     							= self.numpy_to_pil(__a)

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=__a)







'''simple docstring'''






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


__A    	=   logging.get_logger(__name__)

__A    	=   {
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class a_    (    UpperCamelCase_							):
   _snake_case        =							"""vit_msn"""







   def __init__(self					,   __a=7_6_8					,   __a=1_2					,   __a=1_2					,   __a=3_0_7_2					,   __a="gelu"					,   __a=0.0					,   __a=0.0					,   __a=0.02					,   __a=1E-06					,   __a=2_2_4					,   __a=1_6					,   __a=3					,   __a=True					,   **__a					,   )			->      Any:
      """simple docstring"""






      super().__init__(**__a)

      __snake_case				:      List[str]     							= hidden_size
      __snake_case				:      Optional[int]     							= num_hidden_layers
      __snake_case				:      Optional[Any]     							= num_attention_heads
      __snake_case				:      str     							= intermediate_size
      __snake_case				:      List[str]     							= hidden_act
      __snake_case				:      List[Any]     							= hidden_dropout_prob
      __snake_case				:      Tuple     							= attention_probs_dropout_prob
      __snake_case				:      List[str]     							= initializer_range
      __snake_case				:      Optional[int]     							= layer_norm_eps
      __snake_case				:      Dict     							= image_size
      __snake_case				:      int     							= patch_size
      __snake_case				:      Dict     							= num_channels
      __snake_case				:      Tuple     							= qkv_bias