
'''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


_lowercase			:  Any   							=	transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						if isinstance(__lowerCamelCase				,      torch.Tensor							):
												return image
						elif isinstance(__lowerCamelCase				,      PIL.Image.Image							):
												lowercase_     :					Any													=	[image]

						lowercase_     :					List[Any]													=	[trans(img.convert('''RGB'''							)							) for img in image]
						lowercase_     :					Union[str, Any]													=	torch.stack(__lowerCamelCase							)
						return image






class 			lowerCAmelCase__       (  A_					):






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											super().__init__()

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

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






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""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      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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

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

											return timesteps, num_inference_steps - t_start






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=None		):


											"""simple docstring"""





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

											lowercase_     :					str													=	image.to(device=snake_case__    ,							dtype=snake_case__		)

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

											lowercase_     :					Union[str, Any]													=	init_latents.shape
											lowercase_     :					Optional[int]													=	randn_tensor(snake_case__    ,							generator=snake_case__    ,							device=snake_case__    ,							dtype=snake_case__		)

											# get latents
											print('''add noise to latents at timestep'''    ,							snake_case__		)
											lowercase_     :					Dict													=	self.scheduler.add_noise(snake_case__    ,							snake_case__    ,							snake_case__		)
											lowercase_     :					Tuple													=	init_latents

											return latents






					@torch.no_grad()
					def __call__(       self    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = 0.8    ,							__SCREAMING_SNAKE_CASE = 1    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = 0.0    ,							__SCREAMING_SNAKE_CASE = 50    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = "pil"    ,							__SCREAMING_SNAKE_CASE = True    ,							):


											"""simple docstring"""





											self.check_inputs(snake_case__		)

											# 2. Preprocess image
											lowercase_     :					Dict													=	preprocess(snake_case__		)

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

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

											# 5. Denoising loop
											for t in self.progress_bar(snake_case__		):
																	# 1. predict noise model_output
																	lowercase_     :					Dict													=	self.unet(snake_case__    ,							snake_case__		).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_     :					Optional[int]													=	self.scheduler.step(
																	    snake_case__    ,							snake_case__    ,							snake_case__    ,							eta=snake_case__    ,							use_clipped_model_output=snake_case__    ,							generator=snake_case__    ,							).prev_sample

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

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

											return ImagePipelineOutput(images=snake_case__		)





'''simple docstring'''







from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			str				,				__SCREAMING_SNAKE_CASE :			Optional[str] = None							):



						"""simple docstring"""





						if version.parse(hfh.__version__							).release < version.parse('''0.11.0'''							).release:
												# old versions of hfh don't url-encode the file path
												lowercase_     :					int													=	quote(__SCREAMING_SNAKE_CASE							)
						return hfh.hf_hub_url(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE				,				repo_type='''dataset'''				,				revision=__SCREAMING_SNAKE_CASE							)



