'''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  _UpperCamelCase (     _a	:	list[numpy.ndarray]				,     _a	:	int			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  initial_vectors
    for _ in range(_a			):
        __UpperCamelCase	:	Optional[int]		    =  iteration_step(_a			)
    return vectors







def  _UpperCamelCase (     _a	:	list[numpy.ndarray]			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  []
    for i, start_vector in enumerate(vectors[:-1]			):
        __UpperCamelCase	:	Union[str, Any]		    =  vectors[i + 1]
        new_vectors.append(_a			)
        __UpperCamelCase	:	Optional[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				,     6_0			)			)
        new_vectors.append(start_vector + difference_vector * 2 / 3			)
    new_vectors.append(vectors[-1]			)
    return new_vectors







def  _UpperCamelCase (     _a	:	numpy.ndarray				,     _a	:	float			):




    """simple docstring"""



    __UpperCamelCase	:	Any		    =  numpy.radians(_a			)
    __UpperCamelCase    ,					__UpperCamelCase	:	List[Any]		    =  numpy.cos(_a			), numpy.sin(_a			)
    __UpperCamelCase	:	List[Any]		    =  numpy.array(((c, -s), (s, c))			)
    return numpy.dot(_a				,     _a			)







def  _UpperCamelCase (     _a	:	list[numpy.ndarray]			):




    """simple docstring"""



    __UpperCamelCase	:	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	:	str		    =  zip(*_a			)
    plt.plot(_a				,     _a			)
    plt.show()


if __name__ == "__main__":
  import doctest

  doctest.testmod()

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




'''simple docstring'''
import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						LayoutLMTokenizer
     SCREAMING_SNAKE_CASE__		   =						LayoutLMTokenizerFast
     SCREAMING_SNAKE_CASE__		   =						True
     SCREAMING_SNAKE_CASE__		   =						True







     def lowerCAmelCase     (		self    ):
         super().setUp()

         __UpperCamelCase	:	List[str]		    =  [
             '[UNK]',
             '[CLS]',
             '[SEP]',
             'want',
             '##want',
             '##ed',
             'wa',
             'un',
             'runn',
             '##ing',
             ',',
             'low',
             'lowest',
         ]
         __UpperCamelCase	:	Union[str, Any]		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['vocab_file']    )
         with open(self.vocab_file     ,			'w'     ,			encoding='utf-8'    ) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]    )    )







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         return LayoutLMTokenizer.from_pretrained(self.tmpdirname     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  'UNwant\u00E9d,running'
         __UpperCamelCase	:	Union[str, Any]		    =  'unwanted, running'
         return input_text, output_text







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  self.tokenizer_class(self.vocab_file    )

         __UpperCamelCase	:	Optional[Any]		    =  tokenizer.tokenize('UNwant\u00E9d,running'    )
         self.assertListEqual(_lowerCamelCase     ,			['un', '##want', '##ed', ',', 'runn', '##ing']    )
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase    )     ,			[7, 4, 5, 1_0, 8, 9]    )







     def lowerCAmelCase     (		self    ):
         pass



