





import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Dict:
  if isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	):
    lowercase__			    =						np.full((len(_SCREAMING_SNAKE_CASE	), sequence_length, 2)	,		_SCREAMING_SNAKE_CASE	)
  else:
    lowercase__			    =						np.full((len(_SCREAMING_SNAKE_CASE	), sequence_length)	,		_SCREAMING_SNAKE_CASE	)

  for i, tensor in enumerate(_SCREAMING_SNAKE_CASE	):
    if padding_side == "right":
      if isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	):
        lowercase__			    =						tensor[:sequence_length]
      else:
        lowercase__			    =						tensor[:sequence_length]
    else:
      if isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	):
        lowercase__			    =						tensor[:sequence_length]
      else:
        lowercase__			    =						tensor[:sequence_length]

  return out_tensor.tolist()




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Union[str, Any]:
  lowercase__			    =						ord(_SCREAMING_SNAKE_CASE	)
  if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
    return True
  lowercase__			    =						unicodedata.category(_SCREAMING_SNAKE_CASE	)
  if cat.startswith('P'	):
    return True
  return False


@dataclass
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):

   '''simple docstring'''





   _UpperCamelCase					:	PreTrainedTokenizerBase
   _UpperCamelCase					:	Union[bool, str, PaddingStrategy]				      = True
   _UpperCamelCase					:	Optional[int]				      = None
   _UpperCamelCase					:	Optional[int]				      = None
   _UpperCamelCase					:	int				      = -1_00
   _UpperCamelCase					:	str				      = "pt"


   def  SCREAMING_SNAKE_CASE_							(					self    :       str    ,  a    :       List[str] )->     str:





     """simple docstring"""







     import torch

     lowercase__			    =						'label' if 'label' in features[0].keys() else 'labels'
     lowercase__			    =						[feature[label_name] for feature in features] if label_name in features[0].keys() else None
     lowercase__			    =						self.tokenizer.pad(
         a    ,  padding=self.padding    ,  max_length=self.max_length    ,  pad_to_multiple_of=self.pad_to_multiple_of    ,  return_tensors='pt' if labels is None else None    ,  )

     if labels is None:
       return batch

     lowercase__			    =						torch.tensor(batch['entity_ids'] ).shape[1]
     lowercase__			    =						self.tokenizer.padding_side
     if padding_side == "right":
       lowercase__			    =						[
           list(a ) + [self.label_pad_token_id] * (sequence_length - len(a )) for label in labels
       ]
     else:
       lowercase__			    =						[
           [self.label_pad_token_id] * (sequence_length - len(a )) + list(a ) for label in labels
       ]

     lowercase__			    =						[feature['ner_tags'] for feature in features]
     lowercase__			    =						padding_tensor(a    ,  -1    ,  a    ,  a )
     lowercase__			    =						[feature['original_entity_spans'] for feature in features]
     lowercase__			    =						padding_tensor(a    ,  (-1, -1)    ,  a    ,  a )
     lowercase__			    =						{k: torch.tensor(a    ,  dtype=torch.intaa ) for k, v in batch.items()}

     return batch









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[Any]    ,  a    :       UNetaDModel    ,  a    :       UNetaDModel    ,  a    :       DDPMScheduler    ,  a    :       Any    ,  )->     Dict:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       Any    ,  a    :       int )->     Dict:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int]    ,  a    :       str    ,  a    :       List[str] )->     str:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  a    :       Tuple )->     Tuple:





     """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  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       Optional[int]    ,  a    :       Dict    ,  a    :       Optional[Any] )->     List[Any]:





     """simple docstring"""







     for key, val in cond.items():
       lowercase__			    =						val.clone()
     return x_in


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple    ,  a    :       Optional[Any]    ,  a    :       Any    ,  a    :       Optional[Any]    ,  a    :       Optional[int] )->     List[Any]:





     """simple docstring"""







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

           lowercase__			    =						self.scheduler._get_variance(a )
           lowercase__			    =						torch.exp(0.5 * posterior_variance )
           lowercase__			    =						model_std * grad

         lowercase__			    =						0
         lowercase__			    =						x.detach()
         lowercase__			    =						x + scale * grad
         lowercase__			    =						self.reset_xa(a    ,  a    ,  self.action_dim )

       lowercase__			    =						self.unet(x.permute(0    ,  2    ,  1 )    ,  a ).sample.permute(0    ,  2    ,  1 )

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

       # apply conditions to the trajectory (set the initial state)
       lowercase__			    =						self.reset_xa(a    ,  a    ,  self.action_dim )
       lowercase__			    =						self.to_torch(a )
     return x, y


   def __call__(					self    :       Any    ,  a    :       Tuple    ,  a    :       int=64    ,  a    :       Tuple=32    ,  a    :       List[Any]=2    ,  a    :       List[str]=0.1 )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						self.normalize(a    ,  'observations' )
     lowercase__			    =						obs[None].repeat(a    ,  axis=0 )

     lowercase__			    =						{0: self.to_torch(a )}
     lowercase__			    =						(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__			    =						randn_tensor(a    ,  device=self.unet.device )
     lowercase__			    =						self.reset_xa(a    ,  a    ,  self.action_dim )
     lowercase__			    =						self.to_torch(a )

     # run the diffusion process
     lowercase__    ,					lowercase__			    =						self.run_diffusion(a    ,  a    ,  a    ,  a )

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

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

     lowercase__			    =						denorm_actions[selected_index, 0]
     return denorm_actions


