


import colorsys

from PIL import Image  # type: ignore






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[str]    =				x
  lowerCamelCase :					List[str]    =				y
  for step in range(SCREAMING_SNAKE_CASE_	):  # noqa: B007
    lowerCamelCase :					List[str]    =				a * a - b * b + x
    lowerCamelCase :					Dict    =				2 * a * b + y
    lowerCamelCase :					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 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_ = 800      ,						SCREAMING_SNAKE_CASE_ = 600      ,						SCREAMING_SNAKE_CASE_ = -0.6      ,						SCREAMING_SNAKE_CASE_ = 0      ,						SCREAMING_SNAKE_CASE_ = 3.2      ,						SCREAMING_SNAKE_CASE_ = 50      ,						SCREAMING_SNAKE_CASE_ = True      ,						):


  '''simple docstring'''

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

  # loop through the image-coordinates
  for image_x in range(SCREAMING_SNAKE_CASE_	):
    for image_y in range(SCREAMING_SNAKE_CASE_	):
      # determine the figure-coordinates based on the image-coordinates
      lowerCamelCase :					Tuple    =				figure_width / image_width * image_height
      lowerCamelCase :					Optional[Any]    =				figure_center_x + (image_x / image_width - 0.5) * figure_width
      lowerCamelCase :					int    =				figure_center_y + (image_y / image_height - 0.5) * figure_height

      lowerCamelCase :					Optional[int]    =				get_distance(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)

      # color the corresponding pixel based on the selected coloring-function
      if use_distance_color_coding:
        lowerCamelCase :					int    =				get_color_coded_rgb(SCREAMING_SNAKE_CASE_	)
      else:
        lowerCamelCase :					Tuple    =				get_black_and_white_rgb(SCREAMING_SNAKE_CASE_	)

  return img


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # colored version, full figure
   _snake_case             =     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 torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Optional[int]     		=		"M-CLIP"






   def __init__(		self						,				__A=1024						,				__A=768						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					str    =				transformerDimSize
     lowerCamelCase :					Any    =				imageDimSize
     super().__init__(**__A )



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Tuple     		=		MCLIPConfig






   def __init__(		self						,				__A						,				*__A						,				**__A ):






     """simple docstring"""





     super().__init__(__A						,				*__A						,				**__A )
     lowerCamelCase :					Tuple    =				XLMRobertaModel(__A )
     lowerCamelCase :					Optional[Any]    =				torch.nn.Linear(
         in_features=config.transformerDimensions						,				out_features=config.numDims )






   def _snake_case    (		self						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Any    =				self.transformer(input_ids=__A						,				attention_mask=__A )[0]
     lowerCamelCase :					int    =				(embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
     return self.LinearTransformation(__A ), embs



