




import colorsys

from PIL import Image  # type: ignore



def 						lowerCAmelCase		(		_lowerCAmelCase							:   float						,							_lowerCAmelCase							:   float						,							_lowerCAmelCase							:   int	):




 """simple docstring"""







 UpperCAmelCase__							 =						x
 UpperCAmelCase__							 =						y
 for step in range(_lowerCAmelCase	):  # noqa: B007
  UpperCAmelCase__							 =						a * a - b * b + x
  UpperCAmelCase__							 =						2 * a * b + y
  UpperCAmelCase__							 =						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 						lowerCAmelCase		(		_lowerCAmelCase							:   float	):




 """simple docstring"""







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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   float	):




 """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 						lowerCAmelCase		(		_lowerCAmelCase							:   int = 800						,							_lowerCAmelCase							:   int = 600						,							_lowerCAmelCase							:   float = -0.6						,							_lowerCAmelCase							:   float = 0						,							_lowerCAmelCase							:   float = 3.2						,							_lowerCAmelCase							:   int = 50						,							_lowerCAmelCase							:   bool = True						,							):




 """simple docstring"""







 UpperCAmelCase__							 =						Image.new("RGB"						,							(image_width, image_height)	)
 UpperCAmelCase__							 =						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__							 =						figure_width / image_width * image_height
   UpperCAmelCase__							 =						figure_center_x + (image_x / image_width - 0.5) * figure_width
   UpperCAmelCase__							 =						figure_center_y + (image_y / image_height - 0.5) * figure_height

   UpperCAmelCase__							 =						get_distance(_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase	)

   # color the corresponding pixel based on the selected coloring-function
   if use_distance_color_coding:
    UpperCAmelCase__							 =						get_color_coded_rgb(_lowerCAmelCase	)
   else:
    UpperCAmelCase__							 =						get_black_and_white_rgb(_lowerCAmelCase	)

 return img


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # colored version, full figure
  _lowerCAmelCase						:   int		=   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()











from typing import TYPE_CHECKING

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


_lowerCAmelCase						:   Optional[int]		=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   List[str]		=   [
      "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GPTNeoForCausalLM",
      "GPTNeoForQuestionAnswering",
      "GPTNeoForSequenceClassification",
      "GPTNeoForTokenClassification",
      "GPTNeoModel",
      "GPTNeoPreTrainedModel",
      "load_tf_weights_in_gpt_neo",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Tuple		=   [
      "FlaxGPTNeoForCausalLM",
      "FlaxGPTNeoModel",
      "FlaxGPTNeoPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpt_neo import (
        GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTNeoForCausalLM,
        GPTNeoForQuestionAnswering,
        GPTNeoForSequenceClassification,
        GPTNeoForTokenClassification,
        GPTNeoModel,
        GPTNeoPreTrainedModel,
        load_tf_weights_in_gpt_neo,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
  import sys

  _lowerCAmelCase						:   Union[str, Any]		=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





