



'''simple docstring'''

import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


_A				:    Dict     						=logging.getLogger(__name__)







def 						__UpperCamelCase				(							_lowercase=2,				_lowercase=3,				_lowercase=16,				_lowercase = 10,				_lowercase = 2					) ->			Tuple:

	def get_dataset(_lowercase					):
		_lowercase					:       int       =		torch.randn(batch_size * n_batches,				1					)
		return TensorDataset(_lowercase,				a * x + b + 0.1 * torch.randn(batch_size * n_batches,				1					)					)

	_lowercase					:       Dict       =		get_dataset(_lowercase					)
	_lowercase					:       Dict       =		get_dataset(_lowercase					)
	_lowercase					:       str       =		DataLoader(_lowercase,				shuffle=_lowercase,				batch_size=_lowercase,				num_workers=4					)
	_lowercase					:       Any       =		DataLoader(_lowercase,				shuffle=_lowercase,				batch_size=_lowercase,				num_workers=4					)
	return (train_dataloader, valid_dataloader)







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase,				_lowercase,				_lowercase,				_lowercase=None					) ->			Dict:
	_lowercase					:       Any       =		[]
	for epoch in range(_lowercase					):
		# Train quickly
		model.train()
		for batch in dataloader:
			_lowercase  ,    _lowercase					:       List[Any]       =		batch
			_lowercase					:       Dict       =		model(_lowercase					)
			_lowercase					:       Any       =		torch.nn.functional.mse_loss(_lowercase,				_lowercase					)
			accelerator.backward(_lowercase					)
			optimizer.step()
			optimizer.zero_grad()
		rands.append(random.random()					)  # Introduce some randomness
		if scheduler is not None:
			scheduler.step()
	return rands



class 		lowerCamelCase__      (						nn.Module     ):




					'''simple docstring'''



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



						'''simple docstring'''





						super().__init__()
						_lowercase					:       Any       =		nn.Parameter(torch.randn(1					)					)
						_lowercase					:       Optional[Any]       =		nn.Parameter(torch.randn(1					)					)




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



						'''simple docstring'''





						return x * self.a + self.b






class 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	str					)  ->       int:



						'''simple docstring'''





						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42					)
							_lowercase					:       Union[str, Any]       =		DummyModel()
							_lowercase					:       List[Any]       =		torch.optim.Adam(params=model.parameters()     ,  lr=1E-3					)
							_lowercase  ,    _lowercase					:       Union[str, Any]       =		dummy_dataloaders()
							_lowercase					:       List[Any]       =		ProjectConfiguration(total_limit=1     ,  project_dir=UpperCamelCase_     ,  automatic_checkpoint_naming=UpperCamelCase_					)
							# Train baseline
							_lowercase					:       List[Any]       =		Accelerator(project_config=UpperCamelCase_					)
							_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       Union[str, Any]       =		accelerator.prepare(
							    UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
							# Save initial
							accelerator.save_state()

							# Save second state
							accelerator.save_state()
							self.assertEqual(len(os.listdir(accelerator.project_dir					)					)     ,  1					)




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



						'''simple docstring'''





						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42					)
							_lowercase					:       Any       =		DummyModel()
							_lowercase					:       Union[str, Any]       =		torch.optim.Adam(params=model.parameters()     ,  lr=1E-3					)
							_lowercase  ,    _lowercase					:       Dict       =		dummy_dataloaders()
							# Train baseline
							_lowercase					:       List[Any]       =		Accelerator()
							_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       Dict       =		accelerator.prepare(
							    UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
							# Save initial
							_lowercase					:       Any       =		os.path.join(UpperCamelCase_     ,  'initial'					)
							accelerator.save_state(UpperCamelCase_					)
							((_lowercase)  ,    (_lowercase))					:       Union[str, Any]       =		model.a.item(), model.b.item()
							_lowercase					:       Optional[Any]       =		optimizer.state_dict()
							_lowercase					:       int       =		train(3     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
							((_lowercase)  ,    (_lowercase))					:       str       =		model.a.item(), model.b.item()
							_lowercase					:       Union[str, Any]       =		optimizer.state_dict()

							# Train partially
							set_seed(42					)
							_lowercase					:       int       =		DummyModel()
							_lowercase					:       int       =		torch.optim.Adam(params=model.parameters()     ,  lr=1E-3					)
							_lowercase  ,    _lowercase					:       Any       =		dummy_dataloaders()
							_lowercase					:       List[str]       =		Accelerator()
							_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       List[str]       =		accelerator.prepare(
							    UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
							accelerator.load_state(UpperCamelCase_					)
							((_lowercase)  ,    (_lowercase))					:       List[Any]       =		model.a.item(), model.b.item()
							_lowercase					:       int       =		optimizer.state_dict()
							self.assertEqual(UpperCamelCase_     ,  UpperCamelCase_					)
							self.assertEqual(UpperCamelCase_     ,  UpperCamelCase_					)
							self.assertEqual(UpperCamelCase_     ,  UpperCamelCase_					)

							_lowercase					:       str       =		train(2     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
							# Save everything
							_lowercase					:       Tuple       =		os.path.join(UpperCamelCase_     ,  'checkpoint'					)
							accelerator.save_state(UpperCamelCase_					)

							# Load everything back in and make sure all states work
							accelerator.load_state(UpperCamelCase_					)
							test_rands += train(1     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
							((_lowercase)  ,    (_lowercase))					:       str       =		model.a.item(), model.b.item()
							_lowercase					:       int       =		optimizer.state_dict()
							self.assertEqual(UpperCamelCase_     ,  UpperCamelCase_					)
							self.assertEqual(UpperCamelCase_     ,  UpperCamelCase_					)
							self.assertEqual(UpperCamelCase_     ,  UpperCamelCase_					)
							self.assertEqual(UpperCamelCase_     ,  UpperCamelCase_					)




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



						'''simple docstring'''





						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42					)
							_lowercase					:       Optional[Any]       =		DummyModel()
							_lowercase					:       List[str]       =		torch.optim.Adam(params=model.parameters()     ,  lr=1E-3					)
							_lowercase  ,    _lowercase					:       Dict       =		dummy_dataloaders()
							_lowercase					:       Union[str, Any]       =		ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase_					)

							# Train baseline
							_lowercase					:       Optional[Any]       =		Accelerator(project_dir=UpperCamelCase_     ,  project_config=UpperCamelCase_					)
							_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       int       =		accelerator.prepare(
							    UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
							# Save initial
							accelerator.save_state()
							((_lowercase)  ,    (_lowercase))					:       int       =		model.a.item(), model.b.item()
							_lowercase					:       Dict       =		optimizer.state_dict()
							_lowercase					:       Optional[int]       =		train(3     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
							((_lowercase)  ,    (_lowercase))					:       Optional[int]       =		model.a.item(), model.b.item()
							_lowercase					:       List[str]       =		optimizer.state_dict()

							# Train partially
							set_seed(42					)
							_lowercase					:       Tuple       =		DummyModel()
							_lowercase					:       List[Any]       =		torch.optim.Adam(params=model.parameters()     ,  lr=1E-3					)
							_lowercase  ,    _lowercase					:       List[Any]       =		dummy_dataloaders()
							_lowercase					:       Optional[Any]       =		ProjectConfiguration(iteration=1     ,  automatic_checkpoint_naming=UpperCamelCase_					)
							_lowercase					:       Optional[int]       =		Accelerator(project_dir=UpperCamelCase_     ,  project_config=UpperCamelCase_					)
							_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       Dict       =		accelerator.prepare(
							    UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
							accelerator.load_state(os.path.join(UpperCamelCase_     ,  'checkpoints'     ,  'checkpoint_0'					)					)
							((_lowercase)  ,    (_lowercase))					:       str       =		model.a.item(), model.b.item()
							_lowercase					:       Tuple       =		optimizer.state_dict()
							self.assertEqual(UpperCamelCase_     ,  UpperCamelCase_					)
							self.assertEqual(UpperCamelCase_     ,  UpperCamelCase_					)
							self.assertEqual(UpperCamelCase_     ,  UpperCamelCase_					)

							_lowercase					:       List[str]       =		train(2     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
							# Save everything
							accelerator.save_state()

							# Load everything back in and make sure all states work
							accelerator.load_state(os.path.join(UpperCamelCase_     ,  'checkpoints'     ,  'checkpoint_1'					)					)
							test_rands += train(1     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
							((_lowercase)  ,    (_lowercase))					:       Any       =		model.a.item(), model.b.item()
							_lowercase					:       List[Any]       =		optimizer.state_dict()
							self.assertEqual(UpperCamelCase_     ,  UpperCamelCase_					)
							self.assertEqual(UpperCamelCase_     ,  UpperCamelCase_					)
							self.assertEqual(UpperCamelCase_     ,  UpperCamelCase_					)
							self.assertEqual(UpperCamelCase_     ,  UpperCamelCase_					)




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



						'''simple docstring'''





						_lowercase					:       str       =		torch.tensor([1, 2, 3]					)
						_lowercase					:       Union[str, Any]       =		torch.tensor([2, 3, 4]					)
						_lowercase					:       Union[str, Any]       =		DummyModel()
						_lowercase					:       Union[str, Any]       =		torch.optim.Adam(net.parameters()					)
						_lowercase					:       int       =		Accelerator()
						with self.assertRaises(UpperCamelCase_					) as ve:
							accelerator.register_for_checkpointing(UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
						_lowercase					:       int       =		str(ve.exception					)
						self.assertTrue('Item at index 0' in message					)
						self.assertTrue('Item at index 1' in message					)
						self.assertFalse('Item at index 2' in message					)
						self.assertFalse('Item at index 3' in message					)




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Any:



						'''simple docstring'''





						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42					)
							_lowercase					:       Dict       =		DummyModel()
							_lowercase					:       List[str]       =		torch.optim.Adam(params=model.parameters()     ,  lr=1E-3					)
							_lowercase					:       List[str]       =		torch.optim.lr_scheduler.StepLR(UpperCamelCase_     ,  step_size=1     ,  gamma=0.99					)
							_lowercase  ,    _lowercase					:       str       =		dummy_dataloaders()
							_lowercase					:       Any       =		ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase_					)
							# Train baseline
							_lowercase					:       List[str]       =		Accelerator(project_dir=UpperCamelCase_     ,  project_config=UpperCamelCase_					)
							_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       List[str]       =		accelerator.prepare(
							    UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
							# Save initial
							accelerator.save_state()
							_lowercase					:       str       =		scheduler.state_dict()
							train(3     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
							self.assertNotEqual(UpperCamelCase_     ,  scheduler.state_dict()					)

							# Load everything back in and make sure all states work
							accelerator.load_state(os.path.join(UpperCamelCase_     ,  'checkpoints'     ,  'checkpoint_0'					)					)
							self.assertEqual(UpperCamelCase_     ,  scheduler.state_dict()					)




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



						'''simple docstring'''





						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42					)
							_lowercase					:       Optional[Any]       =		DummyModel()
							_lowercase					:       List[str]       =		ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase_     ,  total_limit=2					)
							# Train baseline
							_lowercase					:       List[str]       =		Accelerator(project_dir=UpperCamelCase_     ,  project_config=UpperCamelCase_					)
							_lowercase					:       List[Any]       =		accelerator.prepare(UpperCamelCase_					)
							# Save 3 states:
							for _ in range(11					):
								accelerator.save_state()
							self.assertTrue(not os.path.exists(os.path.join(UpperCamelCase_     ,  'checkpoints'     ,  'checkpoint_0'					)					)					)
							self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_     ,  'checkpoints'     ,  'checkpoint_9'					)					)					)
							self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_     ,  'checkpoints'     ,  'checkpoint_10'					)					)					)




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



						'''simple docstring'''





						_lowercase					:       List[str]       =		['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__					)]
						execute_subprocess_async(UpperCamelCase_     ,  env=os.environ.copy()					)


if __name__ == "__main__":
		_A				:    Union[str, Any]     						='''/tmp/accelerate/state_checkpointing'''
		_A				:    Any     						=DummyModel()
		_A				:    int     						=torch.optim.Adam(params=model.parameters(), lr=1e-3)
		_A				:    str     						=torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
		_A						,			_A				:    int     						=dummy_dataloaders()
		_A				:    Dict     						=ProjectConfiguration(automatic_checkpoint_naming=True)
		# Train baseline
		_A				:    Optional[Any]     						=Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='''no''')
		if accelerator.process_index == 0:
				if os.path.exists(savedir):
						shutil.rmtree(savedir)
				os.makedirs(savedir)
		_A						,			_A						,			_A						,			_A						,			_A				:    Optional[Any]     						=accelerator.prepare(
		    model, optimizer, train_dataloader, valid_dataloader, scheduler
		)
		_A						,			_A				:    Any     						=accelerator.prepare(model, optimizer)
		train(3, model, train_dataloader, optimizer, accelerator, scheduler)
		# Check that the intial optimizer is loaded on the GPU
		for group in optimizer.param_groups:
				_A				:    List[Any]     						=group['''params'''][0].device
				break
		assert param_device.type == accelerator.device.type
		_A				:    Optional[Any]     						=model.cpu()
		accelerator.wait_for_everyone()
		accelerator.save_state()
		accelerator.wait_for_everyone()

		# Check CPU state
		accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''cpu''')
		for group in optimizer.param_groups:
				_A				:    Tuple     						=group['''params'''][0].device
				break
		assert (
		    param_device.type == torch.device('''cpu''').type
		), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

		# Check device state
		model.to(accelerator.device)
		accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''on_device''')
		for group in optimizer.param_groups:
				_A				:    Optional[int]     						=group['''params'''][0].device
				break
		assert (
		    param_device.type == accelerator.device.type
		), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

		# Check error
		with pytest.raises(TypeError, match='''Unsupported optimizer map location passed'''):
				accelerator.load_state(os.path.join(savedir, '''checkpoints''', '''checkpoint_0'''), map_location='''invalid''')
		accelerator.wait_for_everyone()
		if accelerator.process_index == 0:
				shutil.rmtree(savedir)
		accelerator.wait_for_everyone()





'''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)
