



import argparse
import os
import shutil

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






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Dict				     =					args.pruning_method
							_lowerCAmelCase  :							Dict				     =					args.threshold

							_lowerCAmelCase  :							Any				     =					args.model_name_or_path.rstrip("/"						)
							_lowerCAmelCase  :							str				     =					args.target_model_path

							print(F"Load fine-pruned model from {model_name_or_path}"						)
							_lowerCAmelCase  :							Any				     =					torch.load(os.path.join(_lowerCamelCase       ,				"pytorch_model.bin"						)						)
							_lowerCAmelCase  :							Tuple				     =					{}

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

							if target_model_path is None:
														_lowerCAmelCase  :							Optional[Any]				     =					os.path.join(
														    os.path.dirname(_lowerCamelCase						)       ,				F"bertarized_{os.path.basename(_lowerCamelCase						)}"						)

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

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


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

						_snake_case	=						parser.parse_args()

						main(args)





import string






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							for key in range(len(string.ascii_uppercase						)						):
														_lowerCAmelCase  :							str				     =					""
														for symbol in message:
																					if symbol in string.ascii_uppercase:
																												_lowerCAmelCase  :							List[str]				     =					string.ascii_uppercase.find(_lowerCamelCase						)
																												_lowerCAmelCase  :							Dict				     =					num - key
																												if num < 0:
																																			_lowerCAmelCase  :							Dict				     =					num + len(string.ascii_uppercase						)
																												_lowerCAmelCase  :							Optional[Any]				     =					translated + string.ascii_uppercase[num]
																					else:
																												_lowerCAmelCase  :							int				     =					translated + symbol
														print(F"Decryption using Key #{key}: {translated}"						)






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							Tuple				     =					input("Encrypted message: "						)
							_lowerCAmelCase  :							Dict				     =					message.upper()
							decrypt(_lowerCamelCase						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()
