


"""simple docstring"""






import argparse
import os
import shutil

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





def 				A_       ( snake_case__		)					->  Any:
			_UpperCamelCase					:List[str]					     =	args.pruning_method
			_UpperCamelCase					:Union[str, Any]					     =	args.threshold

			_UpperCamelCase					:int					     =	args.model_name_or_path.rstrip('''/'''		)
			_UpperCamelCase					:int					     =	args.target_model_path

			print(f"Load fine-pruned model from {model_name_or_path}"		)
			_UpperCamelCase					:Tuple					     =	torch.load(os.path.join(lowercase_       ,					'''pytorch_model.bin'''		)		)
			_UpperCamelCase					:Union[str, Any]					     =	{}

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

			if target_model_path is None:
						_UpperCamelCase					:int					     =	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__				: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""",
					)

					UpperCamelCase__				:int			   =	parser.parse_args()

					main(args)







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			list:



       """simple docstring"""

       if len(lowercase_		) <= 1:
              return [tuple(lowercase_		)]

       A__ 					=       []

       def generate(lowercase_					,							lowercase_		):
              if k == 1:
                     res.append(tuple(arr[:]		)		)
                     return

              generate(k - 1					,							lowercase_		)

              for i in range(k - 1		):
                     if k % 2 == 0:  # k is even
                            A__					,				A__ 					=       arr[k - 1], arr[i]
                     else:  # k is odd
                            A__					,				A__ 					=       arr[k - 1], arr[0]
                     generate(k - 1					,							lowercase_		)

       generate(len(lowercase_		)					,							lowercase_		)
       return res


if __name__ == "__main__":
  _lowerCamelCase      :						int							=  input("""Enter numbers separated by a comma:\n""").strip()
  _lowerCamelCase      :						str							=  [int(item) for item in user_input.split(""",""")]
  print(heaps(arr))


