




'''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      SCREAMING_SNAKE_CASE    (						snake_case_				):






      def __init__( self   :  Dict						,						lowercase__   :  UNetaDModel						,						lowercase__   :  UNetaDModel						,						lowercase__   :  DDPMScheduler						,						lowercase__   :  Optional[Any]						,						):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  Tuple						,						lowercase__   :  Dict       ):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  Any						,						lowercase__   :  Any       ):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

           if type(lowercase__       ) is dict:
                return {k: self.to_torch(lowercase__       ) for k, v in x_in.items()}
           elif torch.is_tensor(lowercase__       ):
                return x_in.to(self.unet.device       )
           return torch.tensor(lowercase__						,						device=self.unet.device       )






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  Optional[int]						,						lowercase__   :  Any						,						lowercase__   :  Optional[Any]       ):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  int						,						lowercase__   :  int						,						lowercase__   :  int						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  int       ):


           '''simple docstring'''

           a_     :							List[str]     						=  x.shape[0]
           a_     :							Optional[int]     						=  None
           for i in tqdm.tqdm(self.scheduler.timesteps       ):
                # create batch of timesteps to pass into model
                a_     :							str     						=  torch.full((batch_size,)						,						lowercase__						,						device=self.unet.device						,						dtype=torch.long       )
                for _ in range(lowercase__       ):
                     with torch.enable_grad():
                          x.requires_grad_()

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

                          a_     :							Any     						=  self.scheduler._get_variance(lowercase__       )
                          a_     :							Dict     						=  torch.exp(0.5 * posterior_variance       )
                          a_     :							str     						=  model_std * grad

                     a_     :							Optional[int]     						=  0
                     a_     :							Union[str, Any]     						=  x.detach()
                     a_     :							Optional[int]     						=  x + scale * grad
                     a_     :							Optional[Any]     						=  self.reset_xa(lowercase__						,						lowercase__						,						self.action_dim       )

                a_     :							Any     						=  self.unet(x.permute(0						,						2						,						1       )						,						lowercase__       ).sample.permute(0						,						2						,						1       )

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

                # apply conditions to the trajectory (set the initial state)
                a_     :							Optional[Any]     						=  self.reset_xa(lowercase__						,						lowercase__						,						self.action_dim       )
                a_     :							Tuple     						=  self.to_torch(lowercase__       )
           return x, y






      def __call__( self   :  Optional[Any]						,						lowercase__   :  Dict						,						lowercase__   :  List[str]=64						,						lowercase__   :  str=32						,						lowercase__   :  Dict=2						,						lowercase__   :  Optional[Any]=0.1       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  self.normalize(lowercase__						,						"""observations"""       )
           a_     :							Dict     						=  obs[None].repeat(lowercase__						,						axis=0       )

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

           # run the diffusion process
           a_						,     a_     :							Dict     						=  self.run_diffusion(lowercase__						,						lowercase__						,						lowercase__						,						lowercase__       )

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

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

           a_     :							Union[str, Any]     						=  denorm_actions[selected_index, 0]
           return denorm_actions






'''simple docstring'''


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


lowerCAmelCase_      :  str				     =  logging.get_logger(__name__)

lowerCAmelCase_      :  Any				     =  {
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       str   							=					'''transfo-xl'''
      __magic_name__  :       List[str]   							=					['''mems''']
      __magic_name__  :       Dict   							=					{
          '''n_token''': '''vocab_size''',
          '''hidden_size''': '''d_model''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }






      def __init__( self   :  Union[str, Any]						,						lowercase__   :  List[Any]=26_7735						,						lowercase__   :  Optional[Any]=[2_0000, 4_0000, 20_0000]						,						lowercase__   :  Optional[Any]=1024						,						lowercase__   :  str=1024						,						lowercase__   :  Any=16						,						lowercase__   :  int=64						,						lowercase__   :  str=4096						,						lowercase__   :  Union[str, Any]=4						,						lowercase__   :  List[Any]=False						,						lowercase__   :  List[Any]=18						,						lowercase__   :  str=1600						,						lowercase__   :  str=1000						,						lowercase__   :  Any=True						,						lowercase__   :  Optional[Any]=True						,						lowercase__   :  Union[str, Any]=0						,						lowercase__   :  str=-1						,						lowercase__   :  int=True						,						lowercase__   :  str=0.1						,						lowercase__   :  Optional[Any]=0.0						,						lowercase__   :  Tuple=True						,						lowercase__   :  Optional[int]="normal"						,						lowercase__   :  str=0.01						,						lowercase__   :  List[str]=0.01						,						lowercase__   :  Union[str, Any]=0.02						,						lowercase__   :  str=1e-5						,						lowercase__   :  Any=0						,						**lowercase__   :  List[str]						,						):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  vocab_size
           a_     :							Optional[int]     						=  []
           self.cutoffs.extend(lowercase__       )
           if proj_share_all_but_first:
                a_     :							Any     						=  [False] + [True] * len(self.cutoffs       )
           else:
                a_     :							Tuple     						=  [False] + [False] * len(self.cutoffs       )
           a_     :							Tuple     						=  d_model
           a_     :							Optional[int]     						=  d_embed
           a_     :							List[Any]     						=  d_head
           a_     :							List[str]     						=  d_inner
           a_     :							Tuple     						=  div_val
           a_     :							Dict     						=  pre_lnorm
           a_     :							Optional[Any]     						=  n_layer
           a_     :							Dict     						=  n_head
           a_     :							Any     						=  mem_len
           a_     :							Union[str, Any]     						=  same_length
           a_     :							Dict     						=  attn_type
           a_     :							List[str]     						=  clamp_len
           a_     :							str     						=  sample_softmax
           a_     :							Any     						=  adaptive
           a_     :							List[Any]     						=  dropout
           a_     :							str     						=  dropatt
           a_     :							Dict     						=  untie_r
           a_     :							Tuple     						=  init
           a_     :							Optional[int]     						=  init_range
           a_     :							List[Any]     						=  proj_init_std
           a_     :							Optional[int]     						=  init_std
           a_     :							int     						=  layer_norm_epsilon
           super().__init__(eos_token_id=lowercase__						,						**lowercase__       )






      @property
      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           logger.info(F"The model {self.model_type} is one of the few models that has no sequence length limit."       )
           return -1






      @max_position_embeddings.setter
      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  Tuple       ):


           '''simple docstring'''

           raise NotImplementedError(
               F"The model {self.model_type} is one of the few models that has no sequence length limit."       )
