




import colorsys

from PIL import Image  # type: ignore

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					float:
     a     =				x
     a     =				y
     for step in range(__UpperCamelCase):  # 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							(       __UpperCamelCase)      ->					tuple:
     if distance == 1:
          return (0, 0, 0)
     else:
          return (2_55, 2_55, 2_55)

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					tuple:
     if distance == 1:
          return (0, 0, 0)
     else:
          return tuple(round(i * 2_55) for i in colorsys.hsv_to_rgb(__UpperCamelCase		, 1		, 1))

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase = 8_00		, __UpperCamelCase = 6_00		, __UpperCamelCase = -0.6		, __UpperCamelCase = 0		, __UpperCamelCase = 3.2		, __UpperCamelCase = 50		, __UpperCamelCase = True		, )      ->					Image.Image:
     a     =				Image.new("RGB"		, (image_width, image_height))
     a     =				img.load()

     # loop through the image-coordinates
     for image_x in range(__UpperCamelCase):
          for image_y in range(__UpperCamelCase):
               # 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(__UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)

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

     return img


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # colored version, full figure
   lowercase__				:      Dict	   = 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 argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class       a__	( pl.LightningModule				):







     def __init__(     self  ,		A			)  ->   Optional[Any]:



          '''simple docstring'''



          super().__init__()
          a     =				model
          a     =				2
          a     =				nn.Linear(self.model.config.hidden_size  ,		self.num_labels			)







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



          '''simple docstring'''



          pass







def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					List[Any]:
     # load longformer model from model identifier
     a     =				LongformerModel.from_pretrained(__UpperCamelCase)
     a     =				LightningModel(__UpperCamelCase)

     a     =				torch.load(__UpperCamelCase		, map_location=torch.device("cpu"))
     lightning_model.load_state_dict(ckpt["state_dict"])

     # init longformer question answering model
     a     =				LongformerForQuestionAnswering.from_pretrained(__UpperCamelCase)

     # transfer weights
     longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict())
     longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict())
     longformer_for_qa.eval()

     # save model
     longformer_for_qa.save_pretrained(__UpperCamelCase)

     print(f'''Conversion successful. Model saved under {pytorch_dump_folder_path}''')


if __name__ == "__main__":
   lowercase__				:      Optional[Any]	   = argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--longformer_model",
       default=None,
       type=str,
       required=True,
       help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
   )
   parser.add_argument(
       "--longformer_question_answering_ckpt_path",
       default=None,
       type=str,
       required=True,
       help="Path the official PyTorch Lightning Checkpoint.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   lowercase__				:      Union[str, Any]	   = parser.parse_args()
   convert_longformer_qa_checkpoint_to_pytorch(
       args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
   )

