"""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, 0.8660254])
SCREAMING_SNAKE_CASE									=						numpy.array([1, 0])
SCREAMING_SNAKE_CASE									=						[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    List[str]							=							initial_vectors
	for _ in range(SCREAMING_SNAKE_CASE_			):
		_lowercase :    List[str]							=							iteration_step(SCREAMING_SNAKE_CASE_			)
	return vectors


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    List[str]							=							[]
	for i, start_vector in enumerate(vectors[:-1]			):
		_lowercase :    Optional[int]							=							vectors[i + 1]
		new_vectors.append(SCREAMING_SNAKE_CASE_			)
		_lowercase :    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     ,60			)			)
		new_vectors.append(start_vector + difference_vector * 2 / 3			)
	new_vectors.append(vectors[-1]			)
	return new_vectors


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    str							=							numpy.radians(SCREAMING_SNAKE_CASE_			)
	_lowercase :    List[str]							=							numpy.cos(SCREAMING_SNAKE_CASE_			), numpy.sin(SCREAMING_SNAKE_CASE_			)
	_lowercase :    Dict							=							numpy.array(((c, -s), (s, c))			)
	return numpy.dot(SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_			)


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Tuple							=							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()
	_lowercase :    str							=							zip(*SCREAMING_SNAKE_CASE_			)
	plt.plot(SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_			)
	plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

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




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE									=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE									=						[
						    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BioGptForCausalLM',
						    'BioGptForTokenClassification',
						    'BioGptForSequenceClassification',
						    'BioGptModel',
						    'BioGptPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
						from .tokenization_biogpt import BioGptTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_biogpt import (
												    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BioGptForCausalLM,
												    BioGptForSequenceClassification,
												    BioGptForTokenClassification,
												    BioGptModel,
												    BioGptPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE									=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



