



'''simple docstring'''
from __future__ import annotations

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

# initial triangle of Koch snowflake
snake_case_ :	List[str]         =  numpy.array([0, 0])
snake_case_ :	str         =  numpy.array([0.5, 0.8_66_02_54])
snake_case_ :	Optional[int]         =  numpy.array([1, 0])
snake_case_ :	List[Any]         =  [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase					:		Optional[int] 				=   initial_vectors
						for _ in range(_A							):
												_UpperCamelCase					:		Optional[int] 				=   iteration_step(_A							)
						return vectors






def A__    (     UpperCAmelCase_							):
						_UpperCamelCase					:		Any 				=   []
						for i, start_vector in enumerate(vectors[:-1]							):
												_UpperCamelCase					:		Union[str, Any] 				=   vectors[i + 1]
												new_vectors.append(_A							)
												_UpperCamelCase					:		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  ,  6_0							)							)
												new_vectors.append(start_vector + difference_vector * 2 / 3							)
						new_vectors.append(vectors[-1]							)
						return new_vectors






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase					:		Any 				=   numpy.radians(_A							)
						_UpperCamelCase				,			_UpperCamelCase					:		Optional[int] 				=   numpy.cos(_A							), numpy.sin(_A							)
						_UpperCamelCase					:		Dict 				=   numpy.array(((c, -s), (s, c))							)
						return numpy.dot(_A  ,  _A							)






def A__    (     UpperCAmelCase_							):
						_UpperCamelCase					:		Optional[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()

					snake_case_ :	List[str]         =  iterate(INITIAL_VECTORS, 5)
					plot(processed_vectors)



'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase          =  {
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''InstructBlipQFormerModel''',
						    '''InstructBlipPreTrainedModel''',
						    '''InstructBlipForConditionalGeneration''',
						    '''InstructBlipVisionModel''',
						]

if TYPE_CHECKING:
						from .configuration_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    InstructBlipConfig,
						    InstructBlipQFormerConfig,
						    InstructBlipVisionConfig,
						)
						from .processing_instructblip import InstructBlipProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_instructblip import (
												    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InstructBlipForConditionalGeneration,
												    InstructBlipPreTrainedModel,
												    InstructBlipQFormerModel,
												    InstructBlipVisionModel,
												)

else:
						import sys

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




