


"""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


lowercase_	   =     logging.getLogger(__name__)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__=2      ,	SCREAMING_SNAKE_CASE__=3      ,	SCREAMING_SNAKE_CASE__=16      ,	SCREAMING_SNAKE_CASE__ = 10      ,	SCREAMING_SNAKE_CASE__ = 2						):

 def get_dataset(SCREAMING_SNAKE_CASE__						):
  __lowerCamelCase		:							str		 =						torch.randn(batch_size * n_batches      ,	1						)
  return TensorDataset(SCREAMING_SNAKE_CASE__      ,	a * x + b + 0.1 * torch.randn(batch_size * n_batches      ,	1						)						)

 __lowerCamelCase		:							List[Any]		 =						get_dataset(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Any		 =						get_dataset(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							int		 =						DataLoader(SCREAMING_SNAKE_CASE__      ,	shuffle=SCREAMING_SNAKE_CASE__      ,	batch_size=SCREAMING_SNAKE_CASE__      ,	num_workers=4						)
 __lowerCamelCase		:							Tuple		 =						DataLoader(SCREAMING_SNAKE_CASE__      ,	shuffle=SCREAMING_SNAKE_CASE__      ,	batch_size=SCREAMING_SNAKE_CASE__      ,	num_workers=4						)
 return (train_dataloader, valid_dataloader)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__=None						):
 __lowerCamelCase		:							str		 =						[]
 for epoch in range(SCREAMING_SNAKE_CASE__						):
  # Train quickly
  model.train()
  for batch in dataloader:
   __lowerCamelCase		:							Dict		 =						batch
   __lowerCamelCase		:							List[str]		 =						model(SCREAMING_SNAKE_CASE__						)
   __lowerCamelCase		:							Any		 =						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 	A_ (	nn.Module   ):




      '''simple docstring'''


      def __init__(    self:	Union[str, Any]    ):
       super().__init__()
       __lowerCamelCase		:							Dict		 =						nn.Parameter(torch.randn(1    )    )
       __lowerCamelCase		:							Tuple		 =						nn.Parameter(torch.randn(1    )    )




      def 					_snake_case     (    self:	Tuple						,				a:	Union[str, Any]    ):
       return x * self.a + self.b



class 	A_ (	unittest.TestCase   ):




      '''simple docstring'''


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

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

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




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

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

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

        __lowerCamelCase		:							List[Any]		 =						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    )
        (__lowerCamelCase)		:							Any		 =						model.a.item(), model.b.item()
        __lowerCamelCase		:							Any		 =						optimizer.state_dict()
        self.assertEqual(a						,				a    )
        self.assertEqual(a						,				a    )
        self.assertEqual(a						,				a    )
        self.assertEqual(a						,				a    )




      def 					_snake_case     (    self:	Optional[int]    ):
       __lowerCamelCase		:							int		 =						torch.tensor([1, 2, 3]    )
       __lowerCamelCase		:							Optional[int]		 =						torch.tensor([2, 3, 4]    )
       __lowerCamelCase		:							Any		 =						DummyModel()
       __lowerCamelCase		:							Tuple		 =						torch.optim.Adam(net.parameters()    )
       __lowerCamelCase		:							List[Any]		 =						Accelerator()
       with self.assertRaises(a    ) as ve:
        accelerator.register_for_checkpointing(a						,				a						,				a						,				a    )
       __lowerCamelCase		:							List[str]		 =						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 					_snake_case     (    self:	Tuple    ):
       with tempfile.TemporaryDirectory() as tmpdir:
        set_seed(42    )
        __lowerCamelCase		:							Tuple		 =						DummyModel()
        __lowerCamelCase		:							List[Any]		 =						torch.optim.Adam(params=model.parameters()						,				lr=1e-3    )
        __lowerCamelCase		:							str		 =						torch.optim.lr_scheduler.StepLR(a						,				step_size=1						,				gamma=0.9_9    )
        __lowerCamelCase		:							Dict		 =						dummy_dataloaders()
        __lowerCamelCase		:							Optional[Any]		 =						ProjectConfiguration(automatic_checkpoint_naming=a    )
        # Train baseline
        __lowerCamelCase		:							Dict		 =						Accelerator(project_dir=a						,				project_config=a    )
        __lowerCamelCase		:							Optional[int]		 =						accelerator.prepare(
            a						,				a						,				a						,				a						,				a    )
        # Save initial
        accelerator.save_state()
        __lowerCamelCase		:							List[str]		 =						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 					_snake_case     (    self:	List[str]    ):
       with tempfile.TemporaryDirectory() as tmpdir:
        set_seed(42    )
        __lowerCamelCase		:							Union[str, Any]		 =						DummyModel()
        __lowerCamelCase		:							List[str]		 =						ProjectConfiguration(automatic_checkpoint_naming=a						,				total_limit=2    )
        # Train baseline
        __lowerCamelCase		:							Tuple		 =						Accelerator(project_dir=a						,				project_config=a    )
        __lowerCamelCase		:							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 					_snake_case     (    self:	Optional[int]    ):
       __lowerCamelCase		:							List[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__":
       lowercase_	   =     '/tmp/accelerate/state_checkpointing'
       lowercase_	   =     DummyModel()
       lowercase_	   =     torch.optim.Adam(params=model.parameters(), lr=1E-3)
       lowercase_	   =     torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
       lowercase_   ,lowercase_	   =     dummy_dataloaders()
       lowercase_	   =     ProjectConfiguration(automatic_checkpoint_naming=True)
       # Train baseline
       lowercase_	   =     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)
       lowercase_   ,lowercase_   ,lowercase_   ,lowercase_   ,lowercase_	   =     accelerator.prepare(
           model, optimizer, train_dataloader, valid_dataloader, scheduler
       )
       lowercase_   ,lowercase_	   =     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:
              lowercase_	   =     group['params'][0].device
              break
       assert param_device.type == accelerator.device.type
       lowercase_	   =     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:
              lowercase_	   =     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:
              lowercase_	   =     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 darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


lowercase_	   =     {
    'n_samples': 6_4,
    'horizon': 3_2,
    'num_inference_steps': 2_0,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
       lowercase_	   =     'hopper-medium-v2'
       lowercase_	   =     gym.make(env_name)

       lowercase_	   =     ValueGuidedRLPipeline.from_pretrained(
           'bglick13/hopper-medium-v2-value-function-hor32',
           env=env,
       )

       env.seed(0)
       lowercase_	   =     env.reset()
       lowercase_	   =     0
       lowercase_	   =     0
       lowercase_	   =     1_0_0_0
       lowercase_	   =     [obs.copy()]
       try:
              for t in tqdm.tqdm(range(T)):
                     # call the policy
                     lowercase_	   =     pipeline(obs, planning_horizon=3_2)

                     # execute action in environment
                     lowercase_   ,lowercase_   ,lowercase_   ,lowercase_	   =     env.step(denorm_actions)
                     lowercase_	   =     env.get_normalized_score(total_reward)

                     # update return
                     total_reward += reward
                     total_score += score
                     print(
                         F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
                         F""" {total_score}"""
                     )

                     # save observations for rendering
                     rollout.append(next_observation.copy())

                     lowercase_	   =     next_observation
       except KeyboardInterrupt:
              pass

       print(F"""Total reward: {total_reward}""")

