

'''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_       						=							logging.getLogger(__name__)





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

  def get_dataset(snake_case ):
    SCREAMING_SNAKE_CASE:Tuple						      =					torch.randn(batch_size * n_batches      ,       1 )
    return TensorDataset(SCREAMING_SNAKE_CASE_      ,       a * x + b + 0.1 * torch.randn(batch_size * n_batches      ,       1 ) )

  SCREAMING_SNAKE_CASE:Any						      =					get_dataset(SCREAMING_SNAKE_CASE_ )
  SCREAMING_SNAKE_CASE:Any						      =					get_dataset(SCREAMING_SNAKE_CASE_ )
  SCREAMING_SNAKE_CASE:List[Any]						      =					DataLoader(SCREAMING_SNAKE_CASE_      ,       shuffle=SCREAMING_SNAKE_CASE_      ,       batch_size=SCREAMING_SNAKE_CASE_      ,       num_workers=4 )
  SCREAMING_SNAKE_CASE:str						      =					DataLoader(SCREAMING_SNAKE_CASE_      ,       shuffle=SCREAMING_SNAKE_CASE_      ,       batch_size=SCREAMING_SNAKE_CASE_      ,       num_workers=4 )
  return (train_dataloader, valid_dataloader)





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





class 							_snake_case (       nn.Module						):
   def __init__( self			:      List[Any]							):
     super().__init__()
     SCREAMING_SNAKE_CASE:Any						      =					nn.Parameter(torch.randn(1							)							)
     SCREAMING_SNAKE_CASE:List[str]						      =					nn.Parameter(torch.randn(1							)							)







   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      int							):
     return x * self.a + self.b





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

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

       SCREAMING_SNAKE_CASE:Optional[Any]						      =					train(2     ,__lowercase     ,__lowercase     ,__lowercase     ,__lowercase							)
       # Save everything
       SCREAMING_SNAKE_CASE:List[str]						      =					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							)
       ((SCREAMING_SNAKE_CASE)      ,    (SCREAMING_SNAKE_CASE)):Any						      =					model.a.item(), model.b.item()
       SCREAMING_SNAKE_CASE:Optional[int]						      =					optimizer.state_dict()
       self.assertEqual(__lowercase     ,__lowercase							)
       self.assertEqual(__lowercase     ,__lowercase							)
       self.assertEqual(__lowercase     ,__lowercase							)
       self.assertEqual(__lowercase     ,__lowercase							)
   def     __UpperCamelCase						( self			:      List[Any]							):
     with tempfile.TemporaryDirectory() as tmpdir:
       set_seed(42							)
       SCREAMING_SNAKE_CASE:str						      =					DummyModel()
       SCREAMING_SNAKE_CASE:List[str]						      =					torch.optim.Adam(params=model.parameters()     ,lr=1e-3							)
       SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					dummy_dataloaders()
       SCREAMING_SNAKE_CASE:Dict						      =					ProjectConfiguration(automatic_checkpoint_naming=__lowercase							)

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

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

       SCREAMING_SNAKE_CASE:Optional[Any]						      =					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							)
       ((SCREAMING_SNAKE_CASE)      ,    (SCREAMING_SNAKE_CASE)):str						      =					model.a.item(), model.b.item()
       SCREAMING_SNAKE_CASE:Any						      =					optimizer.state_dict()
       self.assertEqual(__lowercase     ,__lowercase							)
       self.assertEqual(__lowercase     ,__lowercase							)
       self.assertEqual(__lowercase     ,__lowercase							)
       self.assertEqual(__lowercase     ,__lowercase							)
   def     __UpperCamelCase						( self			:      List[str]							):
     SCREAMING_SNAKE_CASE:Tuple						      =					torch.tensor([1, 2, 3]							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					torch.tensor([2, 3, 4]							)
     SCREAMING_SNAKE_CASE:List[str]						      =					DummyModel()
     SCREAMING_SNAKE_CASE:int						      =					torch.optim.Adam(net.parameters()							)
     SCREAMING_SNAKE_CASE:int						      =					Accelerator()
     with self.assertRaises(__lowercase							) as ve:
       accelerator.register_for_checkpointing(__lowercase     ,__lowercase     ,__lowercase     ,__lowercase							)
     SCREAMING_SNAKE_CASE: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			:      Union[str, Any]							):
     with tempfile.TemporaryDirectory() as tmpdir:
       set_seed(42							)
       SCREAMING_SNAKE_CASE:Tuple						      =					DummyModel()
       SCREAMING_SNAKE_CASE:str						      =					torch.optim.Adam(params=model.parameters()     ,lr=1e-3							)
       SCREAMING_SNAKE_CASE:Any						      =					torch.optim.lr_scheduler.StepLR(__lowercase     ,step_size=1     ,gamma=0.99							)
       SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:str						      =					dummy_dataloaders()
       SCREAMING_SNAKE_CASE:Dict						      =					ProjectConfiguration(automatic_checkpoint_naming=__lowercase							)
       # Train baseline
       SCREAMING_SNAKE_CASE:Optional[int]						      =					Accelerator(project_dir=__lowercase     ,project_config=__lowercase							)
       SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					accelerator.prepare(
           __lowercase     ,__lowercase     ,__lowercase     ,__lowercase     ,__lowercase							)
       # Save initial
       accelerator.save_state()
       SCREAMING_SNAKE_CASE: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     __UpperCamelCase						( self			:      Dict							):
     with tempfile.TemporaryDirectory() as tmpdir:
       set_seed(42							)
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					DummyModel()
       SCREAMING_SNAKE_CASE:List[str]						      =					ProjectConfiguration(automatic_checkpoint_naming=__lowercase     ,total_limit=2							)
       # Train baseline
       SCREAMING_SNAKE_CASE:Dict						      =					Accelerator(project_dir=__lowercase     ,project_config=__lowercase							)
       SCREAMING_SNAKE_CASE:List[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     __UpperCamelCase						( self			:      Tuple							):
     SCREAMING_SNAKE_CASE: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__":
    A_       						=							"/tmp/accelerate/state_checkpointing"
    A_       						=							DummyModel()
    A_       						=							torch.optim.Adam(params=model.parameters(), lr=1e-3)
    A_       						=							torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
    A_						,	A_       						=							dummy_dataloaders()
    A_       						=							ProjectConfiguration(automatic_checkpoint_naming=True)
    # Train baseline
    A_       						=							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_       						=							accelerator.prepare(
        model, optimizer, train_dataloader, valid_dataloader, scheduler
    )
    A_						,	A_       						=							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_       						=							group["params"][0].device
        break
    assert param_device.type == accelerator.device.type
    A_       						=							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_       						=							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_       						=							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 importlib
from pathlib import Path


# Test all the extensions added in the setup
A_       						=							[
    "kernels/rwkv/wkv_cuda.cu",
    "kernels/rwkv/wkv_op.cpp",
    "kernels/deformable_detr/ms_deform_attn.h",
    "kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh",
    "models/graphormer/algos_graphormer.pyx",
]





def    A_  ( snake_case ):
  # Test all the extensions added in the setup
  for file in FILES_TO_FIND:
    if not (transformers_path / file).exists():
      return False
  return True


if __name__ == "__main__":
    A_       						=							argparse.ArgumentParser()
    parser.add_argument("--check_lib", action="store_true", help="Whether to check the build or the actual package.")
    A_       						=							parser.parse_args()
    if args.check_lib:
        A_       						=							importlib.import_module("transformers")
        A_       						=							Path(transformers_module.__file__).parent
    else:
        A_       						=							Path.cwd() / "build/lib/transformers"
    if not test_custom_files_are_present(transformers_path):
        raise ValueError("The built release does not contain the custom files. Fix this before going further!")






