


'''simple docstring'''


import argparse
import os
import shutil

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






def  __lowerCAmelCase (__lowerCAmelCase   ):

		'''simple docstring'''





		_UpperCAmelCase		:       Dict								=      args.pruning_method
		_UpperCAmelCase		:       List[Any]								=      args.threshold

		_UpperCAmelCase		:       List[str]								=      args.model_name_or_path.rstrip("/"   )
		_UpperCAmelCase		:       str								=      args.target_model_path

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

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

		if target_model_path is None:
				_UpperCAmelCase		:       List[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__":
						lowerCamelCase__    	=     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',
						)

						lowerCamelCase__    	=     parser.parse_args()

						main(args)







'''simple docstring'''


def  __lowerCAmelCase (__lowerCAmelCase   ):  # noqa: E741
		_UpperCAmelCase		:       List[str]								=      len(__lowerCAmelCase   )
		_UpperCAmelCase		:       str								=      0
		_UpperCAmelCase		:       List[str]								=      [0] * n
		_UpperCAmelCase		:       int								=      [False] * n
		_UpperCAmelCase		:       Dict								=      [False] * n

		def dfs(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
				if parent == root:
						out_edge_count += 1
				_UpperCAmelCase		:       List[Any]								=      True
				_UpperCAmelCase		:       str								=      at

				for to in l[at]:
						if to == parent:
								pass
						elif not visited[to]:
								_UpperCAmelCase		:       List[str]								=      dfs(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   )
								_UpperCAmelCase		:       Tuple								=      min(low[at]      , low[to]   )

								# AP found via bridge
								if at < low[to]:
										_UpperCAmelCase		:       Dict								=      True
								# AP found via cycle
								if at == low[to]:
										_UpperCAmelCase		:       Dict								=      True
						else:
								_UpperCAmelCase		:       Optional[int]								=      min(low[at]      , __lowerCAmelCase   )
				return out_edge_count

		for i in range(__lowerCAmelCase   ):
				if not visited[i]:
						_UpperCAmelCase		:       str								=      0
						_UpperCAmelCase		:       Tuple								=      dfs(__lowerCAmelCase      , __lowerCAmelCase      , -1      , __lowerCAmelCase   )
						_UpperCAmelCase		:       Optional[int]								=      out_edge_count > 1

		for x in range(len(__lowerCAmelCase   )   ):
				if is_art[x] is True:
						print(__lowerCAmelCase   )


# Adjacency list of graph
lowerCamelCase__    	=     {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}





compute_ap(data)



