



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


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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					) ->    List[str]:
			if isinstance(a_  ,					torch.Tensor					):
						return image
			elif isinstance(a_  ,					PIL.Image.Image					):
						lowercase__   					=			[image]

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




class 			_snake_case     (   a_):




				def __init__(  self   :  Optional[int],   __lowercase   :  List[Any],   __lowercase   :  Dict  ):
							super().__init__()

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

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




				def 							A__ (  self   :  Optional[Any],   __lowercase   :  Tuple  ):

							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   :  int,   __lowercase   :  Tuple,   __lowercase   :  Optional[Any],   __lowercase   :  Optional[Any]  ):
							lowercase__   					=			min(int(num_inference_steps * strength  ),   lowercase_  )

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

							return timesteps, num_inference_steps - t_start




				def 							A__ (  self   :  List[Any],   __lowercase   :  Dict,   __lowercase   :  str,   __lowercase   :  Optional[Any],   __lowercase   :  Optional[int],   __lowercase   :  str,   __lowercase   :  Optional[int]=None  ):

							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_  )}'''  )

							lowercase__   					=			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.'''  )

							lowercase__   					=			init_latents.shape
							lowercase__   					=			randn_tensor(lowercase_,   generator=lowercase_,   device=lowercase_,   dtype=lowercase_  )

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

							return latents




				@torch.no_grad()
				def __call__(  self   :  Dict,   __lowercase   :  List[str] = None,   __lowercase   :  List[Any] = 0.8,   __lowercase   :  Tuple = 1,   __lowercase   :  Union[str, Any] = None,   __lowercase   :  Any = 0.0,   __lowercase   :  Union[str, Any] = 50,   __lowercase   :  Optional[int] = None,   __lowercase   :  Optional[int] = "pil",   __lowercase   :  List[str] = True,   ):
							self.check_inputs(lowercase_  )

							# 2. Preprocess image
							lowercase__   					=			preprocess(lowercase_  )

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

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

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

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

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

							return ImagePipelineOutput(images=lowercase_  )









from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
							if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import (
							    VersatileDiffusionDualGuidedPipeline,
							    VersatileDiffusionImageVariationPipeline,
							    VersatileDiffusionPipeline,
							    VersatileDiffusionTextToImagePipeline,
							)
else:
							from .modeling_text_unet import UNetFlatConditionModel
							from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
							from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
							from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
							from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline




