





'''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


_SCREAMING_SNAKE_CASE					=    transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def    _lowerCAmelCase						(				lowerCamelCase_			:  Union[str, Any]				):
			if isinstance(lowerCamelCase_		,       torch.Tensor				):
						return image
			elif isinstance(lowerCamelCase_		,       PIL.Image.Image				):
						__lowercase										=							[image]

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






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase )							->							Any:







				'''simple docstring'''





				super().__init__()

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

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


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Any:







				'''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     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							Dict:







				'''simple docstring'''





				__lowercase										=							min(int(num_inference_steps * strength )     ,_lowerCamelCase )

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

				return timesteps, num_inference_steps - t_start


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase=None )							->							str:







				'''simple docstring'''





				if not isinstance(_lowerCamelCase     ,(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(_lowerCamelCase )}" )

				__lowercase										=							image.to(device=_lowerCamelCase     ,dtype=_lowerCamelCase )

				if isinstance(_lowerCamelCase     ,_lowerCamelCase ) and len(_lowerCamelCase ) != batch_size:
							raise ValueError(
							    f"You have passed a list of generators of length {len(_lowerCamelCase )}, 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(_lowerCamelCase     ,generator=_lowerCamelCase     ,device=_lowerCamelCase     ,dtype=_lowerCamelCase )

				# get latents
				print('''add noise to latents at timestep'''     ,_lowerCamelCase )
				__lowercase										=							self.scheduler.add_noise(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )
				__lowercase										=							init_latents

				return latents


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







				'''simple docstring'''





				self.check_inputs(_lowerCamelCase )

				# 2. Preprocess image
				__lowercase										=							preprocess(_lowerCamelCase )

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

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

				# 5. Denoising loop
				for t in self.progress_bar(_lowerCamelCase ):
							# 1. predict noise model_output
							__lowercase										=							self.unet(_lowerCamelCase     ,_lowerCamelCase ).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(
							    _lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,eta=_lowerCamelCase     ,use_clipped_model_output=_lowerCamelCase     ,generator=_lowerCamelCase     ,).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(_lowerCamelCase )

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

				return ImagePipelineOutput(images=_lowerCamelCase )









'''simple docstring'''



# flake8: noqa
# Lint as: python3
_SCREAMING_SNAKE_CASE					=    [
    '''VerificationMode''',
    '''Version''',
    '''disable_progress_bar''',
    '''enable_progress_bar''',
    '''is_progress_bar_enabled''',
    '''experimental''',
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental





