import argparse
import os
import shutil

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





def  _lowerCamelCase					(   a_				: List[str]):
			lowerCamelCase						:List[str]						  =     args.pruning_method
			lowerCamelCase						:Tuple						  =     args.threshold

			lowerCamelCase						:List[str]						  =     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						:Optional[int]						  =     torch.load(os.path.join(a_						,			'''pytorch_model.bin'''))
			lowerCamelCase						:List[str]						  =     {}

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

			if target_model_path is None:
						lowerCamelCase						:int						  =     os.path.join(
						    os.path.dirname(a_)						,			F"bertarized_{os.path.basename(a_)}")

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

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




def  _lowerCamelCase					(   a_				: list):

			if not grid or not grid[0]:
						raise TypeError('''The grid does not contain the appropriate information''')

			for cell_n in range(1						,			len(grid[0])):
						grid[0][cell_n] += grid[0][cell_n - 1]
			lowerCamelCase						:Any						  =     grid[0]

			for row_n in range(1						,			len(a_)):
						lowerCamelCase						:List[str]						  =     grid[row_n]
						lowerCamelCase						:Union[str, Any]						  =     fill_row(a_						,			a_)
						lowerCamelCase						:List[Any]						  =     grid[row_n]

			return grid[-1][-1]





def  _lowerCamelCase					(   a_				: list						,			a_				: list):
			current_row[0] += row_above[0]
			for cell_n in range(1						,			len(a_)):
						current_row[cell_n] += min(current_row[cell_n - 1]						,			row_above[cell_n])

			return current_row


if __name__ == "__main__":
			import doctest

			doctest.testmod()



