





'''simple docstring'''




import argparse
import os
import shutil

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




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->Dict:
	snake_case__	       =   args.pruning_method
	snake_case__	       =   args.threshold

	snake_case__	       =   args.model_name_or_path.rstrip('/'							)
	snake_case__	       =   args.target_model_path

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

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

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

			main(args)








import os
from collections import deque

import torch
from torch.utils.data import Dataset


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,a_=""    ,a_="train"						):







						"""simple docstring"""
						assert os.path.isdir(a_						)

						lowerCAmelCase__		  =  []
						lowerCAmelCase__		  =  os.listdir(a_						)
						for story_filename in story_filenames_list:
										if "summary" in story_filename:
														continue
										lowerCAmelCase__		  =  os.path.join(a_    ,a_						)
										if not os.path.isfile(a_						):
														continue
										self.documents.append(a_						)
		def __len__(							self						):







						"""simple docstring"""
						return len(self.documents						)



		def __getitem__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.documents[idx]
						lowerCAmelCase__		  =  document_path.split('/'						)[-1]
						with open(a_    ,encoding='utf-8'						) as source:
										lowerCAmelCase__		  =  source.read()
										lowerCAmelCase__  ,   lowerCAmelCase__		  =  process_story(a_						)
						return document_name, story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: len(snake_case__					) != 0    ,			[line.strip() for line in raw_story.split('\n'					)]					)					)

				# for some unknown reason some lines miss a period, add it
				lowerCAmelCase__		  =  [_add_missing_period(snake_case__					) for line in nonempty_lines]

				# gather article lines
				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  deque(snake_case__					)
				while True:
								try:
												lowerCAmelCase__		  =  lines.popleft()
												if element.startswith('@highlight'					):
																break
												story_lines.append(snake_case__					)
								except IndexError:
												# if "@highlight" is absent from the file we pop
												# all elements until there is None, raising an exception.
												return story_lines, []

    # gather summary lines
				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: not t.startswith('@highlight'					)    ,			snake_case__					)					)

				return story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     Optional[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
				if line.startswith('@highlight'					):
								return line
				if line[-1] in END_TOKENS:
								return line
				return line + "."







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





				if len(snake_case__					) > block_size:
								return sequence[:block_size]
				else:
								sequence.extend([pad_token_id] * (block_size - len(snake_case__					))					)
								return sequence







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  torch.ones_like(snake_case__					)
				lowerCAmelCase__		  =  sequence == pad_token_id
				lowerCAmelCase__		  =  0
				return mask







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in story_lines]
				lowerCAmelCase__		  =  [token for sentence in story_lines_token_ids for token in sentence]
				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in summary_lines]
				lowerCAmelCase__		  =  [token for sentence in summary_lines_token_ids for token in sentence]

				return story_token_ids, summary_token_ids







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Union[str, Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				for sequence in batch:
								lowerCAmelCase__		  =  -1
								lowerCAmelCase__		  =  []
								for s in sequence:
												if s == separator_token_id:
																sentence_num += 1
												embeddings.append(sentence_num % 2					)
								batch_embeddings.append(snake_case__					)
				return torch.tensor(snake_case__					)
