




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	:						Union[str, Any]			=     logging.getLogger(__name__)



def       UpperCAmelCase__  (     lowerCamelCase=2,			lowerCamelCase=3,			lowerCamelCase=16,			lowerCamelCase = 10,			lowerCamelCase = 2   ):

  def get_dataset(lowerCamelCase   ):
    lowercase    :Tuple      	=						torch.randn(batch_size * n_batches,			1   )
    return TensorDataset(_lowerCamelCase,			a * x + b + 0.1 * torch.randn(batch_size * n_batches,			1   )   )

  lowercase    :List[str]      	=						get_dataset(_lowerCamelCase   )
  lowercase    :int      	=						get_dataset(_lowerCamelCase   )
  lowercase    :Tuple      	=						DataLoader(_lowerCamelCase,			shuffle=_lowerCamelCase,			batch_size=_lowerCamelCase,			num_workers=4   )
  lowercase    :str      	=						DataLoader(_lowerCamelCase,			shuffle=_lowerCamelCase,			batch_size=_lowerCamelCase,			num_workers=4   )
  return (train_dataloader, valid_dataloader)



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase=None   ):
  lowercase    :Dict      	=						[]
  for epoch in range(_lowerCamelCase   ):
    # Train quickly
    model.train()
    for batch in dataloader:
      lowercase    :Tuple      	=						batch
      lowercase    :Union[str, Any]      	=						model(_lowerCamelCase   )
      lowercase    :int      	=						torch.nn.functional.mse_loss(_lowerCamelCase,			_lowerCamelCase   )
      accelerator.backward(_lowerCamelCase   )
      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):






       def __init__(   self:					str				):
         super().__init__()
         lowercase    :int      	=						nn.Parameter(torch.randn(1				)				)
         lowercase    :Any      	=						nn.Parameter(torch.randn(1				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int       ,					_lowerCAmelCase:					Union[str, Any]				):
         return x * self.a + self.b







class    __lowerCAmelCase       (							unittest.TestCase):






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):

         with tempfile.TemporaryDirectory() as tmpdir:
           set_seed(42				)
           lowercase    :Optional[int]      	=						DummyModel()
           lowercase    :List[str]      	=						torch.optim.Adam(params=model.parameters()       ,					lr=1e-3				)
           lowercase    :Optional[int]      	=						dummy_dataloaders()
           lowercase    :List[str]      	=						ProjectConfiguration(total_limit=1       ,					project_dir=_lowercase       ,					automatic_checkpoint_naming=_lowercase				)
           # Train baseline
           lowercase    :str      	=						Accelerator(project_config=_lowercase				)
           lowercase    :Optional[int]      	=						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 					SCREAMING_SNAKE_CASE    (   self:					Dict				):

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

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

           lowercase    :Optional[Any]      	=						train(2       ,					_lowercase       ,					_lowercase       ,					_lowercase       ,					_lowercase				)
           # Save everything
           lowercase    :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				)
           (lowercase)    :str      	=						model.a.item(), model.b.item()
           lowercase    :int      	=						optimizer.state_dict()
           self.assertEqual(_lowercase       ,					_lowercase				)
           self.assertEqual(_lowercase       ,					_lowercase				)
           self.assertEqual(_lowercase       ,					_lowercase				)
           self.assertEqual(_lowercase       ,					_lowercase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):

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

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

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

           lowercase    :Dict      	=						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				)
           (lowercase)    :int      	=						model.a.item(), model.b.item()
           lowercase    :str      	=						optimizer.state_dict()
           self.assertEqual(_lowercase       ,					_lowercase				)
           self.assertEqual(_lowercase       ,					_lowercase				)
           self.assertEqual(_lowercase       ,					_lowercase				)
           self.assertEqual(_lowercase       ,					_lowercase				)






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

         with tempfile.TemporaryDirectory() as tmpdir:
           set_seed(42				)
           lowercase    :int      	=						DummyModel()
           lowercase    :int      	=						torch.optim.Adam(params=model.parameters()       ,					lr=1e-3				)
           lowercase    :Tuple      	=						torch.optim.lr_scheduler.StepLR(_lowercase       ,					step_size=1       ,					gamma=0.99				)
           lowercase    :Optional[Any]      	=						dummy_dataloaders()
           lowercase    :Optional[int]      	=						ProjectConfiguration(automatic_checkpoint_naming=_lowercase				)
           # Train baseline
           lowercase    :List[Any]      	=						Accelerator(project_dir=_lowercase       ,					project_config=_lowercase				)
           lowercase    :Union[str, Any]      	=						accelerator.prepare(
               _lowercase       ,					_lowercase       ,					_lowercase       ,					_lowercase       ,					_lowercase				)
           # Save initial
           accelerator.save_state()
           lowercase    :Optional[int]      	=						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 					SCREAMING_SNAKE_CASE    (   self:					int				):

         with tempfile.TemporaryDirectory() as tmpdir:
           set_seed(42				)
           lowercase    :Dict      	=						DummyModel()
           lowercase    :Dict      	=						ProjectConfiguration(automatic_checkpoint_naming=_lowercase       ,					total_limit=2				)
           # Train baseline
           lowercase    :Tuple      	=						Accelerator(project_dir=_lowercase       ,					project_config=_lowercase				)
           lowercase    :str      	=						accelerator.prepare(_lowercase				)
           # Save 3 states:
           for _ in range(11				):
             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 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         lowercase    :List[Any]      	=						["""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	:						Optional[Any]			=     "/tmp/accelerate/state_checkpointing"
     _UpperCAmelCase	:						Dict			=     DummyModel()
     _UpperCAmelCase	:						Any			=     torch.optim.Adam(params=model.parameters(), lr=1E-3)
     _UpperCAmelCase	:						str			=     torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
     _UpperCAmelCase			,			_UpperCAmelCase	:						Optional[Any]			=     dummy_dataloaders()
     _UpperCAmelCase	:						Dict			=     ProjectConfiguration(automatic_checkpoint_naming=True)
     # Train baseline
     _UpperCAmelCase	:						Tuple			=     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	:						Dict			=     accelerator.prepare(
         model, optimizer, train_dataloader, valid_dataloader, scheduler
     )
     _UpperCAmelCase			,			_UpperCAmelCase	:						Optional[int]			=     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	:						Optional[int]			=     group["params"][0].device
          break
     assert param_device.type == accelerator.device.type
     _UpperCAmelCase	:						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:
          _UpperCAmelCase	:						List[str]			=     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	:						Optional[Any]			=     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	:						Dict			=     logging.get_logger(__name__)

_UpperCAmelCase	:						List[Any]			=     {
    "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    __lowerCAmelCase       :






       def __init__(   self:					Dict       ,					_lowerCAmelCase:					Union[str, Any]=None       ,					**_lowerCAmelCase:					Any				):
         logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."				)
         lowercase    :str      	=						model
         lowercase    :Any      	=						kwargs.get("model_save_dir"       ,					_lowerCAmelCase				)
         lowercase    :Dict      	=						kwargs.get("latest_model_name"       ,					_lowerCAmelCase				)






       def __call__(   self:					Optional[Any]       ,					**_lowerCAmelCase:					List[Any]				):
         lowercase    :List[str]      	=						{k: np.array(_lowerCAmelCase				) for k, v in kwargs.items()}
         return self.model.run(_lowerCAmelCase       ,					_lowerCAmelCase				)






       @staticmethod
       def 					SCREAMING_SNAKE_CASE    (   _lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					List[Any]=None       ,					_lowerCAmelCase:					List[Any]=None				):
         if provider is None:
           logger.info("No onnxruntime provider specified, using CPUExecutionProvider"				)
           lowercase    :int      	=						"CPUExecutionProvider"

         return ort.InferenceSession(_lowerCAmelCase       ,					providers=[provider]       ,					sess_options=_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					Optional[str] = None       ,					**_lowerCAmelCase:					Any				):
         lowercase    :Optional[int]      	=						file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

         # copy external weights (for models >2GB)
         lowercase    :Tuple      	=						self.model_save_dir.joinpath(_lowerCAmelCase				)
         if src_path.exists():
           lowercase    :Dict      	=						Path(_lowerCAmelCase				).joinpath(_lowerCAmelCase				)
           try:
             shutil.copyfile(_lowerCAmelCase       ,					_lowerCAmelCase				)
           except shutil.SameFileError:
             pass






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]       ,					_lowerCAmelCase:					Union[str, os.PathLike]       ,					**_lowerCAmelCase:					int       ,					):
         if os.path.isfile(_lowerCAmelCase				):
           logger.error(F"Provided path ({save_directory}) should be a directory, not a file"				)
           return

         os.makedirs(_lowerCAmelCase       ,					exist_ok=_lowerCAmelCase				)

         # saving model weights/files
         self._save_pretrained(_lowerCAmelCase       ,					**_lowerCAmelCase				)






       @classmethod
       def 					SCREAMING_SNAKE_CASE    (   cls:					Dict       ,					_lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					Optional[Union[bool, str, None]] = None       ,					_lowerCAmelCase:					Optional[Union[str, None]] = None       ,					_lowerCAmelCase:					bool = False       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional["ort.SessionOptions"] = None       ,					**_lowerCAmelCase:					Optional[int]       ,					):
         lowercase    :List[str]      	=						file_name if file_name is not None else ONNX_WEIGHTS_NAME
         # load model from local directory
         if os.path.isdir(_lowerCAmelCase				):
           lowercase    :Union[str, Any]      	=						OnnxRuntimeModel.load_model(
               os.path.join(_lowerCAmelCase       ,					_lowerCAmelCase				)       ,					provider=_lowerCAmelCase       ,					sess_options=_lowerCAmelCase				)
           lowercase    :Optional[Any]      	=						Path(_lowerCAmelCase				)
         # load model from hub
         else:
           # download model
           lowercase    :str      	=						hf_hub_download(
               repo_id=_lowerCAmelCase       ,					filename=_lowerCAmelCase       ,					use_auth_token=_lowerCAmelCase       ,					revision=_lowerCAmelCase       ,					cache_dir=_lowerCAmelCase       ,					force_download=_lowerCAmelCase       ,					)
           lowercase    :Optional[int]      	=						Path(_lowerCAmelCase				).parent
           lowercase    :Tuple      	=						Path(_lowerCAmelCase				).name
           lowercase    :Tuple      	=						OnnxRuntimeModel.load_model(_lowerCAmelCase       ,					provider=_lowerCAmelCase       ,					sess_options=_lowerCAmelCase				)
         return cls(model=_lowerCAmelCase       ,					**_lowerCAmelCase				)






       @classmethod
       def 					SCREAMING_SNAKE_CASE    (   cls:					str       ,					_lowerCAmelCase:					Union[str, Path]       ,					_lowerCAmelCase:					bool = True       ,					_lowerCAmelCase:					Optional[str] = None       ,					_lowerCAmelCase:					Optional[str] = None       ,					**_lowerCAmelCase:					Any       ,					):
         lowercase    :List[str]      	=						None
         if len(str(_lowerCAmelCase				).split("@"				)				) == 2:
           lowercase	,    lowercase    :Tuple      	=						model_id.split("@"				)

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



