




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 			a__    (					snake_case__      ):






						def __init__(   self		,			_A		,			_A		,			_A		,			_A		,			):




											"""simple docstring"""



											super().__init__()
											__lowerCAmelCase				   =       value_function
											__lowerCAmelCase				   =       unet
											__lowerCAmelCase				   =       scheduler
											__lowerCAmelCase				   =       env
											__lowerCAmelCase				   =       env.get_dataset()
											__lowerCAmelCase				   =       {}
											for key in self.data.keys():
																try:
																					__lowerCAmelCase				   =       self.data[key].mean()
																except:  # noqa: E722
																					pass
											__lowerCAmelCase				   =       {}
											for key in self.data.keys():
																try:
																					__lowerCAmelCase				   =       self.data[key].std()
																except:  # noqa: E722
																					pass
											__lowerCAmelCase				   =       env.observation_space.shape[0]
											__lowerCAmelCase				   =       env.action_space.shape[0]






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											return (x_in - self.means[key]) / self.stds[key]






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											return x_in * self.stds[key] + self.means[key]






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											for key, val in cond.items():
																__lowerCAmelCase				   =       val.clone()
											return x_in






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       x.shape[0]
											__lowerCAmelCase				   =       None
											for i in tqdm.tqdm(self.scheduler.timesteps			):
																# create batch of timesteps to pass into model
																__lowerCAmelCase				   =       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
																										__lowerCAmelCase				   =       self.value_function(x.permute(0		,			2		,			1			)		,			_A			).sample
																										__lowerCAmelCase				   =       torch.autograd.grad([y.sum()]		,			[x]			)[0]

																										__lowerCAmelCase				   =       self.scheduler._get_variance(_A			)
																										__lowerCAmelCase				   =       torch.exp(0.5 * posterior_variance			)
																										__lowerCAmelCase				   =       model_std * grad

																					__lowerCAmelCase				   =       0
																					__lowerCAmelCase				   =       x.detach()
																					__lowerCAmelCase				   =       x + scale * grad
																					__lowerCAmelCase				   =       self.reset_xa(_A		,			_A		,			self.action_dim			)

																__lowerCAmelCase				   =       self.unet(x.permute(0		,			2		,			1			)		,			_A			).sample.permute(0		,			2		,			1			)

																# TODO: verify deprecation of this kwarg
																__lowerCAmelCase				   =       self.scheduler.step(_A		,			_A		,			_A		,			predict_epsilon=_A			)["prev_sample"]

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






						def __call__(   self		,			_A		,			_A=6_4		,			_A=3_2		,			_A=2		,			_A=0.1			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.normalize(_A		,			"observations"			)
											__lowerCAmelCase				   =       obs[None].repeat(_A		,			axis=0			)

											__lowerCAmelCase				   =       {0: self.to_torch(_A			)}
											__lowerCAmelCase				   =       (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)
											__lowerCAmelCase				   =       randn_tensor(_A		,			device=self.unet.device			)
											__lowerCAmelCase				   =       self.reset_xa(_A		,			_A		,			self.action_dim			)
											__lowerCAmelCase				   =       self.to_torch(_A			)

											# run the diffusion process
											__lowerCAmelCase   ,							__lowerCAmelCase				   =       self.run_diffusion(_A		,			_A		,			_A		,			_A			)

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

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

											__lowerCAmelCase				   =       denorm_actions[selected_index, 0]
											return denorm_actions










from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
					import tensorflow as tf


UpperCamelCase__    			=			logging.get_logger(__name__)
@dataclass
class 			a__    (					snake_case__      ):
						_a							:    List[str]					=    [
						    """no_inference""",
						    """no_cuda""",
						    """no_tpu""",
						    """no_speed""",
						    """no_memory""",
						    """no_env_print""",
						    """no_multi_process""",
						]






						def __init__(   self		,			**_A			):




											"""simple docstring"""



											for deprecated_arg in self.deprecated_args:
																if deprecated_arg in kwargs:
																					__lowerCAmelCase				   =       deprecated_arg[3:]
																					__lowerCAmelCase				   =       not kwargs.pop(_A			)
																					logger.warning(
																					    f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
																					    f""" {positive_arg}={kwargs[positive_arg]}"""			)
											__lowerCAmelCase				   =       kwargs.pop("tpu_name"		,			self.tpu_name			)
											__lowerCAmelCase				   =       kwargs.pop("device_idx"		,			self.device_idx			)
											__lowerCAmelCase				   =       kwargs.pop("eager_mode"		,			self.eager_mode			)
											__lowerCAmelCase				   =       kwargs.pop("use_xla"		,			self.use_xla			)
											super().__init__(**_A			)

						_a							:    str					=    field(
						    default=snake_case__ ,				metadata={"""help""": """Name of TPU"""} ,				)
						_a							:    int					=    field(
						    default=0 ,				metadata={"""help""": """CPU / GPU device index. Defaults to 0."""} ,				)
						_a							:    bool					=    field(default=snake_case__ ,				metadata={"""help""": """Benchmark models in eager model."""}      )
						_a							:    bool					=    field(
						    default=snake_case__ ,				metadata={
						        """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
						    } ,				)






						@cached_property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											requires_backends(self		,			["tf"]			)
											__lowerCAmelCase				   =       None
											if self.tpu:
																try:
																					if self.tpu_name:
																										__lowerCAmelCase				   =       tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name			)
																					else:
																										__lowerCAmelCase				   =       tf.distribute.cluster_resolver.TPUClusterResolver()
																except ValueError:
																					__lowerCAmelCase				   =       None
											return tpu






						@cached_property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											requires_backends(self		,			["tf"]			)
											if self.is_tpu:
																tf.config.experimental_connect_to_cluster(self._setup_tpu			)
																tf.tpu.experimental.initialize_tpu_system(self._setup_tpu			)

																__lowerCAmelCase				   =       tf.distribute.TPUStrategy(self._setup_tpu			)
											else:
																# currently no multi gpu is allowed
																if self.is_gpu:
																					# TODO: Currently only single GPU is supported
																					tf.config.set_visible_devices(self.gpu_list[self.device_idx]		,			"GPU"			)
																					__lowerCAmelCase				   =       tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}"""			)
																else:
																					tf.config.set_visible_devices([]		,			"GPU"			)  # disable GPU
																					__lowerCAmelCase				   =       tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}"""			)

											return strategy






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											requires_backends(self		,			["tf"]			)
											return self._setup_tpu is not None






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											requires_backends(self		,			["tf"]			)
											return self._setup_strategy






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											requires_backends(self		,			["tf"]			)
											return tf.config.list_physical_devices("GPU"			)






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											requires_backends(self		,			["tf"]			)
											if self.cuda:
																return len(self.gpu_list			)
											return 0






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return self.n_gpu > 0




