
"""simple docstring"""
from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_			      =					TypeVar('''T''')
SCREAMING_SNAKE_CASE_			      =					TypeVar('''U''')
class      lowerCAmelCase_						(							Generic[T, U]				):



							'''simple docstring'''





							def __init__(				self  ,							snake_case_  ,							snake_case_							)   ->       List[str]:
													__lowerCAmelCase        =       key
													__lowerCAmelCase        =       val
													__lowerCAmelCase        =       0
													__lowerCAmelCase        =       None
													__lowerCAmelCase        =       None




							def __repr__(				self							)   ->       str:
													return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
													    self.key  ,							self.val  ,							self.freq  ,							self.next is not None  ,							self.prev is not None							)
class      lowerCAmelCase_						(							Generic[T, U]				):



							'''simple docstring'''





							def __init__(				self							)   ->       None:
													__lowerCAmelCase        =       DoubleLinkedListNode(snake_case_  ,							snake_case_							)
													__lowerCAmelCase        =       DoubleLinkedListNode(snake_case_  ,							snake_case_							)
													__lowerCAmelCase ,      __lowerCAmelCase        =       self.rear, self.head




							def __repr__(				self							)   ->       str:
													__lowerCAmelCase        =       ["""DoubleLinkedList"""]
													__lowerCAmelCase        =       self.head
													while node.next is not None:
																			rep.append(str(snake_case_							)							)
																			__lowerCAmelCase        =       node.next
													rep.append(str(self.rear							)							)
													return ",\n    ".join(snake_case_							)




							def  A__		(				self  ,							snake_case_							)   ->       None:
													__lowerCAmelCase        =       self.rear.prev

													# All nodes other than self.head are guaranteed to have non-None previous
													assert previous is not None

													__lowerCAmelCase        =       node
													__lowerCAmelCase        =       previous
													__lowerCAmelCase        =       node
													__lowerCAmelCase        =       self.rear
													node.freq += 1
													self._position_node(snake_case_							)




							def  A__		(				self  ,							snake_case_							)   ->       None:

													while node.prev is not None and node.prev.freq > node.freq:
																			# swap node with previous node
																			__lowerCAmelCase        =       node.prev

																			__lowerCAmelCase        =       previous_node.prev
																			__lowerCAmelCase        =       node.prev
																			__lowerCAmelCase        =       previous_node
																			__lowerCAmelCase        =       node




							def  A__		(				self  ,							snake_case_							)   ->       DoubleLinkedListNode[T, U] | None:

													if node.prev is None or node.next is None:
																			return None

													__lowerCAmelCase        =       node.next
													__lowerCAmelCase        =       node.prev
													__lowerCAmelCase        =       None
													__lowerCAmelCase        =       None
													return node
class      lowerCAmelCase_						(							Generic[T, U]				):



							'''simple docstring'''





							_snake_case		     =						{}




							def __init__(				self  ,							snake_case_							)   ->       Dict:
													__lowerCAmelCase        =       DoubleLinkedList()
													__lowerCAmelCase        =       capacity
													__lowerCAmelCase        =       0
													__lowerCAmelCase        =       0
													__lowerCAmelCase        =       0
													__lowerCAmelCase        =       {}




							def __repr__(				self							)   ->       str:
													return (
													    f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
													    f"""capacity={self.capacity}, current_size={self.num_keys})"""
													)




							def __contains__(				self  ,							snake_case_							)   ->       bool:
													return key in self.cache




							def  A__		(				self  ,							snake_case_							)   ->       U | None:

													if key in self.cache:
																			self.hits += 1
																			__lowerCAmelCase        =       self.cache[key]
																			__lowerCAmelCase        =       self.list.remove(self.cache[key]							)
																			assert node == value_node

																			# node is guaranteed not None because it is in self.cache
																			assert node is not None
																			self.list.add(snake_case_							)
																			return node.val
													self.miss += 1
													return None




							def  A__		(				self  ,							snake_case_  ,							snake_case_							)   ->       None:

													if key not in self.cache:
																			if self.num_keys >= self.capacity:
																									# delete first node when over capacity
																									__lowerCAmelCase        =       self.list.head.next

																									# guaranteed to have a non-None first node when num_keys > 0
																									# explain to type checker via assertions
																									assert first_node is not None
																									assert first_node.key is not None
																									assert self.list.remove(snake_case_							) is not None
																									# first_node guaranteed to be in list

																									del self.cache[first_node.key]
																									self.num_keys -= 1
																			__lowerCAmelCase        =       DoubleLinkedListNode(snake_case_  ,							snake_case_							)
																			self.list.add(self.cache[key]							)
																			self.num_keys += 1

													else:
																			__lowerCAmelCase        =       self.list.remove(self.cache[key]							)
																			assert node is not None  # node guaranteed to be in list
																			__lowerCAmelCase        =       value
																			self.list.add(snake_case_							)




							@classmethod
							def  A__		(				cls  ,							snake_case_ = 128							)   ->       Callable[[Callable[[T], U]], Callable[..., U]]:

													def cache_decorator_inner(snake_case_							) -> Callable[..., U]:
																			def cache_decorator_wrapper(*snake_case_							) -> U:
																									if func not in cls.decorator_function_to_instance_map:
																															__lowerCAmelCase        =       LFUCache(snake_case_							)

																									__lowerCAmelCase        =       cls.decorator_function_to_instance_map[func].get(args[0]							)
																									if result is None:
																															__lowerCAmelCase        =       func(*snake_case_							)
																															cls.decorator_function_to_instance_map[func].put(args[0]  ,							snake_case_							)
																									return result

																			def cache_info() -> LFUCache[T, U]:
																									return cls.decorator_function_to_instance_map[func]

																			setattr(snake_case_  ,							"""cache_info"""  ,							snake_case_							)  # noqa: B010

																			return cache_decorator_wrapper

													return cache_decorator_inner


if __name__ == "__main__":
	import doctest

	doctest.testmod()


"""simple docstring"""
import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def 				lowercase      (_lowerCAmelCase		):
						__lowerCAmelCase        =       args.serialization_dir
						__lowerCAmelCase        =       args.pruning_method
						__lowerCAmelCase        =       args.threshold

						__lowerCAmelCase        =       torch.load(os.path.join(_lowerCAmelCase				,  """pytorch_model.bin"""		)				,  map_location="""cpu"""		)

						__lowerCAmelCase        =       0  # Number of remaining (not pruned) params in the encoder
						__lowerCAmelCase        =       0  # Number of params in the encoder

						print("""name""".ljust(60				,  """ """		)				,  """Remaining Weights %"""				,  """Remaining Weight"""		)
						for name, param in st.items():
												if "encoder" not in name:
																		continue

												if "mask_scores" in name:
																		if pruning_method == "topK":
																								__lowerCAmelCase        =       TopKBinarizer.apply(_lowerCAmelCase				,  _lowerCAmelCase		).sum().item()
																		elif pruning_method == "sigmoied_threshold":
																								__lowerCAmelCase        =       ThresholdBinarizer.apply(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		).sum().item()
																		elif pruning_method == "l0":
																								__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        =       (mask > 0.0).sum().item()
																		else:
																								raise ValueError("""Unknown pruning method"""		)
																		remaining_count += mask_ones
																		print(name.ljust(60				,  """ """		)				,  str(round(100 * mask_ones / param.numel()				,  3		)		).ljust(20				,  """ """		)				,  str(_lowerCAmelCase		)		)
												else:
																		encoder_count += param.numel()
																		if "bias" in name or "LayerNorm" in name:
																								remaining_count += param.numel()

						print(""""""		)
						print("""Remaining Weights (global) %: """				,  100 * remaining_count / encoder_count		)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_			      =					argparse.ArgumentParser()

			parser.add_argument(
			    '''--pruning_method''',
			    choices=['''l0''', '''topK''', '''sigmoied_threshold'''],
			    type=str,
			    required=True,
			    help=(
			        '''Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'''
			        ''' pruning)'''
			    ),
			)
			parser.add_argument(
			    '''--threshold''',
			    type=float,
			    required=False,
			    help=(
			        '''For `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(
			    '''--serialization_dir''',
			    type=str,
			    required=True,
			    help='''Folder containing the model that was previously fine-pruned''',
			)

			SCREAMING_SNAKE_CASE_			      =					parser.parse_args()

			main(args)
