




'''simple docstring'''


from __future__ import annotations

from typing import Generic, TypeVar

lowerCAmelCase__						:       Any										=						TypeVar("""T""")




class     a			(				Generic[T]       ):






						"""simple docstring"""




						def __init__(							self					:      Tuple			,			snake_case_					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  data
								snake_case__						:       Any       						=  self
								snake_case__						:       Dict       						=  0





class     a			(				Generic[T]       ):






						"""simple docstring"""




						def __init__(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  {}







						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  DisjointSetTreeNode(UpperCAmelCase_  )







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      int  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  self.map[data]
								if elem_ref != elem_ref.parent:
										snake_case__						:       Optional[int]       						=  self.find_set(elem_ref.parent.data  )
								return elem_ref.parent







						def       __magic_name__							(							self					:      Dict			,			snake_case_					:      Optional[Any]			,			snake_case_					:      Dict  ):



								'''simple docstring'''



								if nodea.rank > nodea.rank:
										snake_case__						:       List[str]       						=  nodea
								else:
										snake_case__						:       Union[str, Any]       						=  nodea
										if nodea.rank == nodea.rank:
												nodea.rank += 1







						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      List[str]			,			snake_case_					:      Optional[int]  ):



								'''simple docstring'''



								self.link(self.find_set(UpperCAmelCase_  )			,			self.find_set(UpperCAmelCase_  )  )





class     a			(				Generic[T]       ):






						"""simple docstring"""




						def __init__(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       str       						=  {}







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      Tuple  ):



								'''simple docstring'''



								if node not in self.connections:
										snake_case__						:       List[str]       						=  {}







						def       __magic_name__							(							self					:      List[Any]			,			snake_case_					:      List[Any]			,			snake_case_					:      str			,			snake_case_					:      List[str]  ):



								'''simple docstring'''



								self.add_node(UpperCAmelCase_  )
								self.add_node(UpperCAmelCase_  )
								snake_case__						:       Union[str, Any]       						=  weight
								snake_case__						:       str       						=  weight







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  []
								snake_case__						:       int       						=  set()
								for start in self.connections:
										for end in self.connections[start]:
												if (start, end) not in seen:
														seen.add((end, start)  )
														edges.append((start, end, self.connections[start][end])  )
								edges.sort(key=lambda snake_case_  : x[2]  )

								# creating the disjoint set
								snake_case__						:       int       						=  DisjointSetTree[T]()
								for node in self.connections:
										disjoint_set.make_set(UpperCAmelCase_  )

								# MST generation
								snake_case__						:       int       						=  0
								snake_case__						:       str       						=  0
								snake_case__						:       List[str]       						=  GraphUndirectedWeighted[T]()
								while num_edges < len(self.connections  ) - 1:
										snake_case__						,       snake_case__						,       snake_case__						:       Dict       						=  edges[index]
										index += 1
										snake_case__						:       Dict       						=  disjoint_set.find_set(UpperCAmelCase_  )
										snake_case__						:       Optional[int]       						=  disjoint_set.find_set(UpperCAmelCase_  )
										if parent_u != parent_v:
												num_edges += 1
												graph.add_edge(UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_  )
												disjoint_set.union(UpperCAmelCase_			,			UpperCAmelCase_  )
								return graph






'''simple docstring'''


import argparse
import os
import shutil

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




def 	_a       (				__lowerCAmelCase		:      Dict     ):

		"""simple docstring"""

		snake_case__						:       int       						=  args.pruning_method
		snake_case__						:       Dict       						=  args.threshold

		snake_case__						:       str       						=  args.model_name_or_path.rstrip('''/'''     )
		snake_case__						:       int       						=  args.target_model_path

		print(F"""Load fine-pruned model from {model_name_or_path}"""     )
		snake_case__						:       Dict       						=  torch.load(os.path.join(__lowerCAmelCase   ,							'''pytorch_model.bin'''     )     )
		snake_case__						:       Any       						=  {}

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

		if target_model_path is None:
				snake_case__						:       Optional[int]       						=  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__						:       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__						:       Optional[Any]										=						parser.parse_args()

					main(args)



