






from __future__ import annotations

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

# initial triangle of Koch snowflake
__magic_name__   :  int        =							numpy.array([0, 0])
__magic_name__   :  Any        =							numpy.array([0.5, 0.866_0254])
__magic_name__   :  Dict        =							numpy.array([1, 0])
__magic_name__   :  Dict        =							[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			list[numpy.ndarray]:
      """simple docstring"""

      UpperCamelCase					       =  initial_vectors
      for _ in range(_UpperCamelCase):
            UpperCamelCase					       =  iteration_step(_UpperCamelCase)
      return vectors
def 		lowercase__  (				_UpperCamelCase) ->			list[numpy.ndarray]:
      """simple docstring"""

      UpperCamelCase					       =  []
      for i, start_vector in enumerate(vectors[:-1]):
            UpperCamelCase					       =  vectors[i + 1]
            new_vectors.append(_UpperCamelCase)
            UpperCamelCase					       =  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 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			numpy.ndarray:
      """simple docstring"""

      UpperCamelCase					       =  numpy.radians(_UpperCamelCase)
      UpperCamelCase       ,  UpperCamelCase					       =  numpy.cos(_UpperCamelCase), numpy.sin(_UpperCamelCase)
      UpperCamelCase					       =  numpy.array(((c, -s), (s, c)))
      return numpy.dot(_UpperCamelCase       ,							_UpperCamelCase)
def 		lowercase__  (				_UpperCamelCase) ->			None:
      """simple docstring"""

      UpperCamelCase					       =  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()
      UpperCamelCase       ,  UpperCamelCase					       =  zip(*_UpperCamelCase)
      plt.plot(_UpperCamelCase       ,							_UpperCamelCase)
      plt.show()


if __name__ == "__main__":
       import doctest

       doctest.testmod()

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











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__magic_name__   :  Tuple        =							{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __magic_name__   :  Optional[Any]        =							['''PLBartTokenizer''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __magic_name__   :  Tuple        =							[
           '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''PLBartForCausalLM''',
           '''PLBartForConditionalGeneration''',
           '''PLBartForSequenceClassification''',
           '''PLBartModel''',
           '''PLBartPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_plbart import PLBartTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_plbart import (
                  PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PLBartForCausalLM,
                  PLBartForConditionalGeneration,
                  PLBartForSequenceClassification,
                  PLBartModel,
                  PLBartPreTrainedModel,
              )


else:
       import sys

       __magic_name__   :  Optional[int]        =							_LazyModule(__name__, globals()['''__file__'''], _import_structure)



