






"""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


lowerCamelCase_					=      logging.getLogger(__name__)

def 				snake_case      (   A__=2	,A__=3	,A__=16	,A__ = 10	,A__ = 2						):

			def get_dataset(A__						):
						UpperCAmelCase_     : Optional[Any]			     =					torch.randn(batch_size * n_batches	,1						)
						return TensorDataset(A__	,a * x + b + 0.1 * torch.randn(batch_size * n_batches	,1						)						)

			UpperCAmelCase_     : Tuple			     =					get_dataset(A__						)
			UpperCAmelCase_     : str			     =					get_dataset(A__						)
			UpperCAmelCase_     : List[Any]			     =					DataLoader(A__	,shuffle=A__	,batch_size=A__	,num_workers=4						)
			UpperCAmelCase_     : Dict			     =					DataLoader(A__	,shuffle=A__	,batch_size=A__	,num_workers=4						)
			return (train_dataloader, valid_dataloader)

def 				snake_case      (   A__	,A__	,A__	,A__	,A__	,A__=None						):
			UpperCAmelCase_     : List[Any]			     =					[]
			for epoch in range(A__						):
						# Train quickly
						model.train()
						for batch in dataloader:
									UpperCAmelCase_     ,	UpperCAmelCase_     : List[str]			     =					batch
									UpperCAmelCase_     : int			     =					model(A__						)
									UpperCAmelCase_     : Tuple			     =					torch.nn.functional.mse_loss(A__	,A__						)
									accelerator.backward(A__						)
									optimizer.step()
									optimizer.zero_grad()
						rands.append(random.random()						)  # Introduce some randomness
						if scheduler is not None:
									scheduler.step()
			return rands







class 			UpperCamelCase_       (nn.Module     ):





	def __init__(      self					: int						)						->			int:
				super().__init__()
				UpperCAmelCase_     : Union[str, Any]			     =					nn.Parameter(torch.randn(1						)						)
				UpperCAmelCase_     : List[str]			     =					nn.Parameter(torch.randn(1						)						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: int						)						->			str:
				return x * self.a + self.b







class 			UpperCamelCase_       (unittest.TestCase     ):





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			List[Any]:
				with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42						)
							UpperCAmelCase_     : Dict			     =					DummyModel()
							UpperCAmelCase_     : List[str]			     =					torch.optim.Adam(params=model.parameters()	,	lr=1e-3						)
							UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[int]			     =					dummy_dataloaders()
							UpperCAmelCase_     : Any			     =					ProjectConfiguration(total_limit=1	,	project_dir=lowerCAmelCase_	,	automatic_checkpoint_naming=lowerCAmelCase_						)
							# Train baseline
							UpperCAmelCase_     : Any			     =					Accelerator(project_config=lowerCAmelCase_						)
							UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : List[str]			     =					accelerator.prepare(
							    lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_						)
							# Save initial
							accelerator.save_state()

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





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			str:
				with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42						)
							UpperCAmelCase_     : Dict			     =					DummyModel()
							UpperCAmelCase_     : str			     =					torch.optim.Adam(params=model.parameters()	,	lr=1e-3						)
							UpperCAmelCase_     ,	UpperCAmelCase_     : List[str]			     =					dummy_dataloaders()
							# Train baseline
							UpperCAmelCase_     : Tuple			     =					Accelerator()
							UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[int]			     =					accelerator.prepare(
							    lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_						)
							# Save initial
							UpperCAmelCase_     : str			     =					os.path.join(lowerCAmelCase_	,	"initial"						)
							accelerator.save_state(lowerCAmelCase_						)
							((UpperCAmelCase_)     ,	(UpperCAmelCase_))     : List[str]			     =					model.a.item(), model.b.item()
							UpperCAmelCase_     : Optional[int]			     =					optimizer.state_dict()
							UpperCAmelCase_     : str			     =					train(3	,	lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_						)
							((UpperCAmelCase_)     ,	(UpperCAmelCase_))     : Optional[int]			     =					model.a.item(), model.b.item()
							UpperCAmelCase_     : Optional[Any]			     =					optimizer.state_dict()

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

							UpperCAmelCase_     : Tuple			     =					train(2	,	lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_						)
							# Save everything
							UpperCAmelCase_     : int			     =					os.path.join(lowerCAmelCase_	,	"checkpoint"						)
							accelerator.save_state(lowerCAmelCase_						)

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





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]						)						->			List[str]:
				with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42						)
							UpperCAmelCase_     : Any			     =					DummyModel()
							UpperCAmelCase_     : List[Any]			     =					torch.optim.Adam(params=model.parameters()	,	lr=1e-3						)
							UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[int]			     =					dummy_dataloaders()
							UpperCAmelCase_     : int			     =					ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase_						)

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

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

							UpperCAmelCase_     : Optional[int]			     =					train(2	,	lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_						)
							# Save everything
							accelerator.save_state()

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





	def      _SCREAMING_SNAKE_CASE      (      self					: str						)						->			Dict:
				UpperCAmelCase_     : List[Any]			     =					torch.tensor([1, 2, 3]						)
				UpperCAmelCase_     : Tuple			     =					torch.tensor([2, 3, 4]						)
				UpperCAmelCase_     : List[Any]			     =					DummyModel()
				UpperCAmelCase_     : Any			     =					torch.optim.Adam(net.parameters()						)
				UpperCAmelCase_     : Tuple			     =					Accelerator()
				with self.assertRaises(lowerCAmelCase_						) as ve:
							accelerator.register_for_checkpointing(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_						)
				UpperCAmelCase_     : Optional[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      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			Dict:
				with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42						)
							UpperCAmelCase_     : List[Any]			     =					DummyModel()
							UpperCAmelCase_     : Optional[int]			     =					torch.optim.Adam(params=model.parameters()	,	lr=1e-3						)
							UpperCAmelCase_     : Union[str, Any]			     =					torch.optim.lr_scheduler.StepLR(lowerCAmelCase_	,	step_size=1	,	gamma=0.9_9						)
							UpperCAmelCase_     ,	UpperCAmelCase_     : str			     =					dummy_dataloaders()
							UpperCAmelCase_     : List[str]			     =					ProjectConfiguration(automatic_checkpoint_naming=lowerCAmelCase_						)
							# Train baseline
							UpperCAmelCase_     : str			     =					Accelerator(project_dir=lowerCAmelCase_	,	project_config=lowerCAmelCase_						)
							UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : List[str]			     =					accelerator.prepare(
							    lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_						)
							# Save initial
							accelerator.save_state()
							UpperCAmelCase_     : Dict			     =					scheduler.state_dict()
							train(3	,	lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_						)
							self.assertNotEqual(lowerCAmelCase_	,	scheduler.state_dict()						)

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





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





	@require_cuda
	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Union[str, Any]:
				UpperCAmelCase_     : Union[str, Any]			     =					["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__						)]
				execute_subprocess_async(lowerCAmelCase_	,	env=os.environ.copy()						)


if __name__ == "__main__":
			lowerCamelCase_					=      '''/tmp/accelerate/state_checkpointing'''
			lowerCamelCase_					=      DummyModel()
			lowerCamelCase_					=      torch.optim.Adam(params=model.parameters(), lr=1E-3)
			lowerCamelCase_					=      torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
			lowerCamelCase_	,		lowerCamelCase_					=      dummy_dataloaders()
			lowerCamelCase_					=      ProjectConfiguration(automatic_checkpoint_naming=True)
			# Train baseline
			lowerCamelCase_					=      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)
			lowerCamelCase_	,		lowerCamelCase_	,		lowerCamelCase_	,		lowerCamelCase_	,		lowerCamelCase_					=      accelerator.prepare(
			    model, optimizer, train_dataloader, valid_dataloader, scheduler
			)
			lowerCamelCase_	,		lowerCamelCase_					=      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:
						lowerCamelCase_					=      group['''params'''][0].device
						break
			assert param_device.type == accelerator.device.type
			lowerCamelCase_					=      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:
						lowerCamelCase_					=      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:
						lowerCamelCase_					=      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"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				'''rwkv'''
	__magic_name__	      =				{'''max_position_embeddings''': '''context_length'''}





	def __init__(      self					: str	,	lowerCAmelCase_					: str=50_277	,	lowerCAmelCase_					: Optional[int]=1_024	,	lowerCAmelCase_					: Optional[int]=4_096	,	lowerCAmelCase_					: Union[str, Any]=32	,	lowerCAmelCase_					: List[Any]=None	,	lowerCAmelCase_					: int=None	,	lowerCAmelCase_					: List[Any]=1e-5	,	lowerCAmelCase_					: str=0	,	lowerCAmelCase_					: Tuple=0	,	lowerCAmelCase_					: int=6	,	lowerCAmelCase_					: Optional[Any]=False	,	lowerCAmelCase_					: Any=True	,	**lowerCAmelCase_					: List[Any]	,	)						->			List[str]:
				UpperCAmelCase_     : Tuple			     =					vocab_size
				UpperCAmelCase_     : List[str]			     =					context_length
				UpperCAmelCase_     : Dict			     =					hidden_size
				UpperCAmelCase_     : Optional[int]			     =					num_hidden_layers
				UpperCAmelCase_     : Optional[int]			     =					attention_hidden_size if attention_hidden_size is not None else hidden_size
				UpperCAmelCase_     : Dict			     =					intermediate_size if intermediate_size is not None else 4 * hidden_size
				UpperCAmelCase_     : Any			     =					layer_norm_epsilon
				UpperCAmelCase_     : List[Any]			     =					rescale_every
				UpperCAmelCase_     : List[str]			     =					use_cache

				UpperCAmelCase_     : List[str]			     =					bos_token_id
				UpperCAmelCase_     : Union[str, Any]			     =					eos_token_id

				super().__init__(
				    tie_word_embeddings=lowerCAmelCase_	,	bos_token_id=lowerCAmelCase_	,	eos_token_id=lowerCAmelCase_	,	**lowerCAmelCase_						)



