
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_		(      a__       ):


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


       def 	_snake_case				(    self						,       __A						,       __A				)						->    int:
              return (x_in - self.means[key]) / self.stds[key]


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


       def 	_snake_case				(    self						,       __A				)						->    int:






              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 	_snake_case				(    self						,       __A						,       __A						,       __A				)						->    Tuple:






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


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

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

                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		=0
                            SCREAMING_SNAKE_CASE_							:				List[str]		=x.detach()
                            SCREAMING_SNAKE_CASE_							:				Tuple		=x + scale * grad
                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.reset_xa(_UpperCAmelCase						,       _UpperCAmelCase						,       self.action_dim				)

                     SCREAMING_SNAKE_CASE_							:				int		=self.unet(x.permute(0						,       2						,       1				)						,       _UpperCAmelCase				).sample.permute(0						,       2						,       1				)

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

                     # apply conditions to the trajectory (set the initial state)
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.reset_xa(_UpperCAmelCase						,       _UpperCAmelCase						,       self.action_dim				)
                     SCREAMING_SNAKE_CASE_							:				Dict		=self.to_torch(_UpperCAmelCase				)
              return x, y





       def __call__(    self						,       __A						,       __A=64						,       __A=32						,       __A=2						,       __A=0.1				)						->    str:
              SCREAMING_SNAKE_CASE_							:				str		=self.normalize(_UpperCAmelCase						,       '''observations'''				)
              SCREAMING_SNAKE_CASE_							:				str		=obs[None].repeat(_UpperCAmelCase						,       axis=0				)

              SCREAMING_SNAKE_CASE_							:				Any		={0: self.to_torch(_UpperCAmelCase				)}
              SCREAMING_SNAKE_CASE_							:				Any		=(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)
              SCREAMING_SNAKE_CASE_							:				Tuple		=randn_tensor(_UpperCAmelCase						,       device=self.unet.device				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.reset_xa(_UpperCAmelCase						,       _UpperCAmelCase						,       self.action_dim				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.to_torch(_UpperCAmelCase				)

              # run the diffusion process
              SCREAMING_SNAKE_CASE_							:				Any		=self.run_diffusion(_UpperCAmelCase						,       _UpperCAmelCase						,       _UpperCAmelCase						,       _UpperCAmelCase				)

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

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

              SCREAMING_SNAKE_CASE_							:				str		=denorm_actions[selected_index, 0]
              return denorm_actions







'''simple docstring'''




from ...configuration_utils import PretrainedConfig


lowercase__       : Any         =       {
    '''google/tapas-base-finetuned-sqa''': (
        '''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wtq''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wikisql-supervised''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-tabfact''': (
        '''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
    ),
}


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			'''tapas'''


     def __init__(		self		,      _UpperCAmelCase=3_0522		,      _UpperCAmelCase=768		,      _UpperCAmelCase=12		,      _UpperCAmelCase=12		,      _UpperCAmelCase=3072		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=1024		,      _UpperCAmelCase=[3, 256, 256, 2, 256, 256, 10]		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=1e-1_2		,      _UpperCAmelCase=0		,      _UpperCAmelCase=10.0		,      _UpperCAmelCase=0		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=None		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=False		,      _UpperCAmelCase=None		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase="ratio"		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase=64		,      _UpperCAmelCase=32		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=False		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase		,      **_UpperCAmelCase)

          # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
          __A		:					Dict						=			vocab_size
          __A		:					Tuple						=			hidden_size
          __A		:					Any						=			num_hidden_layers
          __A		:					int						=			num_attention_heads
          __A		:					Tuple						=			hidden_act
          __A		:					Tuple						=			intermediate_size
          __A		:					List[Any]						=			hidden_dropout_prob
          __A		:					int						=			attention_probs_dropout_prob
          __A		:					List[str]						=			max_position_embeddings
          __A		:					Optional[int]						=			type_vocab_sizes
          __A		:					str						=			initializer_range
          __A		:					List[str]						=			layer_norm_eps

          # Fine-tuning task hyperparameters
          __A		:					List[str]						=			positive_label_weight
          __A		:					List[Any]						=			num_aggregation_labels
          __A		:					Optional[Any]						=			aggregation_loss_weight
          __A		:					Tuple						=			use_answer_as_supervision
          __A		:					List[str]						=			answer_loss_importance
          __A		:					Any						=			use_normalized_answer_loss
          __A		:					Any						=			huber_loss_delta
          __A		:					Union[str, Any]						=			temperature
          __A		:					Tuple						=			aggregation_temperature
          __A		:					Optional[Any]						=			use_gumbel_for_cells
          __A		:					List[str]						=			use_gumbel_for_aggregation
          __A		:					Tuple						=			average_approximation_function
          __A		:					List[str]						=			cell_selection_preference
          __A		:					Dict						=			answer_loss_cutoff
          __A		:					Union[str, Any]						=			max_num_rows
          __A		:					Optional[Any]						=			max_num_columns
          __A		:					int						=			average_logits_per_cell
          __A		:					Optional[Any]						=			select_one_column
          __A		:					int						=			allow_empty_column_selection
          __A		:					List[Any]						=			init_cell_selection_weights_to_zero
          __A		:					int						=			reset_position_index_per_cell
          __A		:					Union[str, Any]						=			disable_per_token_loss

          # Aggregation hyperparameters
          __A		:					Optional[Any]						=			aggregation_labels
          __A		:					List[str]						=			no_aggregation_label_index

          if isinstance(self.aggregation_labels		,      _UpperCAmelCase):
               __A		:					Optional[Any]						=			{int(_UpperCAmelCase): v for k, v in aggregation_labels.items()}