



'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer



@dataclass
class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						42






class 		lowerCamelCase__      (						A	,   A     ):




					'''simple docstring'''



					@register_to_config
					def __init__(  self				:	Any     ,  UpperCamelCase_				:	int = 3     ,  UpperCamelCase_				:	int = 3     ,  UpperCamelCase_				:	Tuple[str] = ("DownEncoderBlock2D",)     ,  UpperCamelCase_				:	Tuple[str] = ("UpDecoderBlock2D",)     ,  UpperCamelCase_				:	Tuple[int] = (64,)     ,  UpperCamelCase_				:	int = 1     ,  UpperCamelCase_				:	str = "silu"     ,  UpperCamelCase_				:	int = 3     ,  UpperCamelCase_				:	int = 32     ,  UpperCamelCase_				:	int = 256     ,  UpperCamelCase_				:	int = 32     ,  UpperCamelCase_				:	Optional[int] = None     ,  UpperCamelCase_				:	float = 0.1_82_15     ,  UpperCamelCase_				:	str = "group"     ,  )  ->       Optional[Any]:



						'''simple docstring'''





						super().__init__()

						# pass init params to Encoder
						_lowercase					:       List[str]       =		Encoder(
						    in_channels=UpperCamelCase_     ,  out_channels=UpperCamelCase_     ,  down_block_types=UpperCamelCase_     ,  block_out_channels=UpperCamelCase_     ,  layers_per_block=UpperCamelCase_     ,  act_fn=UpperCamelCase_     ,  norm_num_groups=UpperCamelCase_     ,  double_z=UpperCamelCase_     ,  )

						_lowercase					:       Any       =		vq_embed_dim if vq_embed_dim is not None else latent_channels

						_lowercase					:       Tuple       =		nn.Convad(UpperCamelCase_     ,  UpperCamelCase_     ,  1					)
						_lowercase					:       List[str]       =		VectorQuantizer(UpperCamelCase_     ,  UpperCamelCase_     ,  beta=0.25     ,  remap=UpperCamelCase_     ,  sane_index_shape=UpperCamelCase_					)
						_lowercase					:       Optional[Any]       =		nn.Convad(UpperCamelCase_     ,  UpperCamelCase_     ,  1					)

						# pass init params to Decoder
						_lowercase					:       Tuple       =		Decoder(
						    in_channels=UpperCamelCase_     ,  out_channels=UpperCamelCase_     ,  up_block_types=UpperCamelCase_     ,  block_out_channels=UpperCamelCase_     ,  layers_per_block=UpperCamelCase_     ,  act_fn=UpperCamelCase_     ,  norm_num_groups=UpperCamelCase_     ,  norm_type=UpperCamelCase_     ,  )




					@apply_forward_hook
					def 						__UpperCAmelCase     (  self				:	Any     ,  UpperCamelCase_				:	torch.FloatTensor     ,  UpperCamelCase_				:	bool = True					)  ->       VQEncoderOutput:



						'''simple docstring'''





						_lowercase					:       List[str]       =		self.encoder(UpperCamelCase_					)
						_lowercase					:       Optional[int]       =		self.quant_conv(UpperCamelCase_					)

						if not return_dict:
							return (h,)

						return VQEncoderOutput(latents=UpperCamelCase_					)




					@apply_forward_hook
					def 						__UpperCAmelCase     (  self				:	Any     ,  UpperCamelCase_				:	torch.FloatTensor     ,  UpperCamelCase_				:	bool = False     ,  UpperCamelCase_				:	bool = True					)  ->       Union[DecoderOutput, torch.FloatTensor]:



						'''simple docstring'''





						if not force_not_quantize:
							_lowercase					:       int       =		self.quantize(UpperCamelCase_					)
						else:
							_lowercase					:       Optional[Any]       =		h
						_lowercase					:       Tuple       =		self.post_quant_conv(UpperCamelCase_					)
						_lowercase					:       List[str]       =		self.decoder(UpperCamelCase_     ,  quant if self.config.norm_type == 'spatial' else None					)

						if not return_dict:
							return (dec,)

						return DecoderOutput(sample=UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  UpperCamelCase_				:	torch.FloatTensor     ,  UpperCamelCase_				:	bool = True					)  ->       Union[DecoderOutput, torch.FloatTensor]:



						'''simple docstring'''





						_lowercase					:       Any       =		sample
						_lowercase					:       Optional[int]       =		self.encode(UpperCamelCase_					).latents
						_lowercase					:       List[Any]       =		self.decode(UpperCamelCase_					).sample

						if not return_dict:
							return (dec,)

						return DecoderOutput(sample=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)
