



'''simple docstring'''

import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer



class 		lowerCamelCase__      (						tf.keras.layers.Layer     ):




					'''simple docstring'''



					def __init__(  self				:	Tuple     ,  UpperCamelCase_				:	Dict[str, int]     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	int = None     ,  UpperCamelCase_				:	int = None					)  ->       Any:



						'''simple docstring'''





						super().__init__()
						_lowercase					:       Tuple       =		pad_token_id
						_lowercase					:       int       =		max_length
						_lowercase					:       List[Any]       =		vocab
						_lowercase					:       Tuple       =		merges
						_lowercase					:       int       =		BytePairTokenizer(UpperCamelCase_     ,  UpperCamelCase_     ,  sequence_length=UpperCamelCase_					)




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	Any     ,  UpperCamelCase_				:	GPTaTokenizer     ,  *UpperCamelCase_				:	List[str]     ,  **UpperCamelCase_				:	Union[str, Any]					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		[' '.join(UpperCamelCase_					) for m in tokenizer.bpe_ranks.keys()]
						_lowercase					:       int       =		tokenizer.get_vocab()
						return cls(UpperCamelCase_     ,  UpperCamelCase_     ,  *UpperCamelCase_     ,  **UpperCamelCase_					)




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	int     ,  UpperCamelCase_				:	Union[str, os.PathLike]     ,  *UpperCamelCase_				:	Union[str, Any]     ,  **UpperCamelCase_				:	Dict					)  ->       Dict:



						'''simple docstring'''





						_lowercase					:       int       =		GPTaTokenizer.from_pretrained(UpperCamelCase_     ,  *UpperCamelCase_     ,  **UpperCamelCase_					)
						return cls.from_tokenizer(UpperCamelCase_     ,  *UpperCamelCase_     ,  **UpperCamelCase_					)




					@classmethod
					def 						__UpperCAmelCase     (  cls				:	List[Any]     ,  UpperCamelCase_				:	Any					)  ->       List[str]:



						'''simple docstring'''





						return cls(**UpperCamelCase_					)




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



						'''simple docstring'''





						return {
						    "vocab": self.vocab,
						    "merges": self.merges,
						    "max_length": self.max_length,
						    "pad_token_id": self.pad_token_id,
						}




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	int = None					)  ->       List[Any]:



						'''simple docstring'''





						_lowercase					:       Any       =		self.tf_tokenizer(UpperCamelCase_					)
						_lowercase					:       Optional[Any]       =		tf.ones_like(UpperCamelCase_					)

						if self.pad_token_id is not None:
							# pad the tokens up to max length
							_lowercase					:       Optional[int]       =		max_length if max_length is not None else self.max_length

							if max_length is not None:
								_lowercase					:       List[Any]       =		pad_model_inputs(
								    UpperCamelCase_     ,  max_seq_length=UpperCamelCase_     ,  pad_value=self.pad_token_id					)

						return {"attention_mask": attention_mask, "input_ids": input_ids}










'''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)
