






"""simple docstring"""



import argparse
import os
import shutil

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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str      )		->List[Any]:
						A__						:  Any		  =  args.pruning_method
						A__						:  Tuple		  =  args.threshold

						A__						:  List[Any]		  =  args.model_name_or_path.rstrip("""/"""      )
						A__						:  Optional[int]		  =  args.target_model_path

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

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

						if target_model_path is None:
												A__						:  List[str]		  =  os.path.join(
												    os.path.dirname(UpperCAmelCase__      ),						f'bertarized_{os.path.basename(UpperCAmelCase__      )}'      )

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

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


if __name__ == "__main__":
						A_    =							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''',
						)

						A_    =							parser.parse_args()

						main(args)














"""simple docstring"""



from __future__ import annotations

A_    =							[]




def      _lowerCAmelCase		(							UpperCAmelCase__       :					list[list[int]],						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					int      )		->bool:
						for i in range(len(UpperCAmelCase__      )      ):
												if board[row][i] == 1:
																		return False
						for i in range(len(UpperCAmelCase__      )      ):
												if board[i][column] == 1:
																		return False
						for i, j in zip(range(UpperCAmelCase__,						-1,						-1      ),						range(UpperCAmelCase__,						-1,						-1      )      ):
												if board[i][j] == 1:
																		return False
						for i, j in zip(range(UpperCAmelCase__,						-1,						-1      ),						range(UpperCAmelCase__,						len(UpperCAmelCase__      )      )      ):
												if board[i][j] == 1:
																		return False
						return True




def      _lowerCAmelCase		(							UpperCAmelCase__       :					list[list[int]],						UpperCAmelCase__       :					int      )		->bool:
						if row >= len(UpperCAmelCase__      ):
												solution.append(UpperCAmelCase__      )
												printboard(UpperCAmelCase__      )
												print()
												return True
						for i in range(len(UpperCAmelCase__      )      ):
												if is_safe(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      ):
																		A__						:  Tuple		  =  1
																		solve(UpperCAmelCase__,						row + 1      )
																		A__						:  Optional[int]		  =  0
						return False




def      _lowerCAmelCase		(							UpperCAmelCase__       :					list[list[int]]      )		->None:
						for i in range(len(UpperCAmelCase__      )      ):
												for j in range(len(UpperCAmelCase__      )      ):
																		if board[i][j] == 1:
																								print("""Q""",						end=""" """      )
																		else:
																								print(""".""",						end=""" """      )
												print()


# n=int(input("The no. of queens"))
A_    =							8
A_    =							[[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print('''The total no. of solutions are :''', len(solution))






