

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_			:					Tuple    			=     logging.getLogger(__name__)

def 		UpperCamelCase   (lowercase_:					Optional[Any]=2       , lowercase_:					Optional[int]=3       , lowercase_:					int=16       , lowercase_:					int = 10       , lowercase_:					int = 2   )	-> str:

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

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

def 		UpperCamelCase   (lowercase_:					Any       , lowercase_:					List[Any]       , lowercase_:					Optional[int]       , lowercase_:					Optional[Any]       , lowercase_:					Dict       , lowercase_:					Dict=None   )	-> Dict:
	A__				:       List[str]	  =						[]
	for epoch in range(lowercase_   ):
		# Train quickly
		model.train()
		for batch in dataloader:
			A__  ,  A__				:       Optional[int]	  =						batch
			A__				:       int	  =						model(lowercase_   )
			A__				:       int	  =						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        _a		(nn.Module							):


					'''simple docstring'''






					def __init__(					self     ):
						super().__init__()
						A__				:       Any	  =						nn.Parameter(torch.randn(1     )     )
						A__				:       Tuple	  =						nn.Parameter(torch.randn(1     )     )






					def     __A     (					self       ,			A__     ):
						return x * self.a + self.b



class        _a		(unittest.TestCase							):


					'''simple docstring'''






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

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






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

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

							A__				:       List[str]	  =						train(2       ,			A__       ,			A__       ,			A__       ,			A__     )
							# Save everything
							A__				:       int	  =						os.path.join(A__       ,			"""checkpoint"""     )
							accelerator.save_state(A__     )

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






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

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

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

							A__				:       Tuple	  =						train(2       ,			A__       ,			A__       ,			A__       ,			A__     )
							# Save everything
							accelerator.save_state()

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






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

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






					def     __A     (					self     ):
						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42     )
							A__				:       int	  =						DummyModel()
							A__				:       str	  =						ProjectConfiguration(automatic_checkpoint_naming=A__       ,			total_limit=2     )
							# Train baseline
							A__				:       Dict	  =						Accelerator(project_dir=A__       ,			project_config=A__     )
							A__				:       str	  =						accelerator.prepare(A__     )
							# Save 3 states:
							for _ in range(11     ):
								accelerator.save_state()
							self.assertTrue(not os.path.exists(os.path.join(A__       ,			"""checkpoints"""       ,			"""checkpoint_0"""     )     )     )
							self.assertTrue(os.path.exists(os.path.join(A__       ,			"""checkpoints"""       ,			"""checkpoint_9"""     )     )     )
							self.assertTrue(os.path.exists(os.path.join(A__       ,			"""checkpoints"""       ,			"""checkpoint_10"""     )     )     )






					@require_cuda
					def     __A     (					self     ):
						A__				:       Any	  =						["""torchrun""", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__     )]
						execute_subprocess_async(A__       ,			env=os.environ.copy()     )


if __name__ == "__main__":
	A_			:					Any    			=     '/tmp/accelerate/state_checkpointing'
	A_			:					Tuple    			=     DummyModel()
	A_			:					str    			=     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_			:					Tuple    			=     dummy_dataloaders()
	A_			:					List[str]    			=     ProjectConfiguration(automatic_checkpoint_naming=True)
	# Train baseline
	A_			:					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[int]    			=     accelerator.prepare(
	    model, optimizer, train_dataloader, valid_dataloader, scheduler
	)
	A_			,     A_			:					Dict    			=     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_			:					Union[str, Any]    			=     group['params'][0].device
		break
	assert param_device.type == accelerator.device.type
	A_			:					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_			:					Optional[int]    			=     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_			:					Tuple    			=     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()






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


A_			:					List[Any]    			=     logging.get_logger(__name__)

A_			:					Any    			=     {
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  List[str]    				=			'''gpt_neo'''
					UpperCAmelCase__:  str    				=			['''past_key_values''']
					UpperCAmelCase__:  Dict    				=			{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}






					def __init__(					self       ,			A__=5_0257       ,			A__=2048       ,			A__=2048       ,			A__=24       ,			A__=[[["global", "local"], 12]]       ,			A__=16       ,			A__=None       ,			A__=256       ,			A__="gelu_new"       ,			A__=0.0       ,			A__=0.0       ,			A__=0.0       ,			A__=0.1       ,			A__=1e-5       ,			A__=0.0_2       ,			A__=True       ,			A__=5_0256       ,			A__=5_0256       ,			**A__       ,			):
						A__				:       Optional[Any]	  =						vocab_size
						A__				:       Optional[int]	  =						max_position_embeddings
						A__				:       Optional[int]	  =						hidden_size
						A__				:       Dict	  =						num_layers
						A__				:       List[str]	  =						num_heads
						A__				:       Optional[int]	  =						intermediate_size
						A__				:       Union[str, Any]	  =						window_size
						A__				:       Optional[int]	  =						activation_function
						A__				:       Optional[Any]	  =						resid_dropout
						A__				:       Optional[int]	  =						embed_dropout
						A__				:       Any	  =						attention_dropout
						A__				:       Optional[Any]	  =						classifier_dropout
						A__				:       int	  =						layer_norm_epsilon
						A__				:       Tuple	  =						initializer_range
						A__				:       str	  =						use_cache

						A__				:       List[str]	  =						bos_token_id
						A__				:       List[Any]	  =						eos_token_id

						A__				:       Any	  =						attention_types
						A__				:       List[str]	  =						self.expand_attention_types_params(A__     )

						if len(self.attention_layers     ) != self.num_layers:
							raise ValueError(
							    """Configuration for convolutional module is incorrect. """
							    """It is required that `len(config.attention_layers)` == `config.num_layers` """
							    F"""but is `len(config.attention_layers) = {len(self.attention_layers     )}`, """
							    F"""`config.num_layers = {self.num_layers}`. """
							    """`config.attention_layers` is prepared using `config.attention_types`. """
							    """Please verify the value of `config.attention_types` argument."""     )

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






					@staticmethod
					def     __A     (					A__     ):
						A__				:       int	  =						[]
						for item in attention_types:
							for _ in range(item[1]     ):
								attentions.extend(item[0]     )
						return attentions



def 		UpperCamelCase   (lowercase_:					Dict       , lowercase_:					Any       , lowercase_:					Union[str, Any]       , lowercase_:					Optional[Any]   )	-> Dict:
	import torch

	A__				:       List[Any]	  =						input.size()
	A__				:       List[str]	  =						len(lowercase_   )
	A__				:       Union[str, Any]	  =						shape[dimension]

	A__				:       Optional[int]	  =						torch.arange(0       , lowercase_       , lowercase_   )
	A__				:       str	  =						torch.div(sizedim - size       , lowercase_       , rounding_mode="""floor"""   ) + 1
	A__				:       Any	  =						torch.arange(lowercase_   ) + low_indices[:min_length][:, None]

	A__				:       Union[str, Any]	  =						[slice(lowercase_   )] * rank
	A__				:       Any	  =						indices
	A__				:       List[str]	  =						input[s]

	A__				:       Dict	  =						list(range(0       , rank + 1   )   )
	perm.append(perm.pop(dimension + 1   )   )

	return sliced.permute(lowercase_   )

def 		UpperCamelCase   (lowercase_:					str       , lowercase_:					int   )	-> int:
	import torch

	A__				:       Union[str, Any]	  =						torch.arange(1       , lowercase_   )
	A__				:       str	  =						torch.remainder(lowercase_       , lowercase_   )
	A__				:       Optional[Any]	  =						remainders == 0
	A__				:       Any	  =						candidates[divisor_indices]
	A__				:       Optional[Any]	  =						torch.max(lowercase_   )
	return largest_divisor, torch.div(lowercase_       , lowercase_       , rounding_mode="""floor"""   )



class        _a		(__magic_name__							):


					'''simple docstring'''






					@property
					def     __A     (					self     ):
						A__				:       Any	  =						OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}     )
						if self.use_past:
							self.fill_with_past_key_values_(A__       ,			direction="""inputs"""     )
							A__				:       Optional[Any]	  =						{0: """batch""", 1: """past_sequence + sequence"""}
						else:
							A__				:       List[str]	  =						{0: """batch""", 1: """sequence"""}

						return common_inputs






					@property
					def     __A     (					self     ):
						return self._config.num_heads






					def     __A     (					self       ,			A__       ,			A__ = -1       ,			A__ = -1       ,			A__ = False       ,			A__ = None       ,			):
						A__				:       int	  =						super(A__       ,			self     ).generate_dummy_inputs(
						    A__       ,			batch_size=A__       ,			seq_length=A__       ,			is_pair=A__       ,			framework=A__     )

						# We need to order the input in the way they appears in the forward()
						A__				:       Optional[int]	  =						OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}     )

						# Need to add the past_keys
						if self.use_past:
							if not is_torch_available():
								raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""     )
							else:
								import torch

								A__  ,  A__				:       Dict	  =						common_inputs["""input_ids"""].shape
								# Not using the same length for past_key_values
								A__				:       int	  =						seqlen + 2
								A__				:       int	  =						(
								    batch,
								    self.num_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // self.num_attention_heads,
								)
								A__				:       Dict	  =						[
								    (torch.zeros(A__     ), torch.zeros(A__     )) for _ in range(self.num_layers     )
								]

						A__				:       List[Any]	  =						common_inputs["""attention_mask"""]
						if self.use_past:
							A__				:       int	  =						ordered_inputs["""attention_mask"""].dtype
							A__				:       str	  =						torch.cat(
							    [ordered_inputs["""attention_mask"""], torch.ones(A__       ,			A__       ,			dtype=A__     )]       ,			dim=1     )

						return ordered_inputs






					@property
					def     __A     (					self     ):
						return 13



