




import colorsys

from PIL import Image  # type: ignore







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_						)   ->       float:



       """simple docstring"""

       A__       =    x
       A__       =    y
       for step in range(lowercase_						):  # noqa: B007
              A__       =    a * a - b * b + x
              A__       =    2 * a * b + y
              A__       =    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 SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       tuple:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       tuple:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE				(       lowercase_ = 800 ,				lowercase_ = 600 ,				lowercase_ = -0.6 ,				lowercase_ = 0 ,				lowercase_ = 3.2 ,				lowercase_ = 50 ,				lowercase_ = True ,				)   ->       Image.Image:



       """simple docstring"""

       A__       =    Image.new('''RGB''' ,				(image_width, image_height)						)
       A__       =    img.load()

       # loop through the image-coordinates
       for image_x in range(lowercase_						):
              for image_y in range(lowercase_						):
                     # determine the figure-coordinates based on the image-coordinates
                     A__       =    figure_width / image_width * image_height
                     A__       =    figure_center_x + (image_x / image_width - 0.5) * figure_width
                     A__       =    figure_center_y + (image_y / image_height - 0.5) * figure_height

                     A__       =    get_distance(lowercase_ ,				lowercase_ ,				lowercase_						)

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

       return img


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # colored version, full figure
  _lowerCamelCase	:		List[str]							       =   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()








import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase	:		Tuple							       =   logging.get_logger(__name__)

_lowerCamelCase	:		str							       =   {
    """microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''git_vision_model'''


    def __init__(			self      :	Any					, UpperCAmelCase__      :	Any=768					, UpperCAmelCase__      :	int=3_072					, UpperCAmelCase__      :	List[str]=12					, UpperCAmelCase__      :	Dict=12					, UpperCAmelCase__      :	Optional[int]=3					, UpperCAmelCase__      :	List[Any]=224					, UpperCAmelCase__      :	Union[str, Any]=16					, UpperCAmelCase__      :	Union[str, Any]="quick_gelu"					, UpperCAmelCase__      :	Dict=1e-5					, UpperCAmelCase__      :	Union[str, Any]=0.0					, UpperCAmelCase__      :	Any=0.02					, **UpperCAmelCase__      :	Any					, )						->Optional[int]:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)

           A__       =    hidden_size
           A__       =    intermediate_size
           A__       =    num_hidden_layers
           A__       =    num_attention_heads
           A__       =    num_channels
           A__       =    patch_size
           A__       =    image_size
           A__       =    initializer_range
           A__       =    attention_dropout
           A__       =    layer_norm_eps
           A__       =    hidden_act





    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	Any					, UpperCAmelCase__      :	Union[str, os.PathLike]					, **UpperCAmelCase__      :	int)						->"PretrainedConfig":





           '''simple docstring'''

           cls._set_token_in_kwargs(UpperCAmelCase__)

           A__ ,							A__       =    cls.get_config_dict(UpperCAmelCase__					, **UpperCAmelCase__)

           # get the vision config dict if we are loading from GITConfig
           if config_dict.get('''model_type''') == "git":
                  A__       =    config_dict['''vision_config''']

           if "model_type" in config_dict and hasattr(cls					, '''model_type''') and config_dict["model_type"] != cls.model_type:
                  logger.warning(
                      f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                      f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")

           return cls.from_dict(UpperCAmelCase__					, **UpperCAmelCase__)


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''git'''


    def __init__(			self      :	Dict					, UpperCAmelCase__      :	Dict=None					, UpperCAmelCase__      :	int=30_522					, UpperCAmelCase__      :	Optional[int]=768					, UpperCAmelCase__      :	Dict=6					, UpperCAmelCase__      :	int=12					, UpperCAmelCase__      :	List[str]=3_072					, UpperCAmelCase__      :	str="gelu"					, UpperCAmelCase__      :	int=0.1					, UpperCAmelCase__      :	Union[str, Any]=0.1					, UpperCAmelCase__      :	List[Any]=1_024					, UpperCAmelCase__      :	List[str]=0.02					, UpperCAmelCase__      :	Any=1e-12					, UpperCAmelCase__      :	Union[str, Any]=0					, UpperCAmelCase__      :	List[Any]="absolute"					, UpperCAmelCase__      :	int=True					, UpperCAmelCase__      :	Any=False					, UpperCAmelCase__      :	int=101					, UpperCAmelCase__      :	Tuple=102					, UpperCAmelCase__      :	Dict=None					, **UpperCAmelCase__      :	List[str]					, )						->Any:





           '''simple docstring'''

           super().__init__(bos_token_id=UpperCAmelCase__					, eos_token_id=UpperCAmelCase__					, pad_token_id=UpperCAmelCase__					, **UpperCAmelCase__)

           if vision_config is None:
                  A__       =    {}
                  logger.info('''vision_config is None. initializing the GitVisionConfig with default values.''')

           A__       =    GitVisionConfig(**UpperCAmelCase__)
           A__       =    vocab_size
           A__       =    hidden_size
           A__       =    num_hidden_layers
           A__       =    num_attention_heads
           A__       =    hidden_act
           A__       =    intermediate_size
           A__       =    hidden_dropout_prob
           A__       =    attention_probs_dropout_prob
           A__       =    max_position_embeddings
           A__       =    initializer_range
           A__       =    layer_norm_eps
           A__       =    position_embedding_type
           A__       =    use_cache
           A__       =    tie_word_embeddings
           A__       =    num_image_with_embedding

           A__       =    bos_token_id
           A__       =    eos_token_id





    def        SCREAMING_SNAKE_CASE							(			self      :	Any)						->List[Any]:





           '''simple docstring'''

           A__       =    copy.deepcopy(self.__dict__)
           A__       =    self.vision_config.to_dict()
           A__       =    self.__class__.model_type
           return output


