






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, 0.866_0254])
__A					=	numpy.array([1, 0])
__A					=	[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]







def       lowercase__	(					A_:						list[numpy.ndarray]  ,      A_:						int		)	->				list[numpy.ndarray]:







       """simple docstring"""






       __UpperCAmelCase     				=initial_vectors
       for _ in range(A_		):
              __UpperCAmelCase     				=iteration_step(A_		)
       return vectors







def       lowercase__	(					A_:						list[numpy.ndarray]		)	->				list[numpy.ndarray]:







       """simple docstring"""






       __UpperCAmelCase     				=[]
       for i, start_vector in enumerate(vectors[:-1]		):
              __UpperCAmelCase     				=vectors[i + 1]
              new_vectors.append(A_		)
              __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__	(					A_:						numpy.ndarray  ,      A_:						float		)	->				numpy.ndarray:







       """simple docstring"""






       __UpperCAmelCase     				=numpy.radians(A_		)
       __UpperCAmelCase				, __UpperCAmelCase     				=numpy.cos(A_		), numpy.sin(A_		)
       __UpperCAmelCase     				=numpy.array(((c, -s), (s, c))		)
       return numpy.dot(A_  ,      A_		)







def       lowercase__	(					A_:						list[numpy.ndarray]		)	->				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(*A_		)
       plt.plot(A_  ,      A_		)
       plt.show()


if __name__ == "__main__":
   import doctest

   doctest.testmod()

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






import os
import sys

lowercase   =	os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowercase   =	[
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]



@add_start_docstrings(AutoConfig.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[Any]      ,					**UpperCAmelCase__					:   List[str]							)	->     List[str]:
							return AutoConfig.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoTokenizer.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[str]      ,					**UpperCAmelCase__					:   List[Any]							)	->     Any:
							return AutoTokenizer.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModel.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Union[str, Any]      ,					**UpperCAmelCase__					:   Dict							)	->     Union[str, Any]:
							return AutoModel.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForCausalLM.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[str]      ,					**UpperCAmelCase__					:   List[Any]							)	->     Optional[Any]:
							return AutoModelForCausalLM.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForMaskedLM.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Union[str, Any]      ,					**UpperCAmelCase__					:   Dict							)	->     int:
							return AutoModelForMaskedLM.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForSequenceClassification.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[Any]      ,					**UpperCAmelCase__					:   Dict							)	->     Dict:
							return AutoModelForSequenceClassification.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForQuestionAnswering.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Any      ,					**UpperCAmelCase__					:   List[str]							)	->     Union[str, Any]:
							return AutoModelForQuestionAnswering.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)





