


import colorsys

from PIL import Image  # type: ignore







def     _lowercase							(  __lowerCamelCase      :       float		,__lowerCamelCase      :       float		,__lowerCamelCase      :       int      )	-> List[str]:





     '''simple docstring'''





     UpperCamelCase__				:	Tuple										=  x
     UpperCamelCase__				:	Any										=  y
     for step in range(UpperCamelCase__      ):  # noqa: B007
          UpperCamelCase__				:	Any										=  a * a - b * b + x
          UpperCamelCase__				:	Union[str, Any]										=  2 * a * b + y
          UpperCamelCase__				:	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							(  __lowerCamelCase      :       float      )	-> int:





     '''simple docstring'''





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







def     _lowercase							(  __lowerCamelCase      :       float      )	-> Optional[Any]:





     '''simple docstring'''





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







def     _lowercase							(  __lowerCamelCase      :       int = 800		,__lowerCamelCase      :       int = 600		,__lowerCamelCase      :       float = -0.6		,__lowerCamelCase      :       float = 0		,__lowerCamelCase      :       float = 3.2		,__lowerCamelCase      :       int = 50		,__lowerCamelCase      :       bool = True		,)	-> str:





     '''simple docstring'''





     UpperCamelCase__				:	Tuple										=  Image.new('''RGB'''		,(image_width, image_height)      )
     UpperCamelCase__				:	str										=  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
               UpperCamelCase__				:	Optional[Any]										=  figure_width / image_width * image_height
               UpperCamelCase__				:	Tuple										=  figure_center_x + (image_x / image_width - 0.5) * figure_width
               UpperCamelCase__				:	Union[str, Any]										=  figure_center_y + (image_y / image_height - 0.5) * figure_height

               UpperCamelCase__				:	int										=  get_distance(UpperCamelCase__		,UpperCamelCase__		,UpperCamelCase__      )

               # color the corresponding pixel based on the selected coloring-function
               if use_distance_color_coding:
                    UpperCamelCase__				:	List[Any]										=  get_color_coded_rgb(UpperCamelCase__      )
               else:
                    UpperCamelCase__				:	List[str]										=  get_black_and_white_rgb(UpperCamelCase__      )

     return img


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # colored version, full figure
   _SCREAMING_SNAKE_CASE			:       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()





'''simple docstring'''


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					:				Dict  ,		__a					:				List[str]						)				->						Tuple:
								super().__init__()
								__UpperCAmelCase      					=      model
								__UpperCAmelCase      					=      2
								__UpperCAmelCase      					=      nn.Linear(self.model.config.hidden_size  ,		self.num_labels						)






							def    snake_case__    (   self					:				int						)				->						int:
								pass





def    lowerCAmelCase       (		UpperCamelCase__					: str     ,       UpperCamelCase__					: str     ,       UpperCamelCase__					: str							):


	"""simple docstring"""
	# load longformer model from model identifier
	__UpperCAmelCase      					=      LongformerModel.from_pretrained(UpperCamelCase__							)
	__UpperCAmelCase      					=      LightningModel(UpperCamelCase__							)

	__UpperCAmelCase      					=      torch.load(UpperCamelCase__     ,       map_location=torch.device('''cpu'''							)							)
	lightning_model.load_state_dict(ckpt['''state_dict''']							)

	# init longformer question answering model
	__UpperCAmelCase      					=      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__":
					__lowerCAmelCase						:							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."
					)
					__lowerCAmelCase						:							List[str]    							=						parser.parse_args()
					convert_longformer_qa_checkpoint_to_pytorch(
					    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
					)
