


"""simple docstring"""







from typing import TYPE_CHECKING

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


__lowercase    =				{
    """configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
    """configuration_data2vec_text""": [
        """DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecTextConfig""",
        """Data2VecTextOnnxConfig""",
    ],
    """configuration_data2vec_vision""": [
        """DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecVisionConfig""",
        """Data2VecVisionOnnxConfig""",
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    =				[
     """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """Data2VecAudioForAudioFrameClassification""",
     """Data2VecAudioForCTC""",
     """Data2VecAudioForSequenceClassification""",
     """Data2VecAudioForXVector""",
     """Data2VecAudioModel""",
     """Data2VecAudioPreTrainedModel""",
 ]
 __lowercase    =				[
     """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """Data2VecTextForCausalLM""",
     """Data2VecTextForMaskedLM""",
     """Data2VecTextForMultipleChoice""",
     """Data2VecTextForQuestionAnswering""",
     """Data2VecTextForSequenceClassification""",
     """Data2VecTextForTokenClassification""",
     """Data2VecTextModel""",
     """Data2VecTextPreTrainedModel""",
 ]
 __lowercase    =				[
     """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """Data2VecVisionForImageClassification""",
     """Data2VecVisionForMaskedImageModeling""",
     """Data2VecVisionForSemanticSegmentation""",
     """Data2VecVisionModel""",
     """Data2VecVisionPreTrainedModel""",
 ]

if is_tf_available():
 __lowercase    =				[
     """TFData2VecVisionForImageClassification""",
     """TFData2VecVisionForSemanticSegmentation""",
     """TFData2VecVisionModel""",
     """TFData2VecVisionPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
 from .configuration_dataavec_text import (
     DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecTextConfig,
     DataaVecTextOnnxConfig,
 )
 from .configuration_dataavec_vision import (
     DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecVisionConfig,
     DataaVecVisionOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_dataavec_audio import (
      DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecAudioForAudioFrameClassification,
      DataaVecAudioForCTC,
      DataaVecAudioForSequenceClassification,
      DataaVecAudioForXVector,
      DataaVecAudioModel,
      DataaVecAudioPreTrainedModel,
  )
  from .modeling_dataavec_text import (
      DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecTextForCausalLM,
      DataaVecTextForMaskedLM,
      DataaVecTextForMultipleChoice,
      DataaVecTextForQuestionAnswering,
      DataaVecTextForSequenceClassification,
      DataaVecTextForTokenClassification,
      DataaVecTextModel,
      DataaVecTextPreTrainedModel,
  )
  from .modeling_dataavec_vision import (
      DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecVisionForImageClassification,
      DataaVecVisionForMaskedImageModeling,
      DataaVecVisionForSemanticSegmentation,
      DataaVecVisionModel,
      DataaVecVisionPreTrainedModel,
  )
 if is_tf_available():
  from .modeling_tf_dataavec_vision import (
      TFDataaVecVisionForImageClassification,
      TFDataaVecVisionForSemanticSegmentation,
      TFDataaVecVisionModel,
      TFDataaVecVisionPreTrainedModel,
  )

else:
 import sys

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










"""simple docstring"""







import sys
import turtle


def 					lowercase      (    A_  ,       A_	)->				tuple[float, float]:
  '''simple docstring'''







  return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       )->				None:
  '''simple docstring'''







  my_pen.up()
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)
  my_pen.down()
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)

  if depth == 0:
    return

  triangle(A_  ,       get_mid(A_  ,       A_	)  ,       get_mid(A_  ,       A_	)  ,       depth - 1	)
  triangle(A_  ,       get_mid(A_  ,       A_	)  ,       get_mid(A_  ,       A_	)  ,       depth - 1	)
  triangle(A_  ,       get_mid(A_  ,       A_	)  ,       get_mid(A_  ,       A_	)  ,       depth - 1	)


if __name__ == "__main__":
 if len(sys.argv) != 2:
  raise ValueError(
      """Correct format for using this script: """
      """python fractals.py <int:depth_for_fractal>"""
  )
 __lowercase    =				turtle.Turtle()
 my_pen.ht()
 my_pen.speed(5)
 my_pen.pencolor("""red""")

 __lowercase    =				[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
 triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))






