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


_UpperCAmelCase            =		logging.getLogger(__name__)




def     UpperCamelCase						(	__lowercase						:			Any=2						,__lowercase						:			Union[str, Any]=3						,__lowercase						:			Optional[Any]=16						,__lowercase						:			int = 10						,__lowercase						:			int = 2     ):
		'''simple docstring'''




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

		A_		:    Optional[int]         =				get_dataset(__lowercase     )
		A_		:    List[Any]         =				get_dataset(__lowercase     )
		A_		:    str         =				DataLoader(__lowercase						,shuffle=__lowercase						,batch_size=__lowercase						,num_workers=4     )
		A_		:    Union[str, Any]         =				DataLoader(__lowercase						,shuffle=__lowercase						,batch_size=__lowercase						,num_workers=4     )
		return (train_dataloader, valid_dataloader)




def     UpperCamelCase						(	__lowercase						:			Tuple						,__lowercase						:			Tuple						,__lowercase						:			Union[str, Any]						,__lowercase						:			Union[str, Any]						,__lowercase						:			Tuple						,__lowercase						:			Any=None     ):
		'''simple docstring'''




		A_		:    Optional[int]         =				[]
		for epoch in range(__lowercase     ):
				# Train quickly
				model.train()
				for batch in dataloader:
						A_       ,      A_		:    Union[str, Any]         =				batch
						A_		:    Union[str, Any]         =				model(__lowercase     )
						A_		:    str         =				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 UpperCAmelCase						( nn.Module  ):


				'''simple docstring'''






				def __init__(						self ):



						"""simple docstring"""





						super().__init__()
						A_		:    str         =				nn.Parameter(torch.randn(1 ) )
						A_		:    int         =				nn.Parameter(torch.randn(1 ) )







				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						return x * self.a + self.b





class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						with tempfile.TemporaryDirectory() as tmpdir:
								set_seed(4_2 )
								A_		:    int         =				DummyModel()
								A_		:    Tuple         =				torch.optim.Adam(params=model.parameters()		,     lr=1E-3 )
								A_       ,      A_		:    List[Any]         =				dummy_dataloaders()
								A_		:    Optional[int]         =				ProjectConfiguration(total_limit=1		,     project_dir=lowercase		,     automatic_checkpoint_naming=lowercase )
								# Train baseline
								A_		:    str         =				Accelerator(project_config=lowercase )
								A_       ,      A_       ,      A_       ,      A_		:    Tuple         =				accelerator.prepare(
								    lowercase		,     lowercase		,     lowercase		,     lowercase )
								# Save initial
								accelerator.save_state()

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



						"""simple docstring"""





						with tempfile.TemporaryDirectory() as tmpdir:
								set_seed(4_2 )
								A_		:    Optional[int]         =				DummyModel()
								A_		:    Dict         =				torch.optim.Adam(params=model.parameters()		,     lr=1E-3 )
								A_       ,      A_		:    Tuple         =				dummy_dataloaders()
								# Train baseline
								A_		:    Any         =				Accelerator()
								A_       ,      A_       ,      A_       ,      A_		:    int         =				accelerator.prepare(
								    lowercase		,     lowercase		,     lowercase		,     lowercase )
								# Save initial
								A_		:    Any         =				os.path.join(lowercase		,     'initial' )
								accelerator.save_state(lowercase )
								((A_)       ,      (A_))		:    int         =				model.a.item(), model.b.item()
								A_		:    List[Any]         =				optimizer.state_dict()
								A_		:    Tuple         =				train(3		,     lowercase		,     lowercase		,     lowercase		,     lowercase )
								((A_)       ,      (A_))		:    Dict         =				model.a.item(), model.b.item()
								A_		:    Any         =				optimizer.state_dict()

								# Train partially
								set_seed(4_2 )
								A_		:    str         =				DummyModel()
								A_		:    str         =				torch.optim.Adam(params=model.parameters()		,     lr=1E-3 )
								A_       ,      A_		:    Dict         =				dummy_dataloaders()
								A_		:    str         =				Accelerator()
								A_       ,      A_       ,      A_       ,      A_		:    Tuple         =				accelerator.prepare(
								    lowercase		,     lowercase		,     lowercase		,     lowercase )
								accelerator.load_state(lowercase )
								((A_)       ,      (A_))		:    Optional[Any]         =				model.a.item(), model.b.item()
								A_		:    Tuple         =				optimizer.state_dict()
								self.assertEqual(lowercase		,     lowercase )
								self.assertEqual(lowercase		,     lowercase )
								self.assertEqual(lowercase		,     lowercase )

								A_		:    Optional[Any]         =				train(2		,     lowercase		,     lowercase		,     lowercase		,     lowercase )
								# Save everything
								A_		:    Optional[Any]         =				os.path.join(lowercase		,     'checkpoint' )
								accelerator.save_state(lowercase )

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



						"""simple docstring"""





						with tempfile.TemporaryDirectory() as tmpdir:
								set_seed(4_2 )
								A_		:    int         =				DummyModel()
								A_		:    int         =				torch.optim.Adam(params=model.parameters()		,     lr=1E-3 )
								A_       ,      A_		:    Any         =				dummy_dataloaders()
								A_		:    Any         =				ProjectConfiguration(automatic_checkpoint_naming=lowercase )

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

								# Train partially
								set_seed(4_2 )
								A_		:    Optional[Any]         =				DummyModel()
								A_		:    int         =				torch.optim.Adam(params=model.parameters()		,     lr=1E-3 )
								A_       ,      A_		:    Optional[int]         =				dummy_dataloaders()
								A_		:    Dict         =				ProjectConfiguration(iteration=1		,     automatic_checkpoint_naming=lowercase )
								A_		:    Union[str, Any]         =				Accelerator(project_dir=lowercase		,     project_config=lowercase )
								A_       ,      A_       ,      A_       ,      A_		:    int         =				accelerator.prepare(
								    lowercase		,     lowercase		,     lowercase		,     lowercase )
								accelerator.load_state(os.path.join(lowercase		,     'checkpoints'		,     'checkpoint_0' ) )
								((A_)       ,      (A_))		:    Any         =				model.a.item(), model.b.item()
								A_		:    List[str]         =				optimizer.state_dict()
								self.assertEqual(lowercase		,     lowercase )
								self.assertEqual(lowercase		,     lowercase )
								self.assertEqual(lowercase		,     lowercase )

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

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



						"""simple docstring"""





						A_		:    Optional[Any]         =				torch.tensor([1, 2, 3] )
						A_		:    Optional[Any]         =				torch.tensor([2, 3, 4] )
						A_		:    Union[str, Any]         =				DummyModel()
						A_		:    Union[str, Any]         =				torch.optim.Adam(net.parameters() )
						A_		:    Union[str, Any]         =				Accelerator()
						with self.assertRaises(lowercase ) as ve:
								accelerator.register_for_checkpointing(lowercase		,     lowercase		,     lowercase		,     lowercase )
						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   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						with tempfile.TemporaryDirectory() as tmpdir:
								set_seed(4_2 )
								A_		:    str         =				DummyModel()
								A_		:    Dict         =				torch.optim.Adam(params=model.parameters()		,     lr=1E-3 )
								A_		:    Union[str, Any]         =				torch.optim.lr_scheduler.StepLR(lowercase		,     step_size=1		,     gamma=0.99 )
								A_       ,      A_		:    str         =				dummy_dataloaders()
								A_		:    List[str]         =				ProjectConfiguration(automatic_checkpoint_naming=lowercase )
								# Train baseline
								A_		:    Dict         =				Accelerator(project_dir=lowercase		,     project_config=lowercase )
								A_       ,      A_       ,      A_       ,      A_       ,      A_		:    Optional[Any]         =				accelerator.prepare(
								    lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase )
								# Save initial
								accelerator.save_state()
								A_		:    Union[str, Any]         =				scheduler.state_dict()
								train(3		,     lowercase		,     lowercase		,     lowercase		,     lowercase		,     lowercase )
								self.assertNotEqual(lowercase		,     scheduler.state_dict() )

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



						"""simple docstring"""





						with tempfile.TemporaryDirectory() as tmpdir:
								set_seed(4_2 )
								A_		:    Union[str, Any]         =				DummyModel()
								A_		:    List[str]         =				ProjectConfiguration(automatic_checkpoint_naming=lowercase		,     total_limit=2 )
								# Train baseline
								A_		:    int         =				Accelerator(project_dir=lowercase		,     project_config=lowercase )
								A_		:    Any         =				accelerator.prepare(lowercase )
								# Save 3 states:
								for _ in range(1_1 ):
										accelerator.save_state()
								self.assertTrue(not os.path.exists(os.path.join(lowercase		,     'checkpoints'		,     'checkpoint_0' ) ) )
								self.assertTrue(os.path.exists(os.path.join(lowercase		,     'checkpoints'		,     'checkpoint_9' ) ) )
								self.assertTrue(os.path.exists(os.path.join(lowercase		,     'checkpoints'		,     'checkpoint_10' ) ) )







				@require_cuda
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    int         =				['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
						execute_subprocess_async(lowercase		,     env=os.environ.copy() )


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







import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
	import onnxruntime as ort


_UpperCAmelCase            =		logging.get_logger(__name__)

_UpperCAmelCase            =		{
    """tensor(bool)""": np.bool_,
    """tensor(int8)""": np.inta,
    """tensor(uint8)""": np.uinta,
    """tensor(int16)""": np.intaa,
    """tensor(uint16)""": np.uintaa,
    """tensor(int32)""": np.intaa,
    """tensor(uint32)""": np.uintaa,
    """tensor(int64)""": np.intaa,
    """tensor(uint64)""": np.uintaa,
    """tensor(float16)""": np.floataa,
    """tensor(float)""": np.floataa,
    """tensor(double)""": np.floataa,
}





class UpperCAmelCase						:


				'''simple docstring'''






				def __init__(						self		,     lowercase=None		,     **lowercase ):



						"""simple docstring"""





						logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.' )
						A_		:    List[Any]         =				model
						A_		:    Dict         =				kwargs.get('model_save_dir'		,     lowercase )
						A_		:    List[str]         =				kwargs.get('latest_model_name'		,     lowercase )
				def __call__(						self		,     **lowercase ):



						"""simple docstring"""





						A_		:    str         =				{k: np.array(lowercase ) for k, v in kwargs.items()}
						return self.model.run(lowercase		,     lowercase )
				@staticmethod
				def   lowerCAmelCase_       (						lowercase		,     lowercase=None		,     lowercase=None ):



						"""simple docstring"""





						if provider is None:
								logger.info('No onnxruntime provider specified, using CPUExecutionProvider' )
								A_		:    List[Any]         =				'CPUExecutionProvider'

						return ort.InferenceSession(lowercase		,     providers=[provider]		,     sess_options=lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None		,     **lowercase ):



						"""simple docstring"""





						A_		:    str         =				file_name if file_name is not None else ONNX_WEIGHTS_NAME

						A_		:    Optional[int]         =				self.model_save_dir.joinpath(self.latest_model_name )
						A_		:    int         =				Path(lowercase ).joinpath(lowercase )
						try:
								shutil.copyfile(lowercase		,     lowercase )
						except shutil.SameFileError:
								pass

						# copy external weights (for models >2GB)
						A_		:    Optional[Any]         =				self.model_save_dir.joinpath(lowercase )
						if src_path.exists():
								A_		:    int         =				Path(lowercase ).joinpath(lowercase )
								try:
										shutil.copyfile(lowercase		,     lowercase )
								except shutil.SameFileError:
										pass
				def   lowerCAmelCase_       (						self		,     lowercase		,     **lowercase		,     ):



						"""simple docstring"""





						if os.path.isfile(lowercase ):
								logger.error(F'''Provided path ({save_directory}) should be a directory, not a file''' )
								return

						os.makedirs(lowercase		,     exist_ok=lowercase )

						# saving model weights/files
						self._save_pretrained(lowercase		,     **lowercase )
				@classmethod
				def   lowerCAmelCase_       (						cls		,     lowercase		,     lowercase = None		,     lowercase = None		,     lowercase = False		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Any         =				file_name if file_name is not None else ONNX_WEIGHTS_NAME
						# load model from local directory
						if os.path.isdir(lowercase ):
								A_		:    Optional[int]         =				OnnxRuntimeModel.load_model(
								    os.path.join(lowercase		,     lowercase )		,     provider=lowercase		,     sess_options=lowercase )
								A_		:    Dict         =				Path(lowercase )
						# load model from hub
						else:
								# download model
								A_		:    List[str]         =				hf_hub_download(
								    repo_id=lowercase		,     filename=lowercase		,     use_auth_token=lowercase		,     revision=lowercase		,     cache_dir=lowercase		,     force_download=lowercase		,     )
								A_		:    int         =				Path(lowercase ).parent
								A_		:    Optional[Any]         =				Path(lowercase ).name
								A_		:    Any         =				OnnxRuntimeModel.load_model(lowercase		,     provider=lowercase		,     sess_options=lowercase )
						return cls(model=lowercase		,     **lowercase )







				@classmethod
				def   lowerCAmelCase_       (						cls		,     lowercase		,     lowercase = True		,     lowercase = None		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    List[Any]         =				None
						if len(str(lowercase ).split('@' ) ) == 2:
								A_       ,      A_		:    int         =				model_id.split('@' )

						return cls._from_pretrained(
						    model_id=lowercase		,     revision=lowercase		,     cache_dir=lowercase		,     force_download=lowercase		,     use_auth_token=lowercase		,     **lowercase		,     )






