


from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


UpperCAmelCase   :    int     =		transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)






def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]      ):





			'''simple docstring'''




			if isinstance(lowerCamelCase__  ,							torch.Tensor      ):
						return image
			elif isinstance(lowerCamelCase__  ,							PIL.Image.Image      ):
						lowerCamelCase    		=  [image]

			lowerCamelCase    		=  [trans(img.convert("""RGB"""      )      ) for img in image]
			lowerCamelCase    		=  torch.stack(lowerCamelCase__      )
			return image







class        __lowercase       (							a_  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A	)  -> Tuple:







					'''simple docstring'''







					super().__init__()

					# make sure scheduler can always be converted to DDIM
					lowerCamelCase    		=  DDIMScheduler.from_config(scheduler.config	)

					self.register_modules(unet=A		,      scheduler=A	)







		def 					__A   ( self		,      A	)  -> Dict:







					'''simple docstring'''







					if strength < 0 or strength > 1:
								raise ValueError(F'The value of strength should in [0.0, 1.0] but is {strength}'	)







		def 					__A   ( self		,      A		,      A		,      A	)  -> Tuple:







					'''simple docstring'''







					lowerCamelCase    		=  min(int(num_inference_steps * strength	)		,      A	)

					lowerCamelCase    		=  max(num_inference_steps - init_timestep		,      0	)
					lowerCamelCase    		=  self.scheduler.timesteps[t_start:]

					return timesteps, num_inference_steps - t_start







		def 					__A   ( self		,      A		,      A		,      A		,      A		,      A		,      A=None	)  -> Tuple:







					'''simple docstring'''







					if not isinstance(A		,      (torch.Tensor, PIL.Image.Image, list)	):
								raise ValueError(
								    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(A	)}'	)

					lowerCamelCase    		=  image.to(device=A		,      dtype=A	)

					if isinstance(A		,      A	) and len(A	) != batch_size:
								raise ValueError(
								    F'You have passed a list of generators of length {len(A	)}, but requested an effective batch'
								    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'	)

					lowerCamelCase    		=  init_latents.shape
					lowerCamelCase    		=  randn_tensor(A		,      generator=A		,      device=A		,      dtype=A	)

					# get latents
					print("""add noise to latents at timestep"""		,      A	)
					lowerCamelCase    		=  self.scheduler.add_noise(A		,      A		,      A	)
					lowerCamelCase    		=  init_latents

					return latents







		@torch.no_grad()
		def __call__( self		,      A = None		,      A = 0.8		,      A = 1		,      A = None		,      A = 0.0		,      A = 50		,      A = None		,      A = "pil"		,      A = True		,      )  -> Union[ImagePipelineOutput, Tuple]:







					'''simple docstring'''







					self.check_inputs(A	)

					# 2. Preprocess image
					lowerCamelCase    		=  preprocess(A	)

					# 3. set timesteps
					self.scheduler.set_timesteps(A		,      device=self.device	)
					lowerCamelCase		,		lowerCamelCase    		=  self.get_timesteps(A		,      A		,      self.device	)
					lowerCamelCase    		=  timesteps[:1].repeat(A	)

					# 4. Prepare latent variables
					lowerCamelCase    		=  self.prepare_latents(A		,      A		,      A		,      self.unet.dtype		,      self.device		,      A	)
					lowerCamelCase    		=  latents

					# 5. Denoising loop
					for t in self.progress_bar(A	):
								# 1. predict noise model_output
								lowerCamelCase    		=  self.unet(A		,      A	).sample

								# 2. predict previous mean of image x_t-1 and add variance depending on eta
								# eta corresponds to η in paper and should be between [0, 1]
								# do x_t -> x_t-1
								lowerCamelCase    		=  self.scheduler.step(
								    A		,      A		,      A		,      eta=A		,      use_clipped_model_output=A		,      generator=A		,      ).prev_sample

					lowerCamelCase    		=  (image / 2 + 0.5).clamp(0		,      1	)
					lowerCamelCase    		=  image.cpu().permute(0		,      2		,      3		,      1	).numpy()
					if output_type == "pil":
								lowerCamelCase    		=  self.numpy_to_pil(A	)

					if not return_dict:
								return (image, latent_timestep.item())

					return ImagePipelineOutput(images=A	)





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


UpperCAmelCase   :    str     =		logging.get_logger(__name__)

UpperCAmelCase   :    List[Any]     =		{
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[Any]			       =		"vit_mae"







		def __init__( self		,      A=7_68		,      A=12		,      A=12		,      A=30_72		,      A="gelu"		,      A=0.0		,      A=0.0		,      A=0.02		,      A=1e-1_2		,      A=2_24		,      A=16		,      A=3		,      A=True		,      A=16		,      A=5_12		,      A=8		,      A=20_48		,      A=0.75		,      A=False		,      **A		,      )  -> Dict:







					'''simple docstring'''







					super().__init__(**A	)

					lowerCamelCase    		=  hidden_size
					lowerCamelCase    		=  num_hidden_layers
					lowerCamelCase    		=  num_attention_heads
					lowerCamelCase    		=  intermediate_size
					lowerCamelCase    		=  hidden_act
					lowerCamelCase    		=  hidden_dropout_prob
					lowerCamelCase    		=  attention_probs_dropout_prob
					lowerCamelCase    		=  initializer_range
					lowerCamelCase    		=  layer_norm_eps
					lowerCamelCase    		=  image_size
					lowerCamelCase    		=  patch_size
					lowerCamelCase    		=  num_channels
					lowerCamelCase    		=  qkv_bias
					lowerCamelCase    		=  decoder_num_attention_heads
					lowerCamelCase    		=  decoder_hidden_size
					lowerCamelCase    		=  decoder_num_hidden_layers
					lowerCamelCase    		=  decoder_intermediate_size
					lowerCamelCase    		=  mask_ratio
					lowerCamelCase    		=  norm_pix_loss

