



'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A				:    Optional[Any]     						=logging.get_logger(__name__)

_A				:    Optional[int]     						={
    '''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""gpt_bigcode"""
					A_						    =						["""past_key_values"""]
					A_						    =						{
					    """hidden_size""": """n_embd""",
					    """max_position_embeddings""": """n_positions""",
					    """num_attention_heads""": """n_head""",
					    """num_hidden_layers""": """n_layer""",
					}




					def __init__(  self				:	int     ,  UpperCamelCase_				:	List[str]=5_0257     ,  UpperCamelCase_				:	Optional[int]=1024     ,  UpperCamelCase_				:	Union[str, Any]=768     ,  UpperCamelCase_				:	Optional[int]=12     ,  UpperCamelCase_				:	Tuple=12     ,  UpperCamelCase_				:	str=None     ,  UpperCamelCase_				:	int="gelu_pytorch_tanh"     ,  UpperCamelCase_				:	List[str]=0.1     ,  UpperCamelCase_				:	Union[str, Any]=0.1     ,  UpperCamelCase_				:	Tuple=0.1     ,  UpperCamelCase_				:	List[Any]=1E-5     ,  UpperCamelCase_				:	Dict=0.02     ,  UpperCamelCase_				:	int=True     ,  UpperCamelCase_				:	Optional[int]=True     ,  UpperCamelCase_				:	str=5_0256     ,  UpperCamelCase_				:	Tuple=5_0256     ,  UpperCamelCase_				:	Dict=True     ,  UpperCamelCase_				:	Optional[int]=True     ,  UpperCamelCase_				:	int=True     ,  **UpperCamelCase_				:	str     ,  )  ->       str:



						'''simple docstring'''





						_lowercase					:       List[str]       =		vocab_size
						_lowercase					:       Optional[int]       =		n_positions
						_lowercase					:       Any       =		n_embd
						_lowercase					:       Union[str, Any]       =		n_layer
						_lowercase					:       Optional[Any]       =		n_head
						_lowercase					:       Dict       =		n_inner
						_lowercase					:       Dict       =		activation_function
						_lowercase					:       Union[str, Any]       =		resid_pdrop
						_lowercase					:       Union[str, Any]       =		embd_pdrop
						_lowercase					:       str       =		attn_pdrop
						_lowercase					:       Optional[int]       =		layer_norm_epsilon
						_lowercase					:       List[Any]       =		initializer_range
						_lowercase					:       Any       =		scale_attn_weights
						_lowercase					:       Any       =		use_cache
						_lowercase					:       int       =		attention_softmax_in_fpaa
						_lowercase					:       Optional[Any]       =		scale_attention_softmax_in_fpaa
						_lowercase					:       str       =		multi_query

						_lowercase					:       List[Any]       =		bos_token_id
						_lowercase					:       Optional[int]       =		eos_token_id

						super().__init__(bos_token_id=UpperCamelCase_     ,  eos_token_id=UpperCamelCase_     ,  **UpperCamelCase_					)





'''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)
