

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

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

# initial triangle of Koch snowflake
lowerCamelCase			:				List[Any] 			=					numpy.array([0, 0])
lowerCamelCase			:				Tuple 			=					numpy.array([0.5, 0.8_6_6_0_2_5_4])
lowerCamelCase			:				Any 			=					numpy.array([1, 0])
lowerCamelCase			:				str 			=					[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]


def 	_lowerCAmelCase (						_UpperCamelCase						:					list[numpy.ndarray]							,			_UpperCamelCase						:					int    )				->      list[numpy.ndarray]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =initial_vectors
     for _ in range(_UpperCamelCase    ):
          _SCREAMING_SNAKE_CASE							   =iteration_step(_UpperCamelCase    )
     return vectors


def 	_lowerCAmelCase (						_UpperCamelCase						:					list[numpy.ndarray]    )				->      list[numpy.ndarray]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =[]
     for i, start_vector in enumerate(vectors[:-1]    ):
          _SCREAMING_SNAKE_CASE							   =vectors[i + 1]
          new_vectors.append(_UpperCamelCase    )
          _SCREAMING_SNAKE_CASE							   =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						:					numpy.ndarray							,			_UpperCamelCase						:					float    )				->      numpy.ndarray:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =numpy.radians(_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =numpy.cos(_UpperCamelCase    ), numpy.sin(_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =numpy.array(((c, -s), (s, c))    )
     return numpy.dot(_UpperCamelCase							,			_UpperCamelCase    )


def 	_lowerCAmelCase (						_UpperCamelCase						:					list[numpy.ndarray]    )				->      None:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =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							   =zip(*_UpperCamelCase    )
     plt.plot(_UpperCamelCase							,			_UpperCamelCase    )
     plt.show()


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     lowerCamelCase			:				Any 			=					iterate(INITIAL_VECTORS, 5)
     plot(processed_vectors)



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase			:				Tuple 			=					{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Optional[int] 			=					["BlenderbotTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Tuple 			=					[
         "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BlenderbotForCausalLM",
         "BlenderbotForConditionalGeneration",
         "BlenderbotModel",
         "BlenderbotPreTrainedModel",
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Tuple 			=					[
         "TFBlenderbotForConditionalGeneration",
         "TFBlenderbotModel",
         "TFBlenderbotPreTrainedModel",
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				str 			=					[
         "FlaxBlenderbotForConditionalGeneration",
         "FlaxBlenderbotModel",
         "FlaxBlenderbotPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_blenderbot import (
         BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         BlenderbotConfig,
         BlenderbotOnnxConfig,
     )
     from .tokenization_blenderbot import BlenderbotTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_blenderbot import (
              BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BlenderbotForCausalLM,
              BlenderbotForConditionalGeneration,
              BlenderbotModel,
              BlenderbotPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_blenderbot import (
              TFBlenderbotForConditionalGeneration,
              TFBlenderbotModel,
              TFBlenderbotPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_blenderbot import (
              FlaxBlenderbotForConditionalGeneration,
              FlaxBlenderbotModel,
              FlaxBlenderbotPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase			:				Dict 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
