






'''simple docstring'''

import colorsys

from PIL import Image  # type: ignore



def 							snake_case					(     a_							: List[Any]    ,   a_							: Dict    ,   a_							: List[str]	)       ->				str:





						"""simple docstring"""

						UpperCamelCase_				:   Tuple					    =							x
						UpperCamelCase_				:   Optional[int]					    =							y
						for step in range(lowerCAmelCase__	):  # noqa: B007
												UpperCamelCase_				:   Union[str, Any]					    =							a * a - b * b + x
												UpperCamelCase_				:   List[str]					    =							2 * a * b + y
												UpperCamelCase_				:   Optional[int]					    =							a_new

												# divergence happens for all complex number with an absolute value
												# greater than 4
												if a * a + b * b > 4:
																		break
						return step / (max_step - 1)



def 							snake_case					(     a_							: List[str]	)       ->				Any:





						"""simple docstring"""

						if distance == 1:
												return (0, 0, 0)
						else:
												return (255, 255, 255)



def 							snake_case					(     a_							: Dict	)       ->				Optional[Any]:





						"""simple docstring"""

						if distance == 1:
												return (0, 0, 0)
						else:
												return tuple(round(i * 255	) for i in colorsys.hsv_to_rgb(lowerCAmelCase__    ,   1    ,   1	)	)



def 							snake_case					(     a_							: Optional[int] = 800    ,   a_							: Optional[int] = 600    ,   a_							: Dict = -0.6    ,   a_							: List[Any] = 0    ,   a_							: Optional[Any] = 3.2    ,   a_							: Union[str, Any] = 50    ,   a_							: List[str] = True    ,   )       ->				Dict:





						"""simple docstring"""

						UpperCamelCase_				:   Union[str, Any]					    =							Image.new("""RGB"""    ,   (image_width, image_height)	)
						UpperCamelCase_				:   Tuple					    =							img.load()

						# loop through the image-coordinates
						for image_x in range(lowerCAmelCase__	):
												for image_y in range(lowerCAmelCase__	):
																		# determine the figure-coordinates based on the image-coordinates
																		UpperCamelCase_				:   Optional[Any]					    =							figure_width / image_width * image_height
																		UpperCamelCase_				:   Union[str, Any]					    =							figure_center_x + (image_x / image_width - 0.5) * figure_width
																		UpperCamelCase_				:   str					    =							figure_center_y + (image_y / image_height - 0.5) * figure_height

																		UpperCamelCase_				:   str					    =							get_distance(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__	)

																		# color the corresponding pixel based on the selected coloring-function
																		if use_distance_color_coding:
																								UpperCamelCase_				:   Dict					    =							get_color_coded_rgb(lowerCAmelCase__	)
																		else:
																								UpperCamelCase_				:   str					    =							get_black_and_white_rgb(lowerCAmelCase__	)

						return img


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						# colored version, full figure
						UpperCamelCase							=get_image()

						# uncomment for colored version, different section, zoomed in
						# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
						# figure_width = 0.8)

						# uncomment for black and white version, full figure
						# img = get_image(use_distance_color_coding = False)

						# uncomment to save the image
						# img.save("mandelbrot.png")

						img.show()














'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCamelCase							={
    "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:
      UpperCamelCase							=[
          "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
          "Data2VecAudioForAudioFrameClassification",
          "Data2VecAudioForCTC",
          "Data2VecAudioForSequenceClassification",
          "Data2VecAudioForXVector",
          "Data2VecAudioModel",
          "Data2VecAudioPreTrainedModel",
      ]
      UpperCamelCase							=[
          "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "Data2VecTextForCausalLM",
          "Data2VecTextForMaskedLM",
          "Data2VecTextForMultipleChoice",
          "Data2VecTextForQuestionAnswering",
          "Data2VecTextForSequenceClassification",
          "Data2VecTextForTokenClassification",
          "Data2VecTextModel",
          "Data2VecTextPreTrainedModel",
      ]
      UpperCamelCase							=[
          "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
          "Data2VecVisionForImageClassification",
          "Data2VecVisionForMaskedImageModeling",
          "Data2VecVisionForSemanticSegmentation",
          "Data2VecVisionModel",
          "Data2VecVisionPreTrainedModel",
      ]

if is_tf_available():
      UpperCamelCase							=[
          "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

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






