



"""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__					:						Optional[int]												=			transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def __lowercase    (							_a					):
				if isinstance(_a	,    torch.Tensor					):
								return image
				elif isinstance(_a	,    PIL.Image.Image					):
								snake_case_				:  int       =			[image]

				snake_case_				:  Union[str, Any]       =			[trans(img.convert('''RGB'''					)					) for img in image]
				snake_case_				:  int       =			torch.stack(_a					)
				return image






class  _UpperCAmelCase (    lowerCAmelCase__):

	def __init__(  self	: Tuple						,  lowercase_	: int						,  lowercase_	: Union[str, Any]       ):
					super().__init__()

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

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

	def 		_snake_case      (  self	: Tuple						,  lowercase_	: Optional[int]       ):
					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	: Optional[Any]						,  lowercase_	: Optional[int]						,  lowercase_	: Any						,  lowercase_	: Any       ):
					# get the original timestep using init_timestep
					snake_case_				:  List[Any]       =			min(int(num_inference_steps * strength       )						,  lowercase_       )

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

					return timesteps, num_inference_steps - t_start

	def 		_snake_case      (  self	: List[str]						,  lowercase_	: List[Any]						,  lowercase_	: Union[str, Any]						,  lowercase_	: Dict						,  lowercase_	: Optional[Any]						,  lowercase_	: Optional[Any]						,  lowercase_	: Dict=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_       )}"       )

					snake_case_				:  Optional[Any]       =			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."       )

					snake_case_				:  Dict       =			init_latents.shape
					snake_case_				:  List[Any]       =			randn_tensor(lowercase_						,  generator=lowercase_						,  device=lowercase_						,  dtype=lowercase_       )

					# get latents
					print('''add noise to latents at timestep'''						,  lowercase_       )
					snake_case_				:  Optional[Any]       =			self.scheduler.add_noise(lowercase_						,  lowercase_						,  lowercase_       )
					snake_case_				:  str       =			init_latents

					return latents






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

					# 2. Preprocess image
					snake_case_				:  str       =			preprocess(lowercase_       )

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

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

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

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

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

					return ImagePipelineOutput(images=lowercase_       )










"""simple docstring"""

def __lowercase    (							_a	,    _a					):
    snake_case_				:  str       =			[0 for i in range(r + 1					)]
    # nc0 = 1
    snake_case_				:  int       =			1
    for i in range(1	,    n + 1					):
        # to compute current row from previous row.
        snake_case_				:  Any       =			min(_a	,    _a					)
        while j > 0:
            c[j] += c[j - 1]
            j -= 1
    return c[r]






print(binomial_coefficient(n=10, r=5))





