





import argparse
import os
import shutil

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





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Dict:







			'''simple docstring'''

			UpperCAmelCase   		=				args.pruning_method
			UpperCAmelCase   		=				args.threshold

			UpperCAmelCase   		=				args.model_name_or_path.rstrip('''/''' )
			UpperCAmelCase   		=				args.target_model_path

			print(F"""Load fine-pruned model from {model_name_or_path}""" )
			UpperCAmelCase   		=				torch.load(os.path.join(UpperCamelCase__			,			'''pytorch_model.bin''' ) )
			UpperCAmelCase   		=				{}

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

			if target_model_path is None:
						UpperCAmelCase   		=				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				:    Tuple       =   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				:    int       =   parser.parse_args()

				main(args)










def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				list[int]:







			'''simple docstring'''

			if length <= 0 or not isinstance(UpperCamelCase__			,			UpperCamelCase__ ):
						raise ValueError('''Length must be a positive integer.''' )
			return [n * (2 * n - 1) for n in range(UpperCamelCase__ )]


if __name__ == "__main__":
				print(hexagonal_numbers(length=5))
				print(hexagonal_numbers(length=10))



