






from __future__ import annotations

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

# initial triangle of Koch snowflake
UpperCAmelCase__				   =		numpy.array([0, 0])
UpperCAmelCase__				   =		numpy.array([0.5, 0.866_0254])
UpperCAmelCase__				   =		numpy.array([1, 0])
UpperCAmelCase__				   =		[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def 						_a    (      a :list[numpy.ndarray]	,							a :int )							->			list[numpy.ndarray]:
 a		  =      initial_vectors
 for _ in range(a ):
  a		  =      iteration_step(a )
 return vectors
def 						_a    (      a :list[numpy.ndarray] )							->			list[numpy.ndarray]:
 a		  =      []
 for i, start_vector in enumerate(vectors[:-1] ):
  a		  =      vectors[i + 1]
  new_vectors.append(a )
  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 						_a    (      a :numpy.ndarray	,							a :float )							->			numpy.ndarray:
 a		  =      numpy.radians(a )
 a  ,     a		  =      numpy.cos(a ), numpy.sin(a )
 a		  =      numpy.array(((c, -s), (s, c)) )
 return numpy.dot(a	,							a )
def 						_a    (      a :list[numpy.ndarray] )							->			None:
 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(*a )
 plt.plot(a	,							a )
 plt.show()


if __name__ == "__main__":
     import doctest

     doctest.testmod()

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





import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Union[str, Any]:





    '''simple docstring'''






    return EnvironmentCommand()


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @staticmethod
    def 						snake_case__     (      lowercase_ :       ArgumentParser		)->    Dict:





        '''simple docstring'''







        A__          =   parser.add_parser('env'		)
        download_parser.set_defaults(func=lowercase_		)


    def 						snake_case__     (      self :       List[Any]		)->    List[str]:





        '''simple docstring'''







        A__          =   huggingface_hub.__version__

        A__          =   'not installed'
        A__          =   'NA'
        if is_torch_available():
            import torch

            A__          =   torch.__version__
            A__          =   torch.cuda.is_available()

        A__          =   'not installed'
        if is_transformers_available():
            import transformers

            A__          =   transformers.__version__

        A__          =   'not installed'
        if is_accelerate_available():
            import accelerate

            A__          =   accelerate.__version__

        A__          =   'not installed'
        if is_xformers_available():
            import xformers

            A__          =   xformers.__version__

        A__          =   {
            '`diffusers` version': version,
            'Platform': platform.platform(),
            'Python version': platform.python_version(),
            'PyTorch version (GPU?)': F'{pt_version} ({pt_cuda_available})',
            'Huggingface_hub version': hub_version,
            'Transformers version': transformers_version,
            'Accelerate version': accelerate_version,
            'xFormers version': xformers_version,
            'Using GPU in script?': '<fill in>',
            'Using distributed or parallel set-up in script?': '<fill in>',
        }

        print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'		)
        print(self.format_dict(lowercase_		)		)

        return info


    @staticmethod
    def 						snake_case__     (      lowercase_ :       int		)->    Optional[Any]:





        '''simple docstring'''







        return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]		) + "\n"

