'''simple docstring'''






import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing




class    a (       __lowerCAmelCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_ = "▁"   ,    snake_case_ = True   ,    snake_case_ = "<unk>"   ,    snake_case_ = "</s>"   ,    snake_case_ = "<pad>"   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= {
        """pad""": {"""id""": 0, """token""": pad_token},
        """eos""": {"""id""": 1, """token""": eos_token},
        """unk""": {"""id""": 2, """token""": unk_token},
    }

    __UpperCAmelCase:					Tuple		= [None] * len(self.special_tokens					)
    for token_dict in self.special_tokens.values():
      __UpperCAmelCase:					Any		= token_dict["""token"""]

    __UpperCAmelCase:					List[Any]		= Tokenizer(Unigram()					)

    __UpperCAmelCase:					Optional[Any]		= normalizers.Sequence(
        [
            normalizers.Nmt(),
            normalizers.NFKC(),
            normalizers.Replace(Regex(""" {2,}"""					)   ,    """ """					),
            normalizers.Lowercase(),
        ]					)
    __UpperCAmelCase:					Dict		= pre_tokenizers.Sequence(
        [
            pre_tokenizers.Metaspace(replacement=snake_case_   ,    add_prefix_space=snake_case_					),
            pre_tokenizers.Digits(individual_digits=snake_case_					),
            pre_tokenizers.Punctuation(),
        ]					)
    __UpperCAmelCase:					Union[str, Any]		= decoders.Metaspace(replacement=snake_case_   ,    add_prefix_space=snake_case_					)

    __UpperCAmelCase:					List[str]		= TemplateProcessing(
        single=F'''$A {self.special_tokens['eos']['token']}'''   ,    special_tokens=[(self.special_tokens["""eos"""]["""token"""], self.special_tokens["""eos"""]["""id"""])]   ,    )

    __UpperCAmelCase:					Tuple		= {
        """model""": """SentencePieceUnigram""",
        """replacement""": replacement,
        """add_prefix_space""": add_prefix_space,
    }

    super().__init__(snake_case_   ,    snake_case_					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_ = 8000   ,    snake_case_ = True   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= trainers.UnigramTrainer(
        vocab_size=snake_case_   ,    special_tokens=self.special_tokens_list   ,    show_progress=snake_case_   ,    )

    if isinstance(snake_case_   ,    snake_case_					):
      __UpperCAmelCase:					Optional[int]		= [files]
    self._tokenizer.train(snake_case_   ,    trainer=snake_case_					)

    self.add_unk_id()







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_ = 8000   ,    snake_case_ = True   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					int		= trainers.UnigramTrainer(
        vocab_size=snake_case_   ,    special_tokens=self.special_tokens_list   ,    show_progress=snake_case_   ,    )

    self._tokenizer.train_from_iterator(snake_case_   ,    trainer=snake_case_					)

    self.add_unk_id()







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= json.loads(self._tokenizer.to_str()					)

    __UpperCAmelCase:					List[str]		= self.special_tokens["""unk"""]["""id"""]

    __UpperCAmelCase:					Optional[int]		= Tokenizer.from_str(json.dumps(snake_case_					)					)


'''simple docstring'''






from __future__ import annotations

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

# initial triangle of Koch snowflake
SCREAMING_SNAKE_CASE_	   =			numpy.array([0, 0])
SCREAMING_SNAKE_CASE_	   =			numpy.array([0.5, 0.866_0254])
SCREAMING_SNAKE_CASE_	   =			numpy.array([1, 0])
SCREAMING_SNAKE_CASE_	   =			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]






def  UpperCamelCase__					(					_lowercase    :				list[numpy.ndarray]   ,    _lowercase    :				int     )							->	list[numpy.ndarray]:
  __UpperCAmelCase:					int		= initial_vectors
  for _ in range(_lowercase     ):
    __UpperCAmelCase:					Any		= iteration_step(_lowercase     )
  return vectors






def  UpperCamelCase__					(					_lowercase    :				list[numpy.ndarray]     )							->	list[numpy.ndarray]:
  __UpperCAmelCase:					Optional[int]		= []
  for i, start_vector in enumerate(vectors[:-1]     ):
    __UpperCAmelCase:					Union[str, Any]		= vectors[i + 1]
    new_vectors.append(_lowercase     )
    __UpperCAmelCase:					Optional[int]		= end_vector - start_vector
    new_vectors.append(start_vector + difference_vector / 3     )
    new_vectors.append(
        start_vector + difference_vector / 3 + rotate(difference_vector / 3   ,    6_0     )     )
    new_vectors.append(start_vector + difference_vector * 2 / 3     )
  new_vectors.append(vectors[-1]     )
  return new_vectors






def  UpperCamelCase__					(					_lowercase    :				numpy.ndarray   ,    _lowercase    :				float     )							->	numpy.ndarray:
  __UpperCAmelCase:					Tuple		= numpy.radians(_lowercase     )
  __UpperCAmelCase, __UpperCAmelCase:					Optional[Any]		= numpy.cos(_lowercase     ), numpy.sin(_lowercase     )
  __UpperCAmelCase:					Tuple		= numpy.array(((c, -s), (s, c))     )
  return numpy.dot(_lowercase   ,    _lowercase     )






def  UpperCamelCase__					(					_lowercase    :				list[numpy.ndarray]     )							->	None:
  __UpperCAmelCase:					Union[str, Any]		= 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:					Dict		= zip(*_lowercase     )
  plt.plot(_lowercase   ,    _lowercase     )
  plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

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