



import argparse
import os
import shutil

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






def  UpperCAmelCase__    (    lowercase__   ) ->      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(lowercase__						,       """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=lowercase__						,       threshold=lowercase__   )
																								__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(lowercase__						,       lowercase__   )
																								__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(lowercase__						,       lowercase__						,       lowercase__   )
																								__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(lowercase__   )
																								__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(lowercase__   )						,       F"bertarized_{os.path.basename(lowercase__   )}"   )

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

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


if __name__ == "__main__":
			UpperCamelCase__ =						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",
			)

			UpperCamelCase__ =						parser.parse_args()

			main(args)










def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      bool:
						__lowercase					=				len(lowercase__   )
						__lowercase					=				[[False] * (required_sum + 1) for _ in range(arr_len + 1   )]

						# for each arr value, a sum of zero(0) can be formed by not taking any element
						# hence True/1
						for i in range(arr_len + 1   ):
												__lowercase					=				True

						# sum is not zero and set is empty then false
						for i in range(1						,       required_sum + 1   ):
												__lowercase					=				False

						for i in range(1						,       arr_len + 1   ):
												for j in range(1						,       required_sum + 1   ):
																		if arr[i - 1] > j:
																								__lowercase					=				subset[i - 1][j]
																		if arr[i - 1] <= j:
																								__lowercase					=				subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

						return subset[arr_len][required_sum]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





