
"""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			:       List[str]     ,					__UpperCamelCase			:       Union[str, Any]     )		->Optional[int]:






        '''simple docstring'''






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







    def      _snake_case					(			self			:       List[Any]     )		->Optional[Any]:






        '''simple docstring'''






        pass







def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  str:





    """simple docstring"""

    _UpperCAmelCase          =   LongformerModel.from_pretrained(_A      )
    _UpperCAmelCase          =   LightningModel(_A      )

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

    # init longformer question answering model
    _UpperCAmelCase          =   LongformerForQuestionAnswering.from_pretrained(_A      )

    # 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(_A      )

    print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}"""      )


if __name__ == "__main__":
 a					:	List[str]							=		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.'''
 )
 a					:	Optional[int]							=		parser.parse_args()
 convert_longformer_qa_checkpoint_to_pytorch(
     args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
 )

"""simple docstring"""



import colorsys

from PIL import Image  # type: ignore


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  float:





    """simple docstring"""

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





    """simple docstring"""

    if distance == 1:
        return (0, 0, 0)
    else:
        return (2_5_5, 2_5_5, 2_5_5)


def  _UpperCamelCase	(						_A      )    ->  tuple:





    """simple docstring"""

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


def  _UpperCamelCase	(						_A = 8_0_0					,      _A = 6_0_0					,      _A = -0.6					,      _A = 0					,      _A = 3.2					,      _A = 5_0					,      _A = True					,      )    ->  Image.Image:





    """simple docstring"""

    _UpperCAmelCase          =   Image.new("""RGB"""					,      (image_width, image_height)      )
    _UpperCAmelCase          =   img.load()

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

            _UpperCAmelCase          =   get_distance(_A					,      _A					,      _A      )

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

    return img


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # colored version, full figure
 a					:	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()