"""simple docstring"""




import argparse
import os
import shutil

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







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   args.pruning_method
		lowerCAmelCase__        =   args.threshold

		lowerCAmelCase__        =   args.model_name_or_path.rstrip("""/"""     )
		lowerCAmelCase__        =   args.target_model_path

		print(f"""Load fine-pruned model from {model_name_or_path}"""     )
		lowerCAmelCase__        =   torch.load(os.path.join(lowerCamelCase__			,  """pytorch_model.bin"""     )     )
		lowerCAmelCase__        =   {}

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

		if target_model_path is None:
				lowerCAmelCase__        =   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    :							Union[str, Any]													=						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    :							int													=						parser.parse_args()

			main(args)





"""simple docstring"""




import os
from math import logaa







def 							_UpperCAmelCase		(       lowerCamelCase__ = "base_exp.txt"     ):





		"""simple docstring"""




		lowerCAmelCase__        =   0
		lowerCAmelCase__        =   0
		for i, line in enumerate(open(os.path.join(os.path.dirname(lowerCamelCase__     )			,  lowerCamelCase__     )     )     ):
				lowerCAmelCase__   ,					lowerCAmelCase__        =   list(map(lowerCamelCase__			,  line.split(""","""     )     )     )
				if x * logaa(lowerCamelCase__     ) > largest:
						lowerCAmelCase__        =   x * logaa(lowerCamelCase__     )
						lowerCAmelCase__        =   i + 1
		return result


if __name__ == "__main__":
			print(solution())




