



'''simple docstring'''



from __future__ import annotations

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

# initial triangle of Koch snowflake
lowerCAmelCase_     :       Dict								=						numpy.array([0, 0])
lowerCAmelCase_     :       int								=						numpy.array([0.5, 0.8_660_254])
lowerCAmelCase_     :       Union[str, Any]								=						numpy.array([1, 0])
lowerCAmelCase_     :       Tuple								=						[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]
def 					UpperCAmelCase   (						A  :				list[numpy.ndarray]					,		A  :				int				):
							SCREAMING_SNAKE_CASE					:      int     					=			initial_vectors
							for _ in range(A				):
														SCREAMING_SNAKE_CASE					:      Dict     					=			iteration_step(A				)
							return vectors
def 					UpperCAmelCase   (						A  :				list[numpy.ndarray]				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			[]
							for i, start_vector in enumerate(vectors[:-1]				):
														SCREAMING_SNAKE_CASE					:      Any     					=			vectors[i + 1]
														new_vectors.append(A				)
														SCREAMING_SNAKE_CASE					:      str     					=			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 					UpperCAmelCase   (						A  :				numpy.ndarray					,		A  :				float				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			numpy.radians(A				)
							SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Dict     					=			numpy.cos(A				), numpy.sin(A				)
							SCREAMING_SNAKE_CASE					:      Tuple     					=			numpy.array(((c, -s), (s, c))				)
							return numpy.dot(A					,		A				)
def 					UpperCAmelCase   (						A  :				list[numpy.ndarray]				):
							SCREAMING_SNAKE_CASE					:      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()
							SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[Any]     					=			zip(*A				)
							plt.plot(A					,		A				)
							plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

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









'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCAmelCase_     :       Tuple								=						{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       Dict								=						[
							    'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TapasForMaskedLM',
							    'TapasForQuestionAnswering',
							    'TapasForSequenceClassification',
							    'TapasModel',
							    'TapasPreTrainedModel',
							    'load_tf_weights_in_tapas',
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       str								=						[
							    'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFTapasForMaskedLM',
							    'TFTapasForQuestionAnswering',
							    'TFTapasForSequenceClassification',
							    'TFTapasModel',
							    'TFTapasPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
							from .tokenization_tapas import TapasTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tapas import (
														    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TapasForMaskedLM,
														    TapasForQuestionAnswering,
														    TapasForSequenceClassification,
														    TapasModel,
														    TapasPreTrainedModel,
														    load_tf_weights_in_tapas,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_tapas import (
														    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTapasForMaskedLM,
														    TFTapasForQuestionAnswering,
														    TFTapasForSequenceClassification,
														    TFTapasModel,
														    TFTapasPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase_     :       List[Any]								=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




