




"""simple docstring"""




import argparse
import os
import shutil

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



def     a__ (				snake_case__				)						->					List[str]:
			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(snake_case__       , """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=snake_case__       , threshold=snake_case__				)
												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(snake_case__       , snake_case__				)
												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(snake_case__       , snake_case__       , snake_case__				)
												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(snake_case__				)
												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(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__":
					lowerCAmelCase							:			str     							=							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							:			Dict     							=							parser.parse_args()

					main(args)









"""simple docstring"""




from math import ceil



def     a__ (				snake_case__       , snake_case__				)						->					Optional[int]:
			lowerCamelCase			      =					list(range(0       , snake_case__				)				)

			lowerCamelCase			      =					[item for sublist in list(device_map.values()				) for item in sublist]

			# Duplicate check
			lowerCamelCase			      =					[]
			for i in device_map_blocks:
						if device_map_blocks.count(snake_case__				) > 1 and i not in duplicate_blocks:
									duplicate_blocks.append(snake_case__				)
    # Missing blocks
			lowerCamelCase			      =					[i for i in blocks if i not in device_map_blocks]
			lowerCamelCase			      =					[i for i in device_map_blocks if i not in blocks]

			if len(snake_case__				) != 0:
						raise ValueError(
						    """Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."""
						    """ These attention blocks were specified more than once: """ + str(snake_case__				)				)
			if len(snake_case__				) != 0:
						raise ValueError(
						    """There are attention blocks for this model that are not specified in the device_map. Add these attention """
						    """blocks to a device on the device_map: """ + str(snake_case__				)				)
			if len(snake_case__				) != 0:
						raise ValueError(
						    """The device_map contains more attention blocks than this model has. Remove these from the device_map:"""
						    + str(snake_case__				)				)



def     a__ (				snake_case__       , snake_case__				)						->					List[Any]:
			lowerCamelCase			      =					list(range(snake_case__				)				)
			lowerCamelCase			      =					int(ceil(n_layers / len(snake_case__				)				)				)
			lowerCamelCase			      =					[layers[i : i + n_blocks] for i in range(0       , snake_case__       , snake_case__				)]

			return dict(zip(snake_case__       , snake_case__				)				)



