





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, 0.8_6_6_0_2_5_4])
_SCREAMING_SNAKE_CASE      	=		numpy.array([1, 0])
_SCREAMING_SNAKE_CASE      	=		[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			Any											=	initial_vectors
	for _ in range(__a   ):
		snake_case_		:			List[str]											=	iteration_step(__a   )
	return vectors


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			List[Any]											=	[]
	for i, start_vector in enumerate(vectors[:-1]   ):
		snake_case_		:			Tuple											=	vectors[i + 1]
		new_vectors.append(__a   )
		snake_case_		:			List[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 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	snake_case_		:			Union[str, Any]											=	numpy.radians(__a   )
	snake_case_		:			Dict											=	numpy.cos(__a   ), numpy.sin(__a   )
	snake_case_		:			Union[str, Any]											=	numpy.array(((c, -s), (s, c))   )
	return numpy.dot(__a   , __a   )


def 							SCREAMING_SNAKE_CASE__						(    __a   ):
	snake_case_		:			Dict											=	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()
	snake_case_		:			Optional[int]											=	zip(*__a   )
	plt.plot(__a   , __a   )
	plt.show()


if __name__ == "__main__":
						import doctest

						doctest.testmod()

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








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE      	=		{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """SwinForImageClassification""",
						    """SwinForMaskedImageModeling""",
						    """SwinModel""",
						    """SwinPreTrainedModel""",
						    """SwinBackbone""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFSwinForImageClassification""",
						    """TFSwinForMaskedImageModeling""",
						    """TFSwinModel""",
						    """TFSwinPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swin import (
												    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwinBackbone,
												    SwinForImageClassification,
												    SwinForMaskedImageModeling,
												    SwinModel,
												    SwinPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_swin import (
												    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFSwinForImageClassification,
												    TFSwinForMaskedImageModeling,
												    TFSwinModel,
												    TFSwinPreTrainedModel,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE      	=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

