





import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    int:
		__lowercase											=     args.pruning_method
		__lowercase											=     args.threshold

		__lowercase											=     args.model_name_or_path.rstrip('/'							)
		__lowercase											=     args.target_model_path

		print(F"Load fine-pruned model from {model_name_or_path}"							)
		__lowercase											=     torch.load(os.path.join(snake_case						,   'pytorch_model.bin'							)							)
		__lowercase											=     {}

		for name, tensor in model.items():
				if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
						__lowercase											=     tensor
						print(F"Copied layer {name}"							)
				elif "classifier" in name or "qa_output" in name:
						__lowercase											=     tensor
						print(F"Copied layer {name}"							)
				elif "bias" in name:
						__lowercase											=     tensor
						print(F"Copied layer {name}"							)
				else:
						if pruning_method == "magnitude":
								__lowercase											=     MagnitudeBinarizer.apply(inputs=snake_case						,   threshold=snake_case							)
								__lowercase											=     tensor * mask
								print(F"Pruned layer {name}"							)
						elif pruning_method == "topK":
								if "mask_scores" in name:
										continue
								__lowercase											=     name[:-6]
								__lowercase											=     model[F"{prefix_}mask_scores"]
								__lowercase											=     TopKBinarizer.apply(snake_case						,   snake_case							)
								__lowercase											=     tensor * mask
								print(F"Pruned layer {name}"							)
						elif pruning_method == "sigmoied_threshold":
								if "mask_scores" in name:
										continue
								__lowercase											=     name[:-6]
								__lowercase											=     model[F"{prefix_}mask_scores"]
								__lowercase											=     ThresholdBinarizer.apply(snake_case						,   snake_case						,   snake_case							)
								__lowercase											=     tensor * mask
								print(F"Pruned layer {name}"							)
						elif pruning_method == "l0":
								if "mask_scores" in name:
										continue
								__lowercase											=     name[:-6]
								__lowercase											=     model[F"{prefix_}mask_scores"]
								__lowercase						,      __lowercase											=     -0.1, 1.1
								__lowercase											=     torch.sigmoid(snake_case							)
								__lowercase											=     s * (r - l) + l
								__lowercase											=     s_bar.clamp(min=0.0						,   max=1.0							)
								__lowercase											=     tensor * mask
								print(F"Pruned layer {name}"							)
						else:
								raise ValueError('Unknown pruning method'							)

		if target_model_path is None:
				__lowercase											=     os.path.join(
				    os.path.dirname(snake_case							)						,   F"bertarized_{os.path.basename(snake_case							)}"							)

		if not os.path.isdir(snake_case							):
				shutil.copytree(snake_case						,   snake_case							)
				print(F"\nCreated folder {target_model_path}"							)

		torch.save(snake_case						,   os.path.join(snake_case						,   'pytorch_model.bin'							)							)
		print('\nPruned model saved! See you later!'							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   argparse.ArgumentParser()

		parser.add_argument(
		    '''--pruning_method''',
		    choices=['''l0''', '''magnitude''', '''topK''', '''sigmoied_threshold'''],
		    type=str,
		    required=True,
		    help=(
		        '''Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'''
		        ''' sigmoied_threshold = Soft movement pruning)'''
		    ),
		)
		parser.add_argument(
		    '''--threshold''',
		    type=float,
		    required=False,
		    help=(
		        '''For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
		        '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
		        '''Not needed for `l0`'''
		    ),
		)
		parser.add_argument(
		    '''--model_name_or_path''',
		    type=str,
		    required=True,
		    help='''Folder containing the model that was previously fine-pruned''',
		)
		parser.add_argument(
		    '''--target_model_path''',
		    default=None,
		    type=str,
		    required=False,
		    help='''Folder containing the model that was previously fine-pruned''',
		)

		SCREAMING_SNAKE_CASE_   :      Any			=   parser.parse_args()

		main(args)







'''simple docstring'''



import colorsys

from PIL import Image  # type: ignore


def       snake_case   (	snake_case      : float					,		snake_case      : float					,		snake_case      : int			) ->  float:


    """simple docstring"""


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


    """simple docstring"""


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


def       snake_case   (	snake_case      : float			) ->  tuple:


    """simple docstring"""


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


def       snake_case   (	snake_case      : int = 800					,		snake_case      : int = 600					,		snake_case      : float = -0.6					,		snake_case      : float = 0					,		snake_case      : float = 3.2					,		snake_case      : int = 50					,		snake_case      : bool = True					,		) ->  Image.Image:


    """simple docstring"""


    lowerCAmelCase 						=							Image.new('RGB'					,		(image_width, image_height)			)
    lowerCAmelCase 						=							img.load()

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

            lowerCAmelCase 						=							get_distance(snake_case					,		snake_case					,		snake_case			)

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

    return img


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # colored version, full figure
     _UpperCamelCase						:  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()





