



from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
A_		:     Dict        =     numpy.array([0, 0])
A_		:     Optional[int]        =     numpy.array([0.5, 0.8660254])
A_		:     List[Any]        =     numpy.array([1, 0])
A_		:     Tuple        =     [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]


def 	UpperCAmelCase__(      UpperCAmelCase__ :List[Any] ,					UpperCAmelCase__ :Union[str, Any]					):






 '''simple docstring'''

 a						=		initial_vectors
 for _ in range(lowerCAmelCase__					):
  a						=		iteration_step(lowerCAmelCase__					)
 return vectors


def 	UpperCAmelCase__(      UpperCAmelCase__ :Optional[int]					):






 '''simple docstring'''

 a						=		[]
 for i, start_vector in enumerate(vectors[:-1]					):
  a						=		vectors[i + 1]
  new_vectors.append(lowerCAmelCase__					)
  a						=		end_vector - start_vector
  new_vectors.append(start_vector + difference_vector / 3					)
  new_vectors.append(
      start_vector + difference_vector / 3 + rotate(difference_vector / 3 ,					60					)					)
  new_vectors.append(start_vector + difference_vector * 2 / 3					)
 new_vectors.append(vectors[-1]					)
 return new_vectors


def 	UpperCAmelCase__(      UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :List[str]					):






 '''simple docstring'''

 a						=		numpy.radians(lowerCAmelCase__					)
 a		,  a						=		numpy.cos(lowerCAmelCase__					), numpy.sin(lowerCAmelCase__					)
 a						=		numpy.array(((c, -s), (s, c))					)
 return numpy.dot(lowerCAmelCase__ ,					lowerCAmelCase__					)


def 	UpperCAmelCase__(      UpperCAmelCase__ :int					):






 '''simple docstring'''

 a						=		plt.gca()
 axes.set_aspect("equal"					)

 # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
 # y-coordinates as inputs, which are constructed from the vector-list using
 # zip()
 a		,  a						=		zip(*lowerCAmelCase__					)
 plt.plot(lowerCAmelCase__ ,					lowerCAmelCase__					)
 plt.show()


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     A_		:     List[Any]        =     iterate(INITIAL_VECTORS, 5)
     plot(processed_vectors)






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			42
   _UpperCAmelCase     	=			42

   def __init__(      self			:       Optional[Any]							,   __lowerCAmelCase			:       UNetaDModel							,   __lowerCAmelCase			:       ScoreSdeVeScheduler							)       ->				str:


    """simple docstring"""






    super().__init__()
    self.register_modules(unet=__lowerCAmelCase							,   scheduler=__lowerCAmelCase							)




   @torch.no_grad()
   def __call__(      self			:       int							,   __lowerCAmelCase			:       int = 1							,   __lowerCAmelCase			:       int = 2000							,   __lowerCAmelCase			:       Optional[Union[torch.Generator, List[torch.Generator]]] = None							,   __lowerCAmelCase			:       Optional[str] = "pil"							,   __lowerCAmelCase			:       bool = True							,   **__lowerCAmelCase			:       Any							,   )       ->				Union[ImagePipelineOutput, Tuple]:


    """simple docstring"""






    a						=		self.unet.config.sample_size
    a						=		(batch_size, 3, img_size, img_size)

    a						=		self.unet

    a						=		randn_tensor(__lowerCAmelCase							,   generator=__lowerCAmelCase							) * self.scheduler.init_noise_sigma
    a						=		sample.to(self.device							)

    self.scheduler.set_timesteps(__lowerCAmelCase							)
    self.scheduler.set_sigmas(__lowerCAmelCase							)

    for i, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
     a						=		self.scheduler.sigmas[i] * torch.ones(shape[0]							,   device=self.device							)

     # correction step
     for _ in range(self.scheduler.config.correct_steps							):
      a						=		self.unet(__lowerCAmelCase							,   __lowerCAmelCase							).sample
      a						=		self.scheduler.step_correct(__lowerCAmelCase							,   __lowerCAmelCase							,   generator=__lowerCAmelCase							).prev_sample

     # prediction step
     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							).sample
     a						=		self.scheduler.step_pred(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   generator=__lowerCAmelCase							)

     a		,  a						=		output.prev_sample, output.prev_sample_mean

    a						=		sample_mean.clamp(0							,   1							)
    a						=		sample.cpu().permute(0							,   2							,   3							,   1							).numpy()
    if output_type == "pil":
     a						=		self.numpy_to_pil(__lowerCAmelCase							)

    if not return_dict:
     return (sample,)

    return ImagePipelineOutput(images=__lowerCAmelCase							)

