


"""simple docstring"""




import json
import os
import tempfile
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment

def        lowercase							(		) ->      Optional[int]:
		__a           =			torch.nn.Linear(2    ,					4			)
		__a           =			torch.optim.AdamW(model.parameters()    ,					lr=1.0			)
		__a           =			torch.optim.lr_scheduler.OneCycleLR(lowerCAmelCase__    ,					max_lr=0.01    ,					steps_per_epoch=2    ,					epochs=1			)
		__a           =			DataLoader(TensorDataset(torch.tensor([1, 2, 3]			)			)			)
		__a           =			DataLoader(TensorDataset(torch.tensor([4, 5, 6]			)			)			)

		return model, optimizer, scheduler, train_dl, valid_dl

def        lowercase							(		lowerCAmelCase__ :     int			) ->      Union[str, Any]:
		return (model.weight.abs().sum() + model.bias.abs().sum()).item()

def        lowercase							(		lowerCAmelCase__ :     List[str]			) ->      Union[str, Any]:
		__a           =			torch.nn.Linear(*tuple(model.weight.T.shape			)			).state_dict()
		model.load_state_dict(lowerCAmelCase__			)




class 					__lowerCAmelCase					(   _SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		@require_cuda
		def 			__UpperCAmelCase       (  self      ):
				__a           =			Accelerator()
				assert PartialState._shared_state["_cpu"] is False
				assert PartialState._shared_state["device"].type == "cuda"
				with self.assertRaises(A_      ):
						__a           =			Accelerator(cpu=A_      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			Accelerator()
				__a           =			GradientState()
				assert state.num_steps == 1
				__a           =			4
				assert state.num_steps == 4

				assert state.sync_gradients is True
				__a           =			False
				assert state.sync_gradients is False
				GradientState._reset_state()

		def 			__UpperCAmelCase       (  self      ):
				__a           =			Accelerator()
				__a      ,     __a      ,     __a      ,     __a      ,     __a           =			create_components()

				(
				    (
				    __a  
				)      ,     (
				    __a  
				)      ,     (
				    __a  
				)      ,     (
				    __a  
				)      ,     (
				    __a  
				)      ,       
				)           =			accelerator.prepare(A_							, A_							, A_							, A_							, A_      )

				self.assertTrue(prepared_model in accelerator._models      )
				self.assertTrue(prepared_optimizer in accelerator._optimizers      )
				self.assertTrue(prepared_scheduler in accelerator._schedulers      )
				self.assertTrue(prepared_train_dl in accelerator._dataloaders      )
				self.assertTrue(prepared_valid_dl in accelerator._dataloaders      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			Accelerator()
				__a      ,     __a      ,     __a      ,     __a      ,     __a           =			create_components()
				accelerator.prepare(A_							, A_							, A_							, A_							, A_      )
				accelerator.free_memory()

				self.assertTrue(len(accelerator._models      ) == 0      )
				self.assertTrue(len(accelerator._optimizers      ) == 0      )
				self.assertTrue(len(accelerator._schedulers      ) == 0      )
				self.assertTrue(len(accelerator._dataloaders      ) == 0      )

		def 			__UpperCAmelCase       (  self      ):
				PartialState._reset_state()

				# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
				def noop(*_a							, **_a      ):
						pass

				with patch('''torch.cuda.set_device'''							, A_      ), patch_environment(ACCELERATE_TORCH_DEVICE='''cuda:64'''      ):
						__a           =			Accelerator()
						self.assertEqual(str(accelerator.state.device      )							, '''cuda:64'''      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			Accelerator()
				__a      ,     __a      ,     __a      ,     __a      ,     __a           =			create_components()
				accelerator.prepare(A_							, A_							, A_							, A_							, A_      )

				__a           =			get_signature(A_      )

				with tempfile.TemporaryDirectory() as tmpdirname:
						accelerator.save_state(A_      )

						# make sure random weights don't match
						load_random_weights(A_      )
						self.assertTrue(abs(model_signature - get_signature(A_      )      ) > 1E-3      )

						# make sure loaded weights match
						accelerator.load_state(A_      )
						self.assertTrue(abs(model_signature - get_signature(A_      )      ) < 1E-3      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			Accelerator()
				__a      ,     __a      ,     __a      ,     __a      ,     __a           =			create_components()
				accelerator.prepare(A_							, A_							, A_							, A_							, A_      )

				__a           =			get_signature(A_      )

				# saving hook
				def save_config(_a							, _a							, _a      ):
						__a           =			{'''class_name''': models[0].__class__.__name__}

						with open(os.path.join(A_							, '''data.json'''      )							, '''w'''      ) as f:
								json.dump(A_							, A_      )

        # loading hook
				def load_config(_a							, _a      ):
						with open(os.path.join(A_							, '''data.json'''      )							, '''r'''      ) as f:
								__a           =			json.load(A_      )

						__a           =			config['''class_name''']

				__a           =			accelerator.register_save_state_pre_hook(A_      )
				__a           =			accelerator.register_load_state_pre_hook(A_      )

				with tempfile.TemporaryDirectory() as tmpdirname:
						accelerator.save_state(A_      )

						# make sure random weights don't match with hooks
						load_random_weights(A_      )
						self.assertTrue(abs(model_signature - get_signature(A_      )      ) > 1E-3      )

						# random class name to verify correct one is loaded
						__a           =			'''random'''

						# make sure loaded weights match with hooks
						accelerator.load_state(A_      )
						self.assertTrue(abs(model_signature - get_signature(A_      )      ) < 1E-3      )

						# mode.class_name is loaded from config
						self.assertTrue(model.class_name == model.__class__.__name__      )

				# remove hooks
				save_hook.remove()
				load_hook.remove()

				with tempfile.TemporaryDirectory() as tmpdirname:
						accelerator.save_state(A_      )

						# make sure random weights don't match with hooks removed
						load_random_weights(A_      )
						self.assertTrue(abs(model_signature - get_signature(A_      )      ) > 1E-3      )

						# random class name to verify correct one is loaded
						__a           =			'''random'''

						# make sure loaded weights match with hooks removed
						accelerator.load_state(A_      )
						self.assertTrue(abs(model_signature - get_signature(A_      )      ) < 1E-3      )

						# mode.class_name is NOT loaded from config
						self.assertTrue(model.class_name != model.__class__.__name__      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			Accelerator()
				__a      ,     __a      ,     __a      ,     __a      ,     __a           =			create_components()
				__a           =			None

				# This should work
				__a      ,     __a      ,     __a      ,     __a      ,     __a      ,     __a           =			accelerator.prepare(
				    A_							, A_							, A_							, A_							, A_							, A_      )
				self.assertTrue(dummy_obj is None      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			Accelerator()
				__a      ,     __a      ,     __a      ,     __a      ,     __a           =			create_components()
				__a           =			[1, 2, 3]

				# This should work
				__a      ,     __a      ,     __a      ,     __a      ,     __a      ,     __a           =			accelerator.prepare(
				    A_							, A_							, A_							, A_							, A_							, A_      )
				self.assertEqual(
				    getattr(A_							, '''_is_accelerate_prepared'''							, A_      )							, A_							, '''Dummy object should have `_is_accelerate_prepared` set to `True`'''							, )
				self.assertEqual(
				    getattr(A_							, '''_is_accelerate_prepared'''							, A_      )							, A_							, '''Model is missing `_is_accelerator_prepared` or is set to `False`'''							, )
				self.assertEqual(
				    getattr(A_							, '''_is_accelerate_prepared'''							, A_      )							, A_							, '''Optimizer is missing `_is_accelerator_prepared` or is set to `False`'''							, )
				self.assertEqual(
				    getattr(A_							, '''_is_accelerate_prepared'''							, A_      )							, A_							, '''Scheduler is missing `_is_accelerator_prepared` or is set to `False`'''							, )
				self.assertEqual(
				    getattr(A_							, '''_is_accelerate_prepared'''							, A_      )							, A_							, '''Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`'''							, )
				self.assertEqual(
				    getattr(A_							, '''_is_accelerate_prepared'''							, A_      )							, A_							, '''Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`'''							, )

		@slow
		@require_bnb
		def 			__UpperCAmelCase       (  self      ):
				from transformers import AutoModelForCausalLM

				__a           =			AutoModelForCausalLM.from_pretrained(
				    '''EleutherAI/gpt-neo-125m'''							, load_in_abit=A_							, device_map={'''''': 0}							, )
				__a           =			Accelerator()

				# This should work
				__a           =			accelerator.prepare(A_      )

		@slow
		@require_bnb
		def 			__UpperCAmelCase       (  self      ):
				from transformers import AutoModelForCausalLM

				__a           =			Accelerator()

				with init_empty_weights():
						__a           =			AutoModelForCausalLM.from_pretrained(
						    '''EleutherAI/gpt-neo-125m'''							, )
						model.tie_weights()
						__a           =			infer_auto_device_map(A_      )
						__a           =			'''cpu'''

				__a           =			AutoModelForCausalLM.from_pretrained(
				    '''EleutherAI/gpt-neo-125m'''							, device_map=A_							, load_in_abit=A_							, llm_inta_enable_fpaa_cpu_offload=A_      )

				# This should not work and get value error
				with self.assertRaises(A_      ):
						__a           =			accelerator.prepare(A_      )

		@slow
		@require_bnb
		@require_multi_gpu
		def 			__UpperCAmelCase       (  self      ):
				from transformers import AutoModelForCausalLM

				__a           =			{'''distributed_type''': DistributedType.MULTI_GPU}

				with init_empty_weights():
						__a           =			AutoModelForCausalLM.from_pretrained(
						    '''EleutherAI/gpt-neo-125m'''							, )
						model.tie_weights()
						__a           =			infer_auto_device_map(A_      )
						__a           =			1

				__a           =			AutoModelForCausalLM.from_pretrained(
				    '''EleutherAI/gpt-neo-125m'''							, load_in_abit=A_							, device_map=A_							, )
				__a           =			Accelerator()

				# This should not work and get value error
				with self.assertRaises(A_      ):
						__a           =			accelerator.prepare(A_      )

				PartialState._reset_state()

		@slow
		@require_bnb
		@require_multi_gpu
		def 			__UpperCAmelCase       (  self      ):
				from transformers import AutoModelForCausalLM

				with init_empty_weights():
						__a           =			AutoModelForCausalLM.from_pretrained(
						    '''EleutherAI/gpt-neo-125m'''							, )
						__a           =			infer_auto_device_map(A_      )
						__a           =			1

				__a           =			AutoModelForCausalLM.from_pretrained(
				    '''EleutherAI/gpt-neo-125m'''							, load_in_abit=A_							, device_map=A_							, )
				__a           =			Accelerator()

				# This should work
				__a           =			accelerator.prepare(A_      )





		@require_cuda
		def 			__UpperCAmelCase       (  self      ):
				__a           =			torch.nn.Linear(10							, 10      )
				__a           =			torch.optim.SGD(model.parameters()							, lr=0.01      )
				__a           =			Accelerator(cpu=A_      )
				__a           =			accelerator.prepare(A_      )








import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_				,      A_				,      A_				,      A_				,      )  ->					Optional[int]:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase		=     value_function
				UpperCamelCase		=     unet
				UpperCamelCase		=     scheduler
				UpperCamelCase		=     env
				UpperCamelCase		=     env.get_dataset()
				UpperCamelCase		=     {}
				for key in self.data.keys():
						try:
								UpperCamelCase		=     self.data[key].mean()
						except:  # noqa: E722
								pass
				UpperCamelCase		=     {}
				for key in self.data.keys():
						try:
								UpperCamelCase		=     self.data[key].std()
						except:  # noqa: E722
								pass
				UpperCamelCase		=     env.observation_space.shape[0]
				UpperCamelCase		=     env.action_space.shape[0]



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Optional[Any]:
				"""simple docstring"""
				return (x_in - self.means[key]) / self.stds[key]



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				return x_in * self.stds[key] + self.means[key]



		def __UpperCamelCase ( self				,      A_       )  ->					Any:
				"""simple docstring"""
				if type(A_       ) is dict:
						return {k: self.to_torch(A_       ) for k, v in x_in.items()}
				elif torch.is_tensor(A_       ):
						return x_in.to(self.unet.device       )
				return torch.tensor(A_				,      device=self.unet.device       )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				for key, val in cond.items():
						UpperCamelCase		=     val.clone()
				return x_in



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     x.shape[0]
				UpperCamelCase		=     None
				for i in tqdm.tqdm(self.scheduler.timesteps       ):
						# create batch of timesteps to pass into model
						UpperCamelCase		=     torch.full((batch_size,)				,      A_				,      device=self.unet.device				,      dtype=torch.long       )
						for _ in range(A_       ):
								with torch.enable_grad():
										x.requires_grad_()

										# permute to match dimension for pre-trained models
										UpperCamelCase		=     self.value_function(x.permute(0				,      2				,      1       )				,      A_       ).sample
										UpperCamelCase		=     torch.autograd.grad([y.sum()]				,      [x]       )[0]

										UpperCamelCase		=     self.scheduler._get_variance(A_       )
										UpperCamelCase		=     torch.exp(0.5 * posterior_variance       )
										UpperCamelCase		=     model_std * grad

								UpperCamelCase		=     0
								UpperCamelCase		=     x.detach()
								UpperCamelCase		=     x + scale * grad
								UpperCamelCase		=     self.reset_xa(A_				,      A_				,      self.action_dim       )

						UpperCamelCase		=     self.unet(x.permute(0				,      2				,      1       )				,      A_       ).sample.permute(0				,      2				,      1       )

						# TODO: verify deprecation of this kwarg
						UpperCamelCase		=     self.scheduler.step(A_				,      A_				,      A_				,      predict_epsilon=A_       )['prev_sample']

						# apply conditions to the trajectory (set the initial state)
						UpperCamelCase		=     self.reset_xa(A_				,      A_				,      self.action_dim       )
						UpperCamelCase		=     self.to_torch(A_       )
				return x, y



		def __call__( self				,      A_				,      A_=64				,      A_=32				,      A_=2				,      A_=0.1       )  ->					List[str]:
				"""simple docstring"""
				# normalize the observations and create  batch dimension
				UpperCamelCase		=     self.normalize(A_				,      'observations'       )
				UpperCamelCase		=     obs[None].repeat(A_				,      axis=0       )

				UpperCamelCase		=     {0: self.to_torch(A_       )}
				UpperCamelCase		=     (batch_size, planning_horizon, self.state_dim + self.action_dim)

				# generate initial noise and apply our conditions (to make the trajectories start at current state)
				UpperCamelCase		=     randn_tensor(A_				,      device=self.unet.device       )
				UpperCamelCase		=     self.reset_xa(A_				,      A_				,      self.action_dim       )
				UpperCamelCase		=     self.to_torch(A_       )

				# run the diffusion process
				UpperCamelCase	,  UpperCamelCase		=     self.run_diffusion(A_				,      A_				,      A_				,      A_       )

				# sort output trajectories by value
				UpperCamelCase		=     y.argsort(0				,      descending=A_       ).squeeze()
				UpperCamelCase		=     x[sorted_idx]
				UpperCamelCase		=     sorted_values[:, :, : self.action_dim]
				UpperCamelCase		=     actions.detach().cpu().numpy()
				UpperCamelCase		=     self.de_normalize(A_				,      key='actions'       )

				# select the action with the highest value
				if y is not None:
						UpperCamelCase		=     0
				else:
						# if we didn't run value guiding, select a random action
						UpperCamelCase		=     np.random.randint(0				,      A_       )

				UpperCamelCase		=     denorm_actions[selected_index, 0]
				return denorm_actions
