






import argparse
import os
import shutil

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






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    int:
							_lowercase							:   int 							=   args.pruning_method
							_lowercase							:   Optional[int] 							=   args.threshold

							_lowercase							:   str 							=   args.model_name_or_path.rstrip('/'				)
							_lowercase							:   Optional[Any] 							=   args.target_model_path

							print(F"""Load fine-pruned model from {model_name_or_path}"""				)
							_lowercase							:   int 							=   torch.load(os.path.join(SCREAMING_SNAKE_CASE     ,     'pytorch_model.bin'				)				)
							_lowercase							:   List[Any] 							=   {}

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

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

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

							torch.save(SCREAMING_SNAKE_CASE     ,     os.path.join(SCREAMING_SNAKE_CASE     ,     '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)








import string






def 		_A		(		_lowercase )					-> None:


     """simple docstring"""


     for key in range(len(string.ascii_uppercase ) ):
          __UpperCamelCase =			''
          for symbol in message:
               if symbol in string.ascii_uppercase:
                    __UpperCamelCase =			string.ascii_uppercase.find(_lowercase )
                    __UpperCamelCase =			num - key
                    if num < 0:
                         __UpperCamelCase =			num + len(string.ascii_uppercase )
                    __UpperCamelCase =			translated + string.ascii_uppercase[num]
               else:
                    __UpperCamelCase =			translated + symbol
          print(f'''Decryption using Key #{key}: {translated}''' )






def 		_A		(		)					-> None:


     """simple docstring"""


     __UpperCamelCase =			input('Encrypted message: ' )
     __UpperCamelCase =			message.upper()
     decrypt(_lowercase )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


