


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


lowerCamelCase		   =			transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def       a_ (       SCREAMING_SNAKE_CASE__   : Union[str, Any]	):
       '''simple docstring'''





       if isinstance(SCREAMING_SNAKE_CASE__						,     torch.Tensor	):
              return image
       elif isinstance(SCREAMING_SNAKE_CASE__						,     PIL.Image.Image	):
              _lowerCamelCase			:						Tuple            =[image]

       _lowerCamelCase			:						Tuple            =[trans(img.convert('RGB'	)	) for img in image]
       _lowerCamelCase			:						List[Any]            =torch.stack(SCREAMING_SNAKE_CASE__	)
       return image




class A    (  UpperCamelCase_	):






      def __init__(   self		:	Tuple     ,  lowercase_		:	Optional[int]     ,  lowercase_		:	List[str]					)   ->		Optional[int]:
             """simple docstring"""




             super().__init__()

             # make sure scheduler can always be converted to DDIM
             _lowerCamelCase			:						List[Any]            =DDIMScheduler.from_config(scheduler.config					)

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






      def 			lowerCamelCase			(   self		:	int     ,  lowercase_		:	int					)   ->		str:
             """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 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	List[Any]     ,  lowercase_		:	str     ,  lowercase_		:	int					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			:						str            =min(int(num_inference_steps * strength					)     ,  lowercase_					)

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

             return timesteps, num_inference_steps - t_start






      def 			lowerCamelCase			(   self		:	Any     ,  lowercase_		:	Tuple     ,  lowercase_		:	Union[str, Any]     ,  lowercase_		:	int     ,  lowercase_		:	Dict     ,  lowercase_		:	List[str]     ,  lowercase_		:	List[Any]=None					)   ->		Any:
             """simple docstring"""




             if not isinstance(lowercase_     ,  (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(lowercase_					)}'''					)

             _lowerCamelCase			:						Tuple            =image.to(device=lowercase_     ,  dtype=lowercase_					)

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

             _lowerCamelCase			:						Union[str, Any]            =init_latents.shape
             _lowerCamelCase			:						List[str]            =randn_tensor(lowercase_     ,  generator=lowercase_     ,  device=lowercase_     ,  dtype=lowercase_					)

             # get latents
             print('add noise to latents at timestep'     ,  lowercase_					)
             _lowerCamelCase			:						Optional[Any]            =self.scheduler.add_noise(lowercase_     ,  lowercase_     ,  lowercase_					)
             _lowerCamelCase			:						Optional[int]            =init_latents

             return latents






      @torch.no_grad()
      def __call__(   self		:	List[Any]     ,  lowercase_		:	Union[torch.FloatTensor, PIL.Image.Image] = None     ,  lowercase_		:	float = 0.8     ,  lowercase_		:	int = 1     ,  lowercase_		:	Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,  lowercase_		:	float = 0.0     ,  lowercase_		:	int = 50     ,  lowercase_		:	Optional[bool] = None     ,  lowercase_		:	Optional[str] = "pil"     ,  lowercase_		:	bool = True     ,  )   ->		Union[ImagePipelineOutput, Tuple]:
             """simple docstring"""




             self.check_inputs(lowercase_					)

             # 2. Preprocess image
             _lowerCamelCase			:						str            =preprocess(lowercase_					)

             # 3. set timesteps
             self.scheduler.set_timesteps(lowercase_     ,  device=self.device					)
             _lowerCamelCase      ,	_lowerCamelCase			:						Dict            =self.get_timesteps(lowercase_     ,  lowercase_     ,  self.device					)
             _lowerCamelCase			:						List[Any]            =timesteps[:1].repeat(lowercase_					)

             # 4. Prepare latent variables
             _lowerCamelCase			:						str            =self.prepare_latents(lowercase_     ,  lowercase_     ,  lowercase_     ,  self.unet.dtype     ,  self.device     ,  lowercase_					)
             _lowerCamelCase			:						Any            =latents

             # 5. Denoising loop
             for t in self.progress_bar(lowercase_					):
                    # 1. predict noise model_output
                    _lowerCamelCase			:						List[Any]            =self.unet(lowercase_     ,  lowercase_					).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			:						List[Any]            =self.scheduler.step(
                        lowercase_     ,  lowercase_     ,  lowercase_     ,  eta=lowercase_     ,  use_clipped_model_output=lowercase_     ,  generator=lowercase_     ,  ).prev_sample

             _lowerCamelCase			:						int            =(image / 2 + 0.5).clamp(0     ,  1					)
             _lowerCamelCase			:						List[Any]            =image.cpu().permute(0     ,  2     ,  3     ,  1					).numpy()
             if output_type == "pil":
                    _lowerCamelCase			:						Optional[int]            =self.numpy_to_pil(lowercase_					)

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

             return ImagePipelineOutput(images=lowercase_					)






import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


lowerCamelCase		   =			logging.get_logger(__name__)




class A    (  UpperCamelCase_	):






      def __init__(   self		:	Optional[Any]     ,  *lowercase_		:	str     ,  **lowercase_		:	List[Any]					)   ->		None:
             """simple docstring"""




             warnings.warn(
                 'The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
                 ' Please use MobileViTImageProcessor instead.'     ,  lowercase_     ,  )
             super().__init__(*lowercase_     ,  **lowercase_					)


