

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      _lowerCAmelCase(	__A			):
  if isinstance(__A   ,       torch.Tensor			):
    return image
  elif isinstance(__A   ,       PIL.Image.Image			):
    UpperCAmelCase					      =  [image]

  UpperCAmelCase					      =  [trans(img.convert("RGB"			)			) for img in image]
  UpperCAmelCase					      =  torch.stack(__A			)
  return image






class 						__magic_name__    (       _snake_case    ):


 def __init__(						self		: Optional[Any]			,						lowerCAmelCase__		: Optional[Any]			,						lowerCAmelCase__		: Any    )    ->      Optional[Any]:
   super().__init__()

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

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


 def _UpperCamelCase	(						self		: Tuple			,						lowerCAmelCase__		: str    )    ->      Dict:
   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		: Tuple			,						lowerCAmelCase__		: str			,						lowerCAmelCase__		: int			,						lowerCAmelCase__		: Optional[Any]    )    ->      Union[str, Any]:
   # get the original timestep using init_timestep
   UpperCAmelCase					      =  min(int(num_inference_steps * strength    )			,						lowerCAmelCase__    )

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

   return timesteps, num_inference_steps - t_start


 def _UpperCamelCase	(						self		: List[Any]			,						lowerCAmelCase__		: int			,						lowerCAmelCase__		: str			,						lowerCAmelCase__		: Optional[int]			,						lowerCAmelCase__		: Any			,						lowerCAmelCase__		: Any			,						lowerCAmelCase__		: Any=None    )    ->      Union[str, Any]:
   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__    )}"    )

   UpperCAmelCase					      =  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."    )

   UpperCAmelCase					      =  init_latents.shape
   UpperCAmelCase					      =  randn_tensor(lowerCAmelCase__			,						generator=lowerCAmelCase__			,						device=lowerCAmelCase__			,						dtype=lowerCAmelCase__    )

   # get latents
   print("add noise to latents at timestep"			,						lowerCAmelCase__    )
   UpperCAmelCase					      =  self.scheduler.add_noise(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )
   UpperCAmelCase					      =  init_latents

   return latents


 @torch.no_grad()
 def __call__(						self		: Tuple			,						lowerCAmelCase__		: Union[torch.FloatTensor, PIL.Image.Image] = None			,						lowerCAmelCase__		: float = 0.8			,						lowerCAmelCase__		: int = 1			,						lowerCAmelCase__		: Optional[Union[torch.Generator, List[torch.Generator]]] = None			,						lowerCAmelCase__		: float = 0.0			,						lowerCAmelCase__		: int = 5_0			,						lowerCAmelCase__		: Optional[bool] = None			,						lowerCAmelCase__		: Optional[str] = "pil"			,						lowerCAmelCase__		: bool = True			,						)    ->      Union[ImagePipelineOutput, Tuple]:
   self.check_inputs(lowerCAmelCase__    )

   # 2. Preprocess image
   UpperCAmelCase					      =  preprocess(lowerCAmelCase__    )

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

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

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

   UpperCAmelCase					      =  (image / 2 + 0.5).clamp(0			,						1    )
   UpperCAmelCase					      =  image.cpu().permute(0			,						2			,						3			,						1    ).numpy()
   if output_type == "pil":
     UpperCAmelCase					      =  self.numpy_to_pil(lowerCAmelCase__    )

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

   return ImagePipelineOutput(images=lowerCAmelCase__    )






def      _lowerCAmelCase(	__A   ,       __A			):
  return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def      _lowerCAmelCase(	__A   ,       __A=0			):
  return sorted(__A   ,       key=lambda __A			: x[column]			)
def      _lowerCAmelCase(	__A   ,       __A   ,       __A=float("inf"			)			):

  for i in range(points_counts - 1			):
    for j in range(i + 1   ,       __A			):
      UpperCAmelCase					      =  euclidean_distance_sqr(points[i]   ,       points[j]			)
      if current_dis < min_dis:
        UpperCAmelCase					      =  current_dis
  return min_dis
def      _lowerCAmelCase(	__A   ,       __A   ,       __A=float("inf"			)			):

  for i in range(min(6   ,       points_counts - 1			)   ,       __A			):
    for j in range(max(0   ,       i - 6			)   ,       __A			):
      UpperCAmelCase					      =  euclidean_distance_sqr(points[i]   ,       points[j]			)
      if current_dis < min_dis:
        UpperCAmelCase					      =  current_dis
  return min_dis
def      _lowerCAmelCase(	__A   ,       __A   ,       __A			):

  # base case
  if points_counts <= 3:
    return dis_between_closest_pair(__A   ,       __A			)

  # recursion
  UpperCAmelCase					      =  points_counts // 2
  UpperCAmelCase					      =  closest_pair_of_points_sqr(
      __A   ,       points_sorted_on_y[:mid]   ,       __A			)
  UpperCAmelCase					      =  closest_pair_of_points_sqr(
      __A   ,       points_sorted_on_y[mid:]   ,       points_counts - mid			)
  UpperCAmelCase					      =  min(__A   ,       __A			)
  UpperCAmelCase					      =  []
  for point in points_sorted_on_x:
    if abs(point[0] - points_sorted_on_x[mid][0]			) < closest_pair_dis:
      cross_strip.append(__A			)

  UpperCAmelCase					      =  dis_between_closest_in_strip(
      __A   ,       len(__A			)   ,       __A			)
  return min(__A   ,       __A			)
def      _lowerCAmelCase(	__A   ,       __A			):
  UpperCAmelCase					      =  column_based_sort(__A   ,       column=0			)
  UpperCAmelCase					      =  column_based_sort(__A   ,       column=1			)
  return (
      closest_pair_of_points_sqr(
          __A   ,       __A   ,       __A			)
  ) ** 0.5


if __name__ == "__main__":
      lowerCAmelCase__       = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
      print("Distance:", closest_pair_of_points(points, len(points)))



