
"""simple docstring"""





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


_a								=						transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



def  lowerCamelCase__						(     __snake_case      )				->						Optional[Any]:


					"""simple docstring"""





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

					_UpperCamelCase									=    [trans(img.convert('''RGB'''      )      ) for img in image]
					_UpperCamelCase									=    torch.stack(__snake_case      )
					return image






class        _UpperCAmelCase(       lowerCamelCase      ):



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






							'''simple docstring'''
							super().__init__()

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

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



		def 		UpperCAmelCase				(   self							,						__a)			->			Tuple:






							'''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 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Dict:






							'''simple docstring'''
							# get the original timestep using init_timestep
							_UpperCamelCase									=    min(int(num_inference_steps * strength)							,						__a)

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

							return timesteps, num_inference_steps - t_start



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a=None)			->			int:






							'''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)}''')

							_UpperCamelCase									=    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.''')

							_UpperCamelCase									=    init_latents.shape
							_UpperCamelCase									=    randn_tensor(__a							,						generator=__a							,						device=__a							,						dtype=__a)

							# get latents
							print('''add noise to latents at timestep'''							,						__a)
							_UpperCamelCase									=    self.scheduler.add_noise(__a							,						__a							,						__a)
							_UpperCamelCase									=    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
							_UpperCamelCase									=    preprocess(__a)

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

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

							# 5. Denoising loop
							for t in self.progress_bar(__a):
												# 1. predict noise model_output
												_UpperCamelCase									=    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
												_UpperCamelCase									=    self.scheduler.step(
												    __a							,						__a							,						__a							,						eta=__a							,						use_clipped_model_output=__a							,						generator=__a							,						).prev_sample

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

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

							return ImagePipelineOutput(images=__a)













'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


__UpperCamelCase					    =	logging.get_logger(__name__)




class  _A (					__lowercase		):

		def __init__(   self					:   int  ,      *__magic_name__					:   Optional[Any]  ,      **__magic_name__					:   Any			)					->    None:


					"""simple docstring"""

					warnings.warn(
					    """The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
					    """ use OwlViTImageProcessor instead."""  ,      __magic_name__  ,      )
					super().__init__(*__magic_name__  ,      **__magic_name__			)


