



'''simple docstring'''

import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


_A				:    Dict     						=logging.getLogger(__name__)



@dataclass(frozen=A     )
class 		lowerCamelCase__      :




					'''simple docstring'''



					A_						    =						42
					A_						    =						42
					A_						    =						None
					A_						    =						None
					A_						    =						None






@dataclass(frozen=A     )
class 		lowerCamelCase__      :




					'''simple docstring'''



					A_						    =						42
					A_						    =						None
					A_						    =						None
					A_						    =						None
					A_						    =						None


if is_torch_available():
		import torch
		from torch.utils.data import Dataset



		class 		lowerCamelCase__      (						A     ):




							'''simple docstring'''



							A_						    =						42




							def __init__(  self				:	Any     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	PreTrainedTokenizer     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Optional[int] = None     ,  UpperCamelCase_				:	Dict=False     ,  UpperCamelCase_				:	bool = False     ,  )  ->       Union[str, Any]:



								'''simple docstring'''





								_lowercase					:       Optional[Any]       =		hans_processors[task]()

								_lowercase					:       Dict       =		os.path.join(
								    UpperCamelCase_     ,  'cached_{}_{}_{}_{}'.format(
								        'dev' if evaluate else 'train'     ,  tokenizer.__class__.__name__     ,  str(UpperCamelCase_					)     ,  UpperCamelCase_     ,  )     ,  )
								_lowercase					:       Union[str, Any]       =		processor.get_labels()
								if tokenizer.__class__ in (
								    RobertaTokenizer,
								    RobertaTokenizerFast,
								    XLMRobertaTokenizer,
								    BartTokenizer,
								    BartTokenizerFast,
								):
									# HACK(label indices are swapped in RoBERTa pretrained model)
									_lowercase  ,    _lowercase					:       Optional[Any]       =		label_list[2], label_list[1]
								_lowercase					:       int       =		label_list

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								_lowercase					:       Optional[int]       =		cached_features_file + '.lock'
								with FileLock(UpperCamelCase_					):
									if os.path.exists(UpperCamelCase_					) and not overwrite_cache:
										logger.info(F'''Loading features from cached file {cached_features_file}'''					)
										_lowercase					:       List[Any]       =		torch.load(UpperCamelCase_					)
									else:
										logger.info(F'''Creating features from dataset file at {data_dir}'''					)

										_lowercase					:       Optional[int]       =		(
										    processor.get_dev_examples(UpperCamelCase_					) if evaluate else processor.get_train_examples(UpperCamelCase_					)
										)

										logger.info('Training examples: %s'     ,  len(UpperCamelCase_					)					)
										_lowercase					:       Optional[int]       =		hans_convert_examples_to_features(UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
										logger.info('Saving features into cached file %s'     ,  UpperCamelCase_					)
										torch.save(self.features     ,  UpperCamelCase_					)




							def __len__(  self				:	Tuple					)  ->       int:



								'''simple docstring'''





								return len(self.features					)




							def __getitem__(  self				:	List[Any]     ,  UpperCamelCase_				:	Tuple					)  ->       InputFeatures:



								'''simple docstring'''





								return self.features[i]




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



								'''simple docstring'''





								return self.label_list


if is_tf_available():
		import tensorflow as tf



		class 		lowerCamelCase__      :




							'''simple docstring'''



							A_						    =						42




							def __init__(  self				:	int     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	PreTrainedTokenizer     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Optional[int] = 128     ,  UpperCamelCase_				:	Tuple=False     ,  UpperCamelCase_				:	bool = False     ,  )  ->       Optional[Any]:



								'''simple docstring'''





								_lowercase					:       Union[str, Any]       =		hans_processors[task]()
								_lowercase					:       Dict       =		processor.get_labels()
								if tokenizer.__class__ in (
								    RobertaTokenizer,
								    RobertaTokenizerFast,
								    XLMRobertaTokenizer,
								    BartTokenizer,
								    BartTokenizerFast,
								):
									# HACK(label indices are swapped in RoBERTa pretrained model)
									_lowercase  ,    _lowercase					:       Any       =		label_list[2], label_list[1]
								_lowercase					:       List[Any]       =		label_list

								_lowercase					:       List[str]       =		processor.get_dev_examples(UpperCamelCase_					) if evaluate else processor.get_train_examples(UpperCamelCase_					)
								_lowercase					:       int       =		hans_convert_examples_to_features(UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)

								def gen():
									for ex_index, ex in tqdm.tqdm(enumerate(self.features					)     ,  desc='convert examples to features'					):
										if ex_index % 1_0000 == 0:
											logger.info('Writing example %d of %d' % (ex_index, len(UpperCamelCase_					))					)

										yield (
										    {
										        "example_id": 0,
										        "input_ids": ex.input_ids,
										        "attention_mask": ex.attention_mask,
										        "token_type_ids": ex.token_type_ids,
										    },
										    ex.label,
										)

								_lowercase					:       str       =		tf.data.Dataset.from_generator(
								    UpperCamelCase_     ,  (
								        {
								            'example_id': tf.intaa,
								            'input_ids': tf.intaa,
								            'attention_mask': tf.intaa,
								            'token_type_ids': tf.intaa,
								        },
								        tf.intaa,
								    )     ,  (
								        {
								            'example_id': tf.TensorShape([]					),
								            'input_ids': tf.TensorShape([None, None]					),
								            'attention_mask': tf.TensorShape([None, None]					),
								            'token_type_ids': tf.TensorShape([None, None]					),
								        },
								        tf.TensorShape([]					),
								    )     ,  )




							def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Optional[Any]:



								'''simple docstring'''





								return self.dataset




							def __len__(  self				:	str					)  ->       Optional[int]:



								'''simple docstring'''





								return len(self.features					)




							def __getitem__(  self				:	Dict     ,  UpperCamelCase_				:	Union[str, Any]					)  ->       InputFeatures:



								'''simple docstring'''





								return self.features[i]




							def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       List[Any]:



								'''simple docstring'''





								return self.label_list



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	Union[str, Any]     ,  UpperCamelCase_				:	int					)  ->       Tuple:



						'''simple docstring'''





						return self._create_examples(self._read_tsv(os.path.join(UpperCamelCase_     ,  'heuristics_train_set.txt'					)					)     ,  'train'					)




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



						'''simple docstring'''





						return self._create_examples(self._read_tsv(os.path.join(UpperCamelCase_     ,  'heuristics_evaluation_set.txt'					)					)     ,  'dev'					)




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



						'''simple docstring'''





						return ["contradiction", "entailment", "neutral"]




					def 						__UpperCAmelCase     (  self				:	List[str]     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Tuple					)  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       List[str]       =		[]
						for i, line in enumerate(UpperCamelCase_					):
							if i == 0:
								continue
							_lowercase					:       Tuple       =		'%s-%s' % (set_type, line[0])
							_lowercase					:       Dict       =		line[5]
							_lowercase					:       Union[str, Any]       =		line[6]
							_lowercase					:       Optional[Any]       =		line[7][2:] if line[7].startswith('ex'					) else line[7]
							_lowercase					:       Tuple       =		line[0]
							examples.append(InputExample(guid=UpperCamelCase_     ,  text_a=UpperCamelCase_     ,  text_b=UpperCamelCase_     ,  label=UpperCamelCase_     ,  pairID=UpperCamelCase_					)					)
						return examples







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase,				_lowercase,				) ->			Any:
	_lowercase					:       Tuple       =		{label: i for i, label in enumerate(_lowercase					)}

	_lowercase					:       int       =		[]
	for ex_index, example in tqdm.tqdm(enumerate(_lowercase					),				desc='convert examples to features'					):
		if ex_index % 1_0000 == 0:
			logger.info('Writing example %d' % (ex_index)					)

		_lowercase					:       Optional[Any]       =		tokenizer(
		    example.text_a,				example.text_b,				add_special_tokens=_lowercase,				max_length=_lowercase,				padding='max_length',				truncation=_lowercase,				return_overflowing_tokens=_lowercase,				)

		_lowercase					:       Optional[Any]       =		label_map[example.label] if example.label in label_map else 0

		_lowercase					:       Dict       =		int(example.pairID					)

		features.append(InputFeatures(**_lowercase,				label=_lowercase,				pairID=_lowercase					)					)

	for i, example in enumerate(examples[:5]					):
		logger.info('*** Example ***'					)
		logger.info(f'''guid: {example}'''					)
		logger.info(f'''features: {features[i]}'''					)

	return features


_A				:    List[str]     						={
    '''hans''': 3,
}






_A				:    List[str]     						={
    '''hans''': HansProcessor,
}





'''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)
