
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__       =logging.get_logger(__name__)

lowercase__       ={
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}



class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							Dict	       =      "roberta-prelayernorm"
   def __init__(self :					str					,				snake_case_ :					Tuple=5_0_2_6_5					,				snake_case_ :					List[str]=7_6_8					,				snake_case_ :					str=1_2					,				snake_case_ :					Dict=1_2					,				snake_case_ :					int=3_0_7_2					,				snake_case_ :					Tuple="gelu"					,				snake_case_ :					Optional[int]=0.1					,				snake_case_ :					Any=0.1					,				snake_case_ :					Union[str, Any]=5_1_2					,				snake_case_ :					str=2					,				snake_case_ :					str=0.02					,				snake_case_ :					int=1E-12					,				snake_case_ :					int=1					,				snake_case_ :					int=0					,				snake_case_ :					Any=2					,				snake_case_ :					str="absolute"					,				snake_case_ :					str=True					,				snake_case_ :					Union[str, Any]=None					,				**snake_case_ :					Union[str, Any]					,				):
    super().__init__(pad_token_id=snake_case_					,				bos_token_id=snake_case_					,				eos_token_id=snake_case_					,				**snake_case_   )

    __a :  List[str]						=  vocab_size
    __a :  Optional[int]						=  hidden_size
    __a :  List[Any]						=  num_hidden_layers
    __a :  Tuple						=  num_attention_heads
    __a :  Optional[int]						=  hidden_act
    __a :  List[Any]						=  intermediate_size
    __a :  str						=  hidden_dropout_prob
    __a :  str						=  attention_probs_dropout_prob
    __a :  Dict						=  max_position_embeddings
    __a :  Optional[Any]						=  type_vocab_size
    __a :  List[str]						=  initializer_range
    __a :  Union[str, Any]						=  layer_norm_eps
    __a :  Tuple						=  position_embedding_type
    __a :  Union[str, Any]						=  use_cache
    __a :  Dict						=  classifier_dropout





class 		UpperCamelCase__  (       __lowercase    ):
   @property
   def       lowerCAmelCase			(self :					Any   ):
    if self.task == "multiple-choice":
     __a :  List[Any]						=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
    else:
     __a :  int						=  {0: '''batch''', 1: '''sequence'''}
    return OrderedDict(
        [
            ('''input_ids''', dynamic_axis),
            ('''attention_mask''', dynamic_axis),
        ]   )


import colorsys

from PIL import Image  # type: ignore




def    __UpperCamelCase     (				lowerCAmelCase__							:				float  ,   lowerCAmelCase__							:				float  ,   lowerCAmelCase__							:				int  ):
 __a :  Any						=  x
 __a :  List[Any]						=  y
 for step in range(lowerCAmelCase__  ):  # noqa: B007
  __a :  List[Any]						=  a * a - b * b + x
  __a :  Tuple						=  2 * a * b + y
  __a :  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    __UpperCamelCase     (				lowerCAmelCase__							:				float  ):
 if distance == 1:
  return (0, 0, 0)
 else:
  return (2_5_5, 2_5_5, 2_5_5)




def    __UpperCamelCase     (				lowerCAmelCase__							:				float  ):
 if distance == 1:
  return (0, 0, 0)
 else:
  return tuple(round(i * 2_5_5  ) for i in colorsys.hsv_to_rgb(lowerCAmelCase__  ,   1  ,   1  )  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				int = 8_0_0  ,   lowerCAmelCase__							:				int = 6_0_0  ,   lowerCAmelCase__							:				float = -0.6  ,   lowerCAmelCase__							:				float = 0  ,   lowerCAmelCase__							:				float = 3.2  ,   lowerCAmelCase__							:				int = 5_0  ,   lowerCAmelCase__							:				bool = True  ,   ):
 __a :  int						=  Image.new('''RGB'''  ,   (image_width, image_height)  )
 __a :  Dict						=  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
   __a :  Optional[Any]						=  figure_width / image_width * image_height
   __a :  str						=  figure_center_x + (image_x / image_width - 0.5) * figure_width
   __a :  str						=  figure_center_y + (image_y / image_height - 0.5) * figure_height

   __a :  Tuple						=  get_distance(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )

   # color the corresponding pixel based on the selected coloring-function
   if use_distance_color_coding:
    __a :  Optional[int]						=  get_color_coded_rgb(lowerCAmelCase__  )
   else:
    __a :  Optional[Any]						=  get_black_and_white_rgb(lowerCAmelCase__  )

 return img


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      # colored version, full figure
      lowercase__       =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()
