




import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer




def 		A_	(							snake_case							:				str	) ->      int:



						'''simple docstring'''

						__UpperCamelCase       					=     args.serialization_dir
						__UpperCamelCase       					=     args.pruning_method
						__UpperCamelCase       					=     args.threshold

						__UpperCamelCase       					=     torch.load(os.path.join(_A	, '''pytorch_model.bin'''	)	, map_location='''cpu'''	)

						__UpperCamelCase       					=     0  # Number of remaining (not pruned) params in the encoder
						__UpperCamelCase       					=     0  # Number of params in the encoder

						print('''name'''.ljust(60	, ''' '''	)	, '''Remaining Weights %'''	, '''Remaining Weight'''	)
						for name, param in st.items():
												if "encoder" not in name:
																		continue

												if "mask_scores" in name:
																		if pruning_method == "topK":
																								__UpperCamelCase       					=     TopKBinarizer.apply(_A	, _A	).sum().item()
																		elif pruning_method == "sigmoied_threshold":
																								__UpperCamelCase       					=     ThresholdBinarizer.apply(_A	, _A	, _A	).sum().item()
																		elif pruning_method == "l0":
																								__UpperCamelCase					,							__UpperCamelCase       					=     -0.1, 1.1
																								__UpperCamelCase       					=     torch.sigmoid(_A	)
																								__UpperCamelCase       					=     s * (r - l) + l
																								__UpperCamelCase       					=     s_bar.clamp(min=0.0	, max=1.0	)
																								__UpperCamelCase       					=     (mask > 0.0).sum().item()
																		else:
																								raise ValueError('''Unknown pruning method'''	)
																		remaining_count += mask_ones
																		print(name.ljust(60	, ''' '''	)	, str(round(100 * mask_ones / param.numel()	, 3	)	).ljust(20	, ''' '''	)	, str(_A	)	)
												else:
																		encoder_count += param.numel()
																		if "bias" in name or "LayerNorm" in name:
																								remaining_count += param.numel()

						print(''''''	)
						print('''Remaining Weights (global) %: '''	, 100 * remaining_count / encoder_count	)


if __name__ == "__main__":
				lowercase__							:							Optional[int]													=     argparse.ArgumentParser()

				parser.add_argument(
				    "--pruning_method",
				    choices=["l0", "topK", "sigmoied_threshold"],
				    type=str,
				    required=True,
				    help=(
				        "Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"
				        " pruning)"
				    ),
				)
				parser.add_argument(
				    "--threshold",
				    type=float,
				    required=False,
				    help=(
				        "For `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(
				    "--serialization_dir",
				    type=str,
				    required=True,
				    help="Folder containing the model that was previously fine-pruned",
				)

				lowercase__							:							Dict													=     parser.parse_args()

				main(args)






def 		A_	(							snake_case							:				Union[str, Any]	, snake_case							:				Tuple	, snake_case							:				Optional[Any]	) ->      Union[str, Any]:



						'''simple docstring'''

						assert b != 0, "This cannot accept modulo that is == 0"
						if n == 0:
												return 1

						if n % 2 == 1:
												return (bin_exp_mod(snake_case	, n - 1	, snake_case	) * a) % b

						__UpperCamelCase       					=     bin_exp_mod(snake_case	, n / 2	, snake_case	)
						return (r * r) % b


if __name__ == "__main__":
				try:
								lowercase__							:							Dict													=     int(input("Enter Base : ").strip())
								lowercase__							:							Dict													=     int(input("Enter Power : ").strip())
								lowercase__							:							Dict													=     int(input("Enter Modulo : ").strip())
				except ValueError:
								print("Invalid literal for integer")

				print(bin_exp_mod(BASE, POWER, MODULO))




