





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    __SCREAMING_SNAKE_CASE			( UpperCamelCase__					):







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







       def 						__lowerCamelCase    (      self     :	Optional[Any]		,					A     :	str		,					A     :	Any			)   ->Union[str, Any]:
          return (x_in - self.means[key]) / self.stds[key]







       def 						__lowerCamelCase    (      self     :	int		,					A     :	Tuple		,					A     :	Tuple			)   ->Optional[int]:
          return x_in * self.stds[key] + self.means[key]







       def 						__lowerCamelCase    (      self     :	Optional[int]		,					A     :	Union[str, Any]			)   ->List[str]:
          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     :	List[str]		,					A     :	Any		,					A     :	Dict		,					A     :	Dict			)   ->Optional[Any]:
          for key, val in cond.items():
             lowerCamelCase__   :						int					   =							val.clone()
          return x_in







       def 						__lowerCamelCase    (      self     :	Optional[int]		,					A     :	Tuple		,					A     :	List[str]		,					A     :	int		,					A     :	Optional[int]			)   ->Optional[int]:
          lowerCamelCase__   :						Union[str, Any]					   =							x.shape[0]
          lowerCamelCase__   :						Any					   =							None
          for i in tqdm.tqdm(self.scheduler.timesteps			):
             # create batch of timesteps to pass into model
             lowerCamelCase__   :						Dict					   =							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__   :						List[Any]					   =							self.value_function(x.permute(0		,					2		,					1			)		,					_a			).sample
                   lowerCamelCase__   :						Optional[Any]					   =							torch.autograd.grad([y.sum()]		,					[x]			)[0]

                   lowerCamelCase__   :						Optional[int]					   =							self.scheduler._get_variance(_a			)
                   lowerCamelCase__   :						List[Any]					   =							torch.exp(0.5 * posterior_variance			)
                   lowerCamelCase__   :						Tuple					   =							model_std * grad

                lowerCamelCase__   :						Optional[Any]					   =							0
                lowerCamelCase__   :						Any					   =							x.detach()
                lowerCamelCase__   :						List[Any]					   =							x + scale * grad
                lowerCamelCase__   :						Any					   =							self.reset_xa(_a		,					_a		,					self.action_dim			)

             lowerCamelCase__   :						str					   =							self.unet(x.permute(0		,					2		,					1			)		,					_a			).sample.permute(0		,					2		,					1			)

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

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







       def __call__(      self     :	Tuple		,					A     :	int		,					A     :	Any=6_4		,					A     :	Union[str, Any]=3_2		,					A     :	Optional[Any]=2		,					A     :	Optional[int]=0.1			)   ->Any:
          # normalize the observations and create  batch dimension
          lowerCamelCase__   :						Any					   =							self.normalize(_a		,					'''observations'''			)
          lowerCamelCase__   :						Optional[int]					   =							obs[None].repeat(_a		,					axis=0			)

          lowerCamelCase__   :						List[str]					   =							{0: self.to_torch(_a			)}
          lowerCamelCase__   :						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)
          lowerCamelCase__   :						Optional[int]					   =							randn_tensor(_a		,					device=self.unet.device			)
          lowerCamelCase__   :						int					   =							self.reset_xa(_a		,					_a		,					self.action_dim			)
          lowerCamelCase__   :						int					   =							self.to_torch(_a			)

          # run the diffusion process
          lowerCamelCase__   :						List[Any]					   =							self.run_diffusion(_a		,					_a		,					_a		,					_a			)

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

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

          lowerCamelCase__   :						Any					   =							denorm_actions[selected_index, 0]
          return denorm_actions









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 						lowercase							(   UpperCamelCase__		):
     _a							 =						"xmod"

     def __init__(  self		,     _a=3_0522		,     _a=768		,     _a=12		,     _a=12		,     _a=3072		,     _a="gelu"		,     _a=0.1		,     _a=0.1		,     _a=512		,     _a=2		,     _a=0.02		,     _a=1e-12		,     _a=1		,     _a=0		,     _a=2		,     _a="absolute"		,     _a=True		,     _a=None		,     _a=False		,     _a=2		,     _a=False		,     _a=True		,     _a=True		,     _a=("en_XX",)		,     _a=None		,     **_a		,     )						->							str:
           super().__init__(pad_token_id=_a		,     bos_token_id=_a		,     eos_token_id=_a		,     **_a				)

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







class 						lowercase							(   UpperCamelCase__		):

     @property
     def 					a__	(  self				)						->							Mapping[str, Mapping[int, str]]:
           if self.task == "multiple-choice":
                 _A							:     Dict			=						{0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
                 _A							:     Dict			=						{0: """batch""", 1: """sequence"""}
           return OrderedDict(
               [
                   ("""input_ids""", dynamic_axis),
                   ("""attention_mask""", dynamic_axis),
               ]				)

