





from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_lowerCamelCase      							=   typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_lowerCamelCase      							=   typing.Union[np.floataa, int, float]  # noqa: UP007




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       return np.sqrt(np.sum((np.asarray(lowercase_   ) - np.asarray(lowercase_   )) ** 2   )   )




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       return sum((va - va) ** 2 for va, va in zip(lowercase_			,     lowercase_   )   ) ** (1 / 2)


if __name__ == "__main__":




       def  __UpperCAmelCase(   ):
              from timeit import timeit

              print('''Without Numpy'''   )
              print(
                  timeit(
                      '''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'''			,     number=1_00_00			,     globals=globals()			,     )   )
              print('''With Numpy'''   )
              print(
                  timeit(
                      '''euclidean_distance([1, 2, 3], [4, 5, 6])'''			,     number=1_00_00			,     globals=globals()			,     )   )



       benchmark()








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((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def  __UpperCAmelCase(   lowercase_   ):
       if isinstance(lowercase_			,     torch.Tensor   ):
              return image
       elif isinstance(lowercase_			,     PIL.Image.Image   ):
              _lowerCamelCase				:  List[str]						=		[image]

       _lowerCamelCase				:  Optional[int]						=		[trans(img.convert('''RGB'''   )   ) for img in image]
       _lowerCamelCase				:  Dict						=		torch.stack(lowercase_   )
       return image







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__):



             """simple docstring"""





             super().__init__()

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

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


      def 					__snake_case      (		self   ,				a__):



             """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   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		min(int(num_inference_steps * strength)   ,				a__)

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

             return timesteps, num_inference_steps - t_start


      def 					__snake_case      (		self   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__=None):



             """simple docstring"""





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

             _lowerCamelCase				:  Any						=		image.to(device=a__   ,				dtype=a__)

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

             _lowerCamelCase				:  Any						=		init_latents.shape
             _lowerCamelCase				:  Tuple						=		randn_tensor(a__   ,				generator=a__   ,				device=a__   ,				dtype=a__)

             # get latents
             print('''add noise to latents at timestep'''   ,				a__)
             _lowerCamelCase				:  List[Any]						=		self.scheduler.add_noise(a__   ,				a__   ,				a__)
             _lowerCamelCase				:  List[Any]						=		init_latents

             return latents



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



             """simple docstring"""





             self.check_inputs(a__)

             # 2. Preprocess image
             _lowerCamelCase				:  Union[str, Any]						=		preprocess(a__)

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

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

             # 5. Denoising loop
             for t in self.progress_bar(a__):
                    # 1. predict noise model_output
                    _lowerCamelCase				:  Dict						=		self.unet(a__   ,				a__).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
                    _lowerCamelCase				:  Tuple						=		self.scheduler.step(
                        a__   ,				a__   ,				a__   ,				eta=a__   ,				use_clipped_model_output=a__   ,				generator=a__   ,				).prev_sample

             _lowerCamelCase				:  str						=		(image / 2 + 0.5).clamp(0   ,				1)
             _lowerCamelCase				:  Dict						=		image.cpu().permute(0   ,				2   ,				3   ,				1).numpy()
             if output_type == "pil":
                    _lowerCamelCase				:  Any						=		self.numpy_to_pil(a__)

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

             return ImagePipelineOutput(images=a__)

