


"""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 _lowerCAmelCase       (   __lowercase			):







   def __init__(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			)			->	Union[str, Any]:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		)			->	str:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		)			->	List[Any]:

       '''simple docstring'''




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







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	List[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 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)			->	Optional[int]:

       '''simple docstring'''




       for key, val in cond.items():
           snake_case					:   Optional[Any]         =					val.clone()
       return x_in







   def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		)			->	Tuple:

       '''simple docstring'''




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

                   snake_case					:   Optional[int]         =					self.scheduler._get_variance(__A		)
                   snake_case					:   int         =					torch.exp(0.5 * posterior_variance		)
                   snake_case					:   Optional[Any]         =					model_std * grad

               snake_case					:   List[Any]         =					0
               snake_case					:   Union[str, Any]         =					x.detach()
               snake_case					:   Union[str, Any]         =					x + scale * grad
               snake_case					:   Optional[Any]         =					self.reset_xa(__A		,			__A		,			self.action_dim		)

           snake_case					:   Tuple         =					self.unet(x.permute(0		,			2		,			1		)		,			__A		).sample.permute(0		,			2		,			1		)

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

           # apply conditions to the trajectory (set the initial state)
           snake_case					:   str         =					self.reset_xa(__A		,			__A		,			self.action_dim		)
           snake_case					:   Union[str, Any]         =					self.to_torch(__A		)
       return x, y







   def __call__(			self		,			UpperCamelCase__		,			UpperCamelCase__=64		,			UpperCamelCase__=32		,			UpperCamelCase__=2		,			UpperCamelCase__=0.1		)			->	str:

       '''simple docstring'''




       snake_case					:   Union[str, Any]         =					self.normalize(__A		,			"observations"		)
       snake_case					:   Any         =					obs[None].repeat(__A		,			axis=0		)

       snake_case					:   Dict         =					{0: self.to_torch(__A		)}
       snake_case					:   Dict         =					(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)
       snake_case					:   Tuple         =					randn_tensor(__A		,			device=self.unet.device		)
       snake_case					:   int         =					self.reset_xa(__A		,			__A		,			self.action_dim		)
       snake_case					:   Optional[int]         =					self.to_torch(__A		)

       # run the diffusion process
       snake_case      ,snake_case					:   List[str]         =					self.run_diffusion(__A		,			__A		,			__A		,			__A		)

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

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

       snake_case					:   List[str]         =					denorm_actions[selected_index, 0]
       return denorm_actions









"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

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


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Union[str, Any]													=						'''xmod'''







   def __init__(			self		,			UpperCamelCase__=3_0522		,			UpperCamelCase__=768		,			UpperCamelCase__=12		,			UpperCamelCase__=12		,			UpperCamelCase__=3072		,			UpperCamelCase__="gelu"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=512		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1e-12		,			UpperCamelCase__=1		,			UpperCamelCase__=0		,			UpperCamelCase__=2		,			UpperCamelCase__="absolute"		,			UpperCamelCase__=True		,			UpperCamelCase__=None		,			UpperCamelCase__=False		,			UpperCamelCase__=2		,			UpperCamelCase__=False		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__=("en_XX",)		,			UpperCamelCase__=None		,			**UpperCamelCase__		,			)			->	List[str]:

       '''simple docstring'''




       super().__init__(pad_token_id=UpperCamelCase__		,			bos_token_id=UpperCamelCase__		,			eos_token_id=UpperCamelCase__		,			**UpperCamelCase__		)

       snake_case					:   Optional[int]         =					vocab_size
       snake_case					:   Optional[Any]         =					hidden_size
       snake_case					:   Union[str, Any]         =					num_hidden_layers
       snake_case					:   int         =					num_attention_heads
       snake_case					:   Dict         =					hidden_act
       snake_case					:   Union[str, Any]         =					intermediate_size
       snake_case					:   str         =					hidden_dropout_prob
       snake_case					:   List[str]         =					attention_probs_dropout_prob
       snake_case					:   Dict         =					max_position_embeddings
       snake_case					:   Union[str, Any]         =					type_vocab_size
       snake_case					:   Union[str, Any]         =					initializer_range
       snake_case					:   str         =					layer_norm_eps
       snake_case					:   Dict         =					position_embedding_type
       snake_case					:   Any         =					use_cache
       snake_case					:   str         =					classifier_dropout
       snake_case					:   Any         =					pre_norm
       snake_case					:   Union[str, Any]         =					adapter_reduction_factor
       snake_case					:   Optional[int]         =					adapter_layer_norm
       snake_case					:   int         =					adapter_reuse_layer_norm
       snake_case					:   List[Any]         =					ln_before_adapter
       snake_case					:   Tuple         =					list(UpperCamelCase__		)
       snake_case					:   Tuple         =					default_language







class _lowerCAmelCase       (   snake_case_			):







   @property
   def 				lowerCamelCase	(			self		)			->	Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       if self.task == "multiple-choice":
           snake_case					:   str         =					{0: "batch", 1: "choice", 2: "sequence"}
       else:
           snake_case					:   Tuple         =					{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]		)





