






"""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


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


def      UpperCamelCase       ( _A )     ->       str:
				if isinstance(_A  ,				torch.Tensor ):
								return image
				elif isinstance(_A  ,				PIL.Image.Image ):
								lowercase    :      Tuple       = [image]

				lowercase    :      Tuple       = [trans(img.convert("""RGB""" ) ) for img in image]
				lowercase    :      Dict       = torch.stack(_A )
				return image


class      UpperCamelCase		(__snake_case     ):






						def __init__(		self  :List[Any]			, __magic_name__  :Dict			, __magic_name__  :int				)      ->int:
										super().__init__()

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

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






						def      __snake_case      (		self  :str			, __magic_name__  :int				)      ->str:
										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  :str			, __magic_name__  :Dict			, __magic_name__  :List[str]			, __magic_name__  :Optional[Any]				)      ->Any:
										# get the original timestep using init_timestep
										lowercase    :      Tuple       = min(int(num_inference_steps * strength				)			, __magic_name__				)

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

										return timesteps, num_inference_steps - t_start






						def      __snake_case      (		self  :Any			, __magic_name__  :Optional[Any]			, __magic_name__  :List[str]			, __magic_name__  :Union[str, Any]			, __magic_name__  :List[Any]			, __magic_name__  :Dict			, __magic_name__  :Tuple=None				)      ->Tuple:
										if not isinstance(__magic_name__			, (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(__magic_name__				)}"""				)

										lowercase    :      Optional[int]       = image.to(device=__magic_name__			, dtype=__magic_name__				)

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

										lowercase    :      int       = init_latents.shape
										lowercase    :      Tuple       = randn_tensor(__magic_name__			, generator=__magic_name__			, device=__magic_name__			, dtype=__magic_name__				)

										# get latents
										print("""add noise to latents at timestep"""			, __magic_name__				)
										lowercase    :      Tuple       = self.scheduler.add_noise(__magic_name__			, __magic_name__			, __magic_name__				)
										lowercase    :      Dict       = init_latents

										return latents






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

										# 2. Preprocess image
										lowercase    :      Tuple       = preprocess(__magic_name__				)

										# 3. set timesteps
										self.scheduler.set_timesteps(__magic_name__			, device=self.device				)
										lowercase      ,      lowercase    :      Union[str, Any]       = self.get_timesteps(__magic_name__			, __magic_name__			, self.device				)
										lowercase    :      str       = timesteps[:1].repeat(__magic_name__				)

										# 4. Prepare latent variables
										lowercase    :      str       = self.prepare_latents(__magic_name__			, __magic_name__			, __magic_name__			, self.unet.dtype			, self.device			, __magic_name__				)
										lowercase    :      Dict       = latents

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

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

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

										return ImagePipelineOutput(images=__magic_name__				)








"""simple docstring"""







def      UpperCamelCase       ( _A  ,				_A )     ->       None:
				lowercase    :      List[Any]       = len(_A )
				print("""The following activities are selected:""" )

				# The first activity is always selected
				lowercase    :      Optional[int]       = 0
				print(_A  ,				end=""",""" )

				# Consider rest of the activities
				for j in range(_A ):
								# If this activity has start time greater than
								# or equal to the finish time of previously
								# selected activity, then select it
								if start[j] >= finish[i]:
												print(_A  ,				end=""",""" )
												lowercase    :      str       = j


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowerCAmelCase            =	[1, 3, 0, 5, 8, 5]
				_lowerCAmelCase            =	[2, 4, 6, 7, 9, 9]
				print_max_activities(start, finish)
