




"""simple docstring"""






import argparse
import os
import shutil

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





def 							UpperCAmelCase		(		snake_case			:					str    ):
					_lowerCAmelCase:str									=  args.pruning_method
					_lowerCAmelCase:Any									=  args.threshold

					_lowerCAmelCase:Union[str, Any]									=  args.model_name_or_path.rstrip('''/'''    )
					_lowerCAmelCase:Optional[int]									=  args.target_model_path

					print(F'Load fine-pruned model from {model_name_or_path}'    )
					_lowerCAmelCase:Dict									=  torch.load(os.path.join(snake_case      ,       '''pytorch_model.bin'''    )    )
					_lowerCAmelCase:str									=  {}

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

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

					torch.save(snake_case      ,       os.path.join(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)












"""simple docstring"""






def 							UpperCAmelCase		(		snake_case			:					str      ,       snake_case			:					str    ):
					assert x is not None
					assert y is not None

					_lowerCAmelCase:Optional[Any]									=  len(snake_case    )
					_lowerCAmelCase:Tuple									=  len(snake_case    )

					# declaring the array for storing the dp values
					_lowerCAmelCase:Optional[Any]									=  [[0] * (n + 1) for _ in range(m + 1    )]  # noqa: E741

					for i in range(1      ,       m + 1    ):
										for j in range(1      ,       n + 1    ):
															_lowerCAmelCase:Optional[int]									=  1 if x[i - 1] == y[j - 1] else 0

															_lowerCAmelCase:int									=  max(l[i - 1][j]      ,       l[i][j - 1]      ,       l[i - 1][j - 1] + match    )

					_lowerCAmelCase:List[Any]									=  ''''''
					_lowerCAmelCase							,   _lowerCAmelCase:Dict									=  m, n
					while i > 0 and j > 0:
										_lowerCAmelCase:Any									=  1 if x[i - 1] == y[j - 1] else 0

										if l[i][j] == l[i - 1][j - 1] + match:
															if match == 1:
																				_lowerCAmelCase:Any									=  x[i - 1] + seq
															i -= 1
															j -= 1
										elif l[i][j] == l[i - 1][j]:
															i -= 1
										else:
															j -= 1

					return l[m][n], seq


if __name__ == "__main__":
				UpperCamelCase__						=			'''AGGTAB'''
				UpperCamelCase__						=			'''GXTXAYB'''
				UpperCamelCase__						=			4
				UpperCamelCase__						=			'''GTAB'''

				UpperCamelCase__						,							UpperCamelCase__						=			longest_common_subsequence(a, b)
				print('''len =''', ln, ''', sub-sequence =''', subseq)
				import doctest

				doctest.testmod()






