"""simple docstring"""






from __future__ import annotations

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

# initial triangle of Koch snowflake
_A   					=    numpy.array([0, 0])
_A   					=    numpy.array([0.5, 0.8_660_254])
_A   					=    numpy.array([1, 0])
_A   					=    [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   list[numpy.ndarray]:
				SCREAMING_SNAKE_CASE__		  =				initial_vectors
				for _ in range(__UpperCAmelCase       ):
								SCREAMING_SNAKE_CASE__		  =				iteration_step(__UpperCAmelCase       )
				return vectors




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   list[numpy.ndarray]:
				SCREAMING_SNAKE_CASE__		  =				[]
				for i, start_vector in enumerate(vectors[:-1]       ):
								SCREAMING_SNAKE_CASE__		  =				vectors[i + 1]
								new_vectors.append(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   numpy.ndarray:
				SCREAMING_SNAKE_CASE__		  =				numpy.radians(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				numpy.cos(__UpperCAmelCase       ), numpy.sin(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				numpy.array(((c, -s), (s, c))       )
				return numpy.dot(__UpperCAmelCase  ,					__UpperCAmelCase       )




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   None:
				SCREAMING_SNAKE_CASE__		  =				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__		  =				zip(*__UpperCAmelCase       )
				plt.plot(__UpperCAmelCase  ,					__UpperCAmelCase       )
				plt.show()


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						_A   					=    iterate(INITIAL_VECTORS, 5)
						plot(processed_vectors)





"""simple docstring"""






import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


_A   					=    HUGGINGFACE_HUB_CACHE


_A   					=    'config.json'
_A   					=    'diffusion_pytorch_model.bin'
_A   					=    'diffusion_flax_model.msgpack'
_A   					=    'model.onnx'
_A   					=    'diffusion_pytorch_model.safetensors'
_A   					=    'weights.pb'
_A   					=    'https://huggingface.co'
_A   					=    default_cache_path
_A   					=    'diffusers_modules'
_A   					=    os.getenv('HF_MODULES_CACHE', os.path.join(hf_cache_home, 'modules'))
_A   					=    ['fp16', 'non-ema']
_A   					=    '.self_attn'




