






"""simple docstring"""






from __future__ import annotations

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

# initial triangle of Koch snowflake
UpperCAmelCase_     :				Tuple         =				numpy.array([0, 0])
UpperCAmelCase_     :				int         =				numpy.array([0.5, 0.8_6_6_0_2_5_4])
UpperCAmelCase_     :				Tuple         =				numpy.array([1, 0])
UpperCAmelCase_     :				int         =				[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]



def       _A    (__a					,       __a							)				-> list[numpy.ndarray]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						int							=	initial_vectors
    for _ in range(__a							):
        SCREAMING_SNAKE_CASE_   :						str							=	iteration_step(__a							)
    return vectors



def       _A    (__a							)				-> list[numpy.ndarray]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Any							=	[]
    for i, start_vector in enumerate(vectors[:-1]							):
        SCREAMING_SNAKE_CASE_   :						List[str]							=	vectors[i + 1]
        new_vectors.append(__a							)
        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	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       _A    (__a					,       __a							)				-> numpy.ndarray:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						List[Any]							=	numpy.radians(__a							)
    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						int							=	numpy.cos(__a							), numpy.sin(__a							)
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	numpy.array(((c, -s), (s, c))							)
    return numpy.dot(__a					,       __a							)



def       _A    (__a							)				-> None:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Dict							=	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()
    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						List[str]							=	zip(*__a							)
    plt.plot(__a					,       __a							)
    plt.show()


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCAmelCase_     :				int         =				iterate(INITIAL_VECTORS, 5)
  plot(processed_vectors)










"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      @property
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	UNetaDModel(
              block_out_channels=(32, 64)		,			layers_per_block=2		,			sample_size=32		,			in_channels=3		,			out_channels=3		,			down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')		,			up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')		,			)
          return model



      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	self.dummy_uncond_unet
          SCREAMING_SNAKE_CASE_   :						int							=	ScoreSdeVeScheduler()

          SCREAMING_SNAKE_CASE_   :						List[str]							=	ScoreSdeVePipeline(unet=lowercase_		,			scheduler=lowercase_)
          sde_ve.to(lowercase_)
          sde_ve.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Dict							=	sde_ve(num_inference_steps=2		,			output_type='''numpy'''		,			generator=lowercase_).images

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	sde_ve(num_inference_steps=2		,			output_type='''numpy'''		,			generator=lowercase_		,			return_dict=lowercase_)[
              0
          ]

          SCREAMING_SNAKE_CASE_   :						Dict							=	image[0, -3:, -3:, -1]
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          SCREAMING_SNAKE_CASE_   :						str							=	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2




@slow
@require_torch
class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	'''google/ncsnpp-church-256'''
          SCREAMING_SNAKE_CASE_   :						str							=	UNetaDModel.from_pretrained(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	ScoreSdeVeScheduler.from_pretrained(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	ScoreSdeVePipeline(unet=lowercase_		,			scheduler=lowercase_)
          sde_ve.to(lowercase_)
          sde_ve.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	sde_ve(num_inference_steps=10		,			output_type='''numpy'''		,			generator=lowercase_).images

          SCREAMING_SNAKE_CASE_   :						Any							=	image[0, -3:, -3:, -1]

          assert image.shape == (1, 256, 256, 3)

          SCREAMING_SNAKE_CASE_   :						int							=	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


