



'''simple docstring'''

from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass



@dataclass
class 		lowerCamelCase__      :




					'''simple docstring'''



					A_						    =						42
					A_						    =						42






class 		lowerCamelCase__      :




					'''simple docstring'''



					def __init__(  self				:	Any     ,  UpperCamelCase_				:	int					)  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       list[list[Edge]]       =		[[] for _ in range(UpperCAmelCase_					)]
						_lowercase					:       Dict       =		size




					def __getitem__(  self				:	Any     ,  UpperCamelCase_				:	int					)  ->       Optional[Any]:



						'''simple docstring'''





						return iter(self._graph[vertex]					)




					@property
					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       str:



						'''simple docstring'''





						return self._size




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	int					)  ->       int:



						'''simple docstring'''





						if weight not in (0, 1):
							raise ValueError('Edge weight must be either 0 or 1.'					)

						if to_vertex < 0 or to_vertex >= self.size:
							raise ValueError('Vertex indexes must be in [0; size).'					)

						self._graph[from_vertex].append(Edge(UpperCAmelCase_     ,  UpperCAmelCase_					)					)




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	int					)  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		deque([start_vertex]					)
						_lowercase					:       list[int | None]       =		[None] * self.size
						_lowercase					:       Union[str, Any]       =		0

						while queue:
							_lowercase					:       Dict       =		queue.popleft()
							_lowercase					:       Any       =		distances[current_vertex]
							if current_distance is None:
								continue

							for edge in self[current_vertex]:
								_lowercase					:       Optional[int]       =		current_distance + edge.weight
								_lowercase					:       Tuple       =		distances[edge.destination_vertex]
								if (
								    isinstance(UpperCAmelCase_     ,  UpperCAmelCase_					)
								    and new_distance >= dest_vertex_distance
								):
									continue
								_lowercase					:       Dict       =		new_distance
								if edge.weight == 0:
									queue.appendleft(edge.destination_vertex					)
								else:
									queue.append(edge.destination_vertex					)

						if distances[finish_vertex] is None:
							raise ValueError('No path from start_vertex to finish_vertex.'					)

						return distances[finish_vertex]


if __name__ == "__main__":
		import doctest

		doctest.testmod()










'''simple docstring'''

import argparse
import os
import shutil

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







def 						__UpperCamelCase				(							_lowercase					) ->			List[Any]:
	_lowercase					:       Tuple       =		args.pruning_method
	_lowercase					:       int       =		args.threshold

	_lowercase					:       str       =		args.model_name_or_path.rstrip('/'					)
	_lowercase					:       Dict       =		args.target_model_path

	print(f'''Load fine-pruned model from {model_name_or_path}'''					)
	_lowercase					:       str       =		torch.load(os.path.join(_lowercase,				'pytorch_model.bin'					)					)
	_lowercase					:       List[Any]       =		{}

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

	if target_model_path is None:
		_lowercase					:       List[Any]       =		os.path.join(
		    os.path.dirname(_lowercase					),				f'''bertarized_{os.path.basename(_lowercase					)}'''					)

	if not os.path.isdir(_lowercase					):
		shutil.copytree(_lowercase,				_lowercase					)
		print(f'''\nCreated folder {target_model_path}'''					)

	torch.save(_lowercase,				os.path.join(_lowercase,				'pytorch_model.bin'					)					)
	print('\nPruned model saved! See you later!'					)


if __name__ == "__main__":
		_A				:    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''',
		)

		_A				:    List[Any]     						=parser.parse_args()

		main(args)
