
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     _a					(						lowerCamelCase=2,	lowerCamelCase=3,	lowerCamelCase=16,	lowerCamelCase = 10,	lowerCamelCase = 2							):

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

 lowerCamelCase		:	Optional[int]							=      get_dataset(lowerCamelCase							)
 lowerCamelCase		:	str							=      get_dataset(lowerCamelCase							)
 lowerCamelCase		:	Dict							=      DataLoader(lowerCamelCase,	shuffle=lowerCamelCase,	batch_size=lowerCamelCase,	num_workers=4							)
 lowerCamelCase		:	Optional[int]							=      DataLoader(lowerCamelCase,	shuffle=lowerCamelCase,	batch_size=lowerCamelCase,	num_workers=4							)
 return (train_dataloader, valid_dataloader)





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase=None							):
 lowerCamelCase		:	Any							=      []
 for epoch in range(lowerCamelCase							):
  # Train quickly
  model.train()
  for batch in dataloader:
   lowerCamelCase			,    lowerCamelCase		:	Dict							=      batch
   lowerCamelCase		:	Tuple							=      model(lowerCamelCase							)
   lowerCamelCase		:	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 							A__		(	nn.Module):
      def __init__(      self      ):
       super().__init__()
       lowerCamelCase		:	Tuple							=      nn.Parameter(torch.randn(1      )      )
       lowerCamelCase		:	List[Any]							=      nn.Parameter(torch.randn(1      )      )






      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       return x * self.a + self.b





class 							A__		(	unittest.TestCase):
      def 						UpperCamelCase__							(      self      ):
       with tempfile.TemporaryDirectory() as tmpdir:
        set_seed(4_2      )
        lowerCamelCase		:	Optional[int]							=      DummyModel()
        lowerCamelCase		:	Union[str, Any]							=      torch.optim.Adam(params=model.parameters()    ,  lr=1e-3      )
        lowerCamelCase			,    lowerCamelCase		:	List[Any]							=      dummy_dataloaders()
        lowerCamelCase		:	int							=      ProjectConfiguration(total_limit=1    ,  project_dir=__magic_name__    ,  automatic_checkpoint_naming=__magic_name__      )
        # Train baseline
        lowerCamelCase		:	int							=      Accelerator(project_config=__magic_name__      )
        lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Union[str, Any]							=      accelerator.prepare(
            __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      )
        # Save initial
        accelerator.save_state()

        # Save second state
        accelerator.save_state()
        self.assertEqual(len(os.listdir(accelerator.project_dir      )      )    ,  1      )
      def 						UpperCamelCase__							(      self      ):
       with tempfile.TemporaryDirectory() as tmpdir:
        set_seed(4_2      )
        lowerCamelCase		:	Optional[Any]							=      DummyModel()
        lowerCamelCase		:	Union[str, Any]							=      torch.optim.Adam(params=model.parameters()    ,  lr=1e-3      )
        lowerCamelCase			,    lowerCamelCase		:	List[Any]							=      dummy_dataloaders()
        # Train baseline
        lowerCamelCase		:	Tuple							=      Accelerator()
        lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	List[Any]							=      accelerator.prepare(
            __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      )
        # Save initial
        lowerCamelCase		:	int							=      os.path.join(__magic_name__    ,  """initial"""      )
        accelerator.save_state(__magic_name__      )
        ((lowerCamelCase)			,    (lowerCamelCase))		:	Union[str, Any]							=      model.a.item(), model.b.item()
        lowerCamelCase		:	Dict							=      optimizer.state_dict()
        lowerCamelCase		:	str							=      train(3    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      )
        ((lowerCamelCase)			,    (lowerCamelCase))		:	str							=      model.a.item(), model.b.item()
        lowerCamelCase		:	List[Any]							=      optimizer.state_dict()

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

        lowerCamelCase		:	Tuple							=      train(2    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      )
        # Save everything
        lowerCamelCase		:	List[Any]							=      os.path.join(__magic_name__    ,  """checkpoint"""      )
        accelerator.save_state(__magic_name__      )

        # Load everything back in and make sure all states work
        accelerator.load_state(__magic_name__      )
        test_rands += train(1    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      )
        ((lowerCamelCase)			,    (lowerCamelCase))		:	Optional[int]							=      model.a.item(), model.b.item()
        lowerCamelCase		:	str							=      optimizer.state_dict()
        self.assertEqual(__magic_name__    ,  __magic_name__      )
        self.assertEqual(__magic_name__    ,  __magic_name__      )
        self.assertEqual(__magic_name__    ,  __magic_name__      )
        self.assertEqual(__magic_name__    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       with tempfile.TemporaryDirectory() as tmpdir:
        set_seed(4_2      )
        lowerCamelCase		:	Tuple							=      DummyModel()
        lowerCamelCase		:	Any							=      torch.optim.Adam(params=model.parameters()    ,  lr=1e-3      )
        lowerCamelCase			,    lowerCamelCase		:	Optional[int]							=      dummy_dataloaders()
        lowerCamelCase		:	Optional[int]							=      ProjectConfiguration(automatic_checkpoint_naming=__magic_name__      )

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

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

        lowerCamelCase		:	Optional[Any]							=      train(2    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      )
        # Save everything
        accelerator.save_state()

        # Load everything back in and make sure all states work
        accelerator.load_state(os.path.join(__magic_name__    ,  """checkpoints"""    ,  """checkpoint_1"""      )      )
        test_rands += train(1    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      )
        ((lowerCamelCase)			,    (lowerCamelCase))		:	List[Any]							=      model.a.item(), model.b.item()
        lowerCamelCase		:	Optional[int]							=      optimizer.state_dict()
        self.assertEqual(__magic_name__    ,  __magic_name__      )
        self.assertEqual(__magic_name__    ,  __magic_name__      )
        self.assertEqual(__magic_name__    ,  __magic_name__      )
        self.assertEqual(__magic_name__    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[Any]							=      torch.tensor([1, 2, 3]      )
       lowerCamelCase		:	Union[str, Any]							=      torch.tensor([2, 3, 4]      )
       lowerCamelCase		:	Union[str, Any]							=      DummyModel()
       lowerCamelCase		:	Optional[int]							=      torch.optim.Adam(net.parameters()      )
       lowerCamelCase		:	int							=      Accelerator()
       with self.assertRaises(__magic_name__      ) as ve:
        accelerator.register_for_checkpointing(__magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      )
       lowerCamelCase		:	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 						UpperCamelCase__							(      self      ):
       with tempfile.TemporaryDirectory() as tmpdir:
        set_seed(4_2      )
        lowerCamelCase		:	Optional[Any]							=      DummyModel()
        lowerCamelCase		:	Optional[int]							=      torch.optim.Adam(params=model.parameters()    ,  lr=1e-3      )
        lowerCamelCase		:	Tuple							=      torch.optim.lr_scheduler.StepLR(__magic_name__    ,  step_size=1    ,  gamma=0.99      )
        lowerCamelCase			,    lowerCamelCase		:	List[Any]							=      dummy_dataloaders()
        lowerCamelCase		:	Dict							=      ProjectConfiguration(automatic_checkpoint_naming=__magic_name__      )
        # Train baseline
        lowerCamelCase		:	Optional[Any]							=      Accelerator(project_dir=__magic_name__    ,  project_config=__magic_name__      )
        lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	int							=      accelerator.prepare(
            __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      )
        # Save initial
        accelerator.save_state()
        lowerCamelCase		:	Dict							=      scheduler.state_dict()
        train(3    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      )
        self.assertNotEqual(__magic_name__    ,  scheduler.state_dict()      )

        # Load everything back in and make sure all states work
        accelerator.load_state(os.path.join(__magic_name__    ,  """checkpoints"""    ,  """checkpoint_0"""      )      )
        self.assertEqual(__magic_name__    ,  scheduler.state_dict()      )
      def 						UpperCamelCase__							(      self      ):
       with tempfile.TemporaryDirectory() as tmpdir:
        set_seed(4_2      )
        lowerCamelCase		:	List[Any]							=      DummyModel()
        lowerCamelCase		:	Tuple							=      ProjectConfiguration(automatic_checkpoint_naming=__magic_name__    ,  total_limit=2      )
        # Train baseline
        lowerCamelCase		:	Optional[Any]							=      Accelerator(project_dir=__magic_name__    ,  project_config=__magic_name__      )
        lowerCamelCase		:	Optional[int]							=      accelerator.prepare(__magic_name__      )
        # Save 3 states:
        for _ in range(1_1      ):
         accelerator.save_state()
        self.assertTrue(not os.path.exists(os.path.join(__magic_name__    ,  """checkpoints"""    ,  """checkpoint_0"""      )      )      )
        self.assertTrue(os.path.exists(os.path.join(__magic_name__    ,  """checkpoints"""    ,  """checkpoint_9"""      )      )      )
        self.assertTrue(os.path.exists(os.path.join(__magic_name__    ,  """checkpoints"""    ,  """checkpoint_10"""      )      )      )






      @require_cuda
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      ["""torchrun""", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__      )]
       execute_subprocess_async(__magic_name__    ,  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()






import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Union[str, Any]           =			"""conditional_detr"""
      _UpperCAmelCase				:    Optional[int]           =			["""past_key_values"""]
      _UpperCAmelCase				:    Optional[Any]           =			{
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
      }
      def __init__(      self    ,  __magic_name__=True    ,  __magic_name__=None    ,  __magic_name__=3    ,  __magic_name__=3_0_0    ,  __magic_name__=6    ,  __magic_name__=2_0_4_8    ,  __magic_name__=8    ,  __magic_name__=6    ,  __magic_name__=2_0_4_8    ,  __magic_name__=8    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=True    ,  __magic_name__="relu"    ,  __magic_name__=2_5_6    ,  __magic_name__=0.1    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.02    ,  __magic_name__=1.0    ,  __magic_name__=False    ,  __magic_name__="sine"    ,  __magic_name__="resnet50"    ,  __magic_name__=True    ,  __magic_name__=False    ,  __magic_name__=2    ,  __magic_name__=5    ,  __magic_name__=2    ,  __magic_name__=1    ,  __magic_name__=1    ,  __magic_name__=2    ,  __magic_name__=5    ,  __magic_name__=2    ,  __magic_name__=0.25    ,  **__magic_name__    ,  ):
       if backbone_config is not None and use_timm_backbone:
        raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""      )

       if not use_timm_backbone:
        if backbone_config is None:
         logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""      )
         lowerCamelCase		:	Optional[int]							=      CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]      )
        elif isinstance(__magic_name__    ,  __magic_name__      ):
         lowerCamelCase		:	List[Any]							=      backbone_config.get("""model_type"""      )
         lowerCamelCase		:	Dict							=      CONFIG_MAPPING[backbone_model_type]
         lowerCamelCase		:	str							=      config_class.from_dict(__magic_name__      )

       lowerCamelCase		:	Dict							=      use_timm_backbone
       lowerCamelCase		:	str							=      backbone_config
       lowerCamelCase		:	Tuple							=      num_channels
       lowerCamelCase		:	Dict							=      num_queries
       lowerCamelCase		:	Any							=      d_model
       lowerCamelCase		:	Optional[Any]							=      encoder_ffn_dim
       lowerCamelCase		:	List[str]							=      encoder_layers
       lowerCamelCase		:	Union[str, Any]							=      encoder_attention_heads
       lowerCamelCase		:	Any							=      decoder_ffn_dim
       lowerCamelCase		:	Dict							=      decoder_layers
       lowerCamelCase		:	Union[str, Any]							=      decoder_attention_heads
       lowerCamelCase		:	Dict							=      dropout
       lowerCamelCase		:	List[str]							=      attention_dropout
       lowerCamelCase		:	Union[str, Any]							=      activation_dropout
       lowerCamelCase		:	Optional[int]							=      activation_function
       lowerCamelCase		:	int							=      init_std
       lowerCamelCase		:	str							=      init_xavier_std
       lowerCamelCase		:	Tuple							=      encoder_layerdrop
       lowerCamelCase		:	str							=      decoder_layerdrop
       lowerCamelCase		:	Tuple							=      encoder_layers
       lowerCamelCase		:	Optional[int]							=      auxiliary_loss
       lowerCamelCase		:	Optional[Any]							=      position_embedding_type
       lowerCamelCase		:	Optional[int]							=      backbone
       lowerCamelCase		:	Union[str, Any]							=      use_pretrained_backbone
       lowerCamelCase		:	str							=      dilation
       # Hungarian matcher
       lowerCamelCase		:	Optional[Any]							=      class_cost
       lowerCamelCase		:	Dict							=      bbox_cost
       lowerCamelCase		:	Tuple							=      giou_cost
       # Loss coefficients
       lowerCamelCase		:	Union[str, Any]							=      mask_loss_coefficient
       lowerCamelCase		:	Dict							=      dice_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      cls_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      bbox_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      giou_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      focal_alpha
       super().__init__(is_encoder_decoder=__magic_name__    ,  **__magic_name__      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return self.encoder_attention_heads
      @property
      def 						UpperCamelCase__							(      self      ):
       return self.d_model






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[int]							=      copy.deepcopy(self.__dict__      )
       if self.backbone_config is not None:
        lowerCamelCase		:	Optional[int]							=      self.backbone_config.to_dict()
       lowerCamelCase		:	Optional[Any]							=      self.__class__.model_type
       return output





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Any           =			version.parse("""1.11""")
      @property
      def 						UpperCamelCase__							(      self      ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
               ("""pixel_mask""", {0: """batch"""}),
           ]      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return 1e-5






      @property
      def 						UpperCamelCase__							(      self      ):
       return 1_2




