





'''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       (a__				):


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      ):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          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 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






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

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

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

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

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

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


     def __call__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=64		,      _UpperCAmelCase=32		,      _UpperCAmelCase=2		,      _UpperCAmelCase=0.1):

          '''simple docstring'''






          __A		:					str						=			self.normalize(_UpperCAmelCase		,      'observations')
          __A		:					str						=			obs[None].repeat(_UpperCAmelCase		,      axis=0)

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

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

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

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

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






'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_camembert import CamembertTokenizer
else:
  lowercase__       : Optional[int]         =       None


lowercase__       : List[str]         =       logging.get_logger(__name__)

lowercase__       : Optional[Any]         =       {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase__       : List[str]         =       {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

lowercase__       : Dict         =       {
    '''camembert-base''': 5_12,
}

lowercase__       : str         =       '''▁'''


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			VOCAB_FILES_NAMES
     lowerCAmelCase						     =			PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase						     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase						     =			['''input_ids''', '''attention_mask''']
     lowerCAmelCase						     =			CamembertTokenizer


     def __init__(		self		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase="<s>"		,      _UpperCAmelCase="</s>"		,      _UpperCAmelCase="</s>"		,      _UpperCAmelCase="<s>"		,      _UpperCAmelCase="<unk>"		,      _UpperCAmelCase="<pad>"		,      _UpperCAmelCase="<mask>"		,      _UpperCAmelCase=["<s>NOTUSED", "</s>NOTUSED"]		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					int						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else mask_token

          super().__init__(
              _UpperCAmelCase		,      tokenizer_file=_UpperCAmelCase		,      bos_token=_UpperCAmelCase		,      eos_token=_UpperCAmelCase		,      sep_token=_UpperCAmelCase		,      cls_token=_UpperCAmelCase		,      unk_token=_UpperCAmelCase		,      pad_token=_UpperCAmelCase		,      mask_token=_UpperCAmelCase		,      additional_special_tokens=_UpperCAmelCase		,      **_UpperCAmelCase		,      )

          __A		:					List[str]						=			vocab_file
          __A		:					Optional[int]						=			False if not self.vocab_file else True


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          __A		:					Optional[Any]						=			[self.cls_token_id]
          __A		:					Optional[int]						=			[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          __A		:					Optional[int]						=			[self.sep_token_id]
          __A		:					List[str]						=			[self.cls_token_id]

          if token_ids_a is None:
               return len(cls + token_ids_a + sep) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          if not self.can_save_slow_tokenizer:
               raise ValueError(
                   'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                   'tokenizer.')

          if not os.path.isdir(_UpperCAmelCase):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory')
               return
          __A		:					List[Any]						=			os.path.join(
              _UpperCAmelCase		,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

          if os.path.abspath(self.vocab_file) != os.path.abspath(_UpperCAmelCase):
               copyfile(self.vocab_file		,      _UpperCAmelCase)

          return (out_vocab_file,)