




"""simple docstring"""




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   UpperCAmelCase_    (			snake_case		):
		def __init__(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						)						->       Any:
					super().__init__()
					__lowercase			:  Optional[Any]								=       value_function
					__lowercase			:  Tuple								=       unet
					__lowercase			:  Any								=       scheduler
					__lowercase			:  List[str]								=       env
					__lowercase			:  Dict								=       env.get_dataset()
					__lowercase			:  Dict								=       {}
					for key in self.data.keys():
								try:
											__lowercase			:  List[str]								=       self.data[key].mean()
								except:  # noqa: E722
											pass
					__lowercase			:  List[str]								=       {}
					for key in self.data.keys():
								try:
											__lowercase			:  str								=       self.data[key].std()
								except:  # noqa: E722
											pass
					__lowercase			:  Any								=       env.observation_space.shape[0]
					__lowercase			:  List[Any]								=       env.action_space.shape[0]
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_       )						->       Any:
					return (x_in - self.means[key]) / self.stds[key]
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_       )						->       Tuple:
					return x_in * self.stds[key] + self.means[key]
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_       )						->       List[Any]:
					if type(UpperCamelCase_       ) is dict:
								return {k: self.to_torch(UpperCamelCase_       ) for k, v in x_in.items()}
					elif torch.is_tensor(UpperCamelCase_       ):
								return x_in.to(self.unet.device       )
					return torch.tensor(UpperCamelCase_				,						device=self.unet.device       )
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_       )						->       Union[str, Any]:
					for key, val in cond.items():
								__lowercase			:  Optional[int]								=       val.clone()
					return x_in
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_       )						->       str:
					__lowercase			:  List[str]								=       x.shape[0]
					__lowercase			:  int								=       None
					for i in tqdm.tqdm(self.scheduler.timesteps       ):
								# create batch of timesteps to pass into model
								__lowercase			:  Tuple								=       torch.full((batch_size,)				,						UpperCamelCase_				,						device=self.unet.device				,						dtype=torch.long       )
								for _ in range(UpperCamelCase_       ):
											with torch.enable_grad():
														x.requires_grad_()

														# permute to match dimension for pre-trained models
														__lowercase			:  Optional[int]								=       self.value_function(x.permute(0				,						2				,						1       )				,						UpperCamelCase_       ).sample
														__lowercase			:  List[Any]								=       torch.autograd.grad([y.sum()]				,						[x]       )[0]

														__lowercase			:  Dict								=       self.scheduler._get_variance(UpperCamelCase_       )
														__lowercase			:  str								=       torch.exp(0.5 * posterior_variance       )
														__lowercase			:  Tuple								=       model_std * grad

											__lowercase			:  Dict								=       0
											__lowercase			:  Optional[int]								=       x.detach()
											__lowercase			:  Optional[Any]								=       x + scale * grad
											__lowercase			:  List[str]								=       self.reset_xa(UpperCamelCase_				,						UpperCamelCase_				,						self.action_dim       )

								__lowercase			:  List[Any]								=       self.unet(x.permute(0				,						2				,						1       )				,						UpperCamelCase_       ).sample.permute(0				,						2				,						1       )

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

								# apply conditions to the trajectory (set the initial state)
								__lowercase			:  Any								=       self.reset_xa(UpperCamelCase_				,						UpperCamelCase_				,						self.action_dim       )
								__lowercase			:  Dict								=       self.to_torch(UpperCamelCase_       )
					return x, y
		def __call__(						self				,						UpperCamelCase_				,						UpperCamelCase_=64				,						UpperCamelCase_=32				,						UpperCamelCase_=2				,						UpperCamelCase_=0.1       )						->       List[Any]:
					# normalize the observations and create  batch dimension
					__lowercase			:  Optional[Any]								=       self.normalize(UpperCamelCase_				,						'''observations'''       )
					__lowercase			:  Any								=       obs[None].repeat(UpperCamelCase_				,						axis=0       )

					__lowercase			:  Optional[int]								=       {0: self.to_torch(UpperCamelCase_       )}
					__lowercase			:  Optional[int]								=       (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)
					__lowercase			:  int								=       randn_tensor(UpperCamelCase_				,						device=self.unet.device       )
					__lowercase			:  int								=       self.reset_xa(UpperCamelCase_				,						UpperCamelCase_				,						self.action_dim       )
					__lowercase			:  List[Any]								=       self.to_torch(UpperCamelCase_       )

					# run the diffusion process
					__lowercase			,__lowercase			:  str								=       self.run_diffusion(UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_       )

					# sort output trajectories by value
					__lowercase			:  Optional[Any]								=       y.argsort(0				,						descending=UpperCamelCase_       ).squeeze()
					__lowercase			:  List[Any]								=       x[sorted_idx]
					__lowercase			:  Union[str, Any]								=       sorted_values[:, :, : self.action_dim]
					__lowercase			:  List[str]								=       actions.detach().cpu().numpy()
					__lowercase			:  Optional[Any]								=       self.de_normalize(UpperCamelCase_				,						key='''actions'''       )

					# select the action with the highest value
					if y is not None:
								__lowercase			:  str								=       0
					else:
								# if we didn't run value guiding, select a random action
								__lowercase			:  Optional[int]								=       np.random.randint(0				,						UpperCamelCase_       )

					__lowercase			:  Optional[Any]								=       denorm_actions[selected_index, 0]
					return denorm_actions





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case      :							List[str]     	=    logging.get_logger(__name__)

_snake_case      :							int     	=    {
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  List[str]				   =					'''switch_transformers'''
						UpperCamelCase      :  Dict				   =					['''past_key_values''']
						UpperCamelCase      :  Union[str, Any]				   =					{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





						def __init__(			self							:int		,    lowerCAmelCase__							:str=32128		,    lowerCAmelCase__							:Union[str, Any]=768		,    lowerCAmelCase__							:Tuple=64		,    lowerCAmelCase__							:Any=2048		,    lowerCAmelCase__							:Optional[int]=64		,    lowerCAmelCase__							:Any=12		,    lowerCAmelCase__							:Any=3		,    lowerCAmelCase__							:Union[str, Any]=12		,    lowerCAmelCase__							:Tuple=3		,    lowerCAmelCase__							:str=12		,    lowerCAmelCase__							:Dict=8		,    lowerCAmelCase__							:Union[str, Any]=False		,    lowerCAmelCase__							:List[str]=0.01		,    lowerCAmelCase__							:Union[str, Any]="float32"		,    lowerCAmelCase__							:Optional[int]=False		,    lowerCAmelCase__							:Optional[Any]=32		,    lowerCAmelCase__							:int=128		,    lowerCAmelCase__							:Optional[Any]=0.1		,    lowerCAmelCase__							:Optional[Any]=1E-6		,    lowerCAmelCase__							:Any=0.0_01		,    lowerCAmelCase__							:Union[str, Any]=0.0_01		,    lowerCAmelCase__							:List[Any]=1.0		,    lowerCAmelCase__							:Optional[Any]="relu"		,    lowerCAmelCase__							:Optional[int]=True		,    lowerCAmelCase__							:List[Any]=False		,    lowerCAmelCase__							:int=True		,    lowerCAmelCase__							:int=0		,    lowerCAmelCase__							:Optional[Any]=1		,    **lowerCAmelCase__							:List[Any]		,    )  ->Dict:
										lowercase         =      vocab_size
										lowercase         =      d_model
										lowercase         =      d_kv
										lowercase         =      d_ff

										lowercase         =      num_sparse_encoder_layers

										lowercase         =      num_layers
										lowercase         =      (
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										lowercase         =      num_sparse_decoder_layers

										# This tells us, each how many encoder layer we'll have to set a sparse layer.
										if self.num_sparse_encoder_layers > 0:
														lowercase         =      self.num_layers // self.num_sparse_encoder_layers
										else:
														lowercase         =      self.num_layers  # HACK: this will create 0 sparse layers

										# This tells us, each how many encoder layer we'll have to set a sparse layer.
										if self.num_sparse_decoder_layers > 0:
														lowercase         =      self.num_decoder_layers // self.num_sparse_decoder_layers
										else:
														lowercase         =      self.num_decoder_layers  # HACK: this will create 0 sparse layers

										lowercase         =      num_heads
										lowercase         =      num_experts
										lowercase         =      expert_capacity
										lowercase         =      router_bias
										lowercase         =      router_jitter_noise
										if router_dtype not in ["float32", "float16", "bfloat16"]:
														raise ValueError(F'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''     )
										lowercase         =      router_dtype

										lowercase         =      router_ignore_padding_tokens
										lowercase         =      relative_attention_num_buckets
										lowercase         =      relative_attention_max_distance

										lowercase         =      dropout_rate
										lowercase         =      layer_norm_epsilon
										lowercase         =      initializer_factor
										lowercase         =      feed_forward_proj
										lowercase         =      use_cache
										lowercase         =      add_router_probs

										lowercase         =      router_z_loss_coef
										lowercase         =      router_aux_loss_coef

										lowercase         =      self.feed_forward_proj.split("-"     )
										lowercase         =      act_info[-1]
										lowercase         =      act_info[0] == "gated"

										if len(lowerCAmelCase__     ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase__     ) > 2:
														raise ValueError(
														    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
														    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
														    "'gated-gelu' or 'relu'"     )

										# for backwards compatibility
										if feed_forward_proj == "gated-gelu":
														lowercase         =      "gelu_new"

										super().__init__(
										    pad_token_id=lowerCAmelCase__		,    eos_token_id=lowerCAmelCase__		,    is_encoder_decoder=lowerCAmelCase__		,    **lowerCAmelCase__		,    )
