


'''simple docstring'''







from typing import List, Optional, Union

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


SCREAMING_SNAKE_CASE_   =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_   =      {
    """huggingface/informer-tourism-monthly""": (
        """https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"""
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}


class 			lowerCAmelCase_    (							lowercase_   ):





  """simple docstring"""



  a_							:Tuple				       ='''informer'''
  a_							:List[Any]				       ={
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
      '''num_hidden_layers''': '''encoder_layers''',
  }

  def __init__(       self  :				List[Any]					,				SCREAMING_SNAKE_CASE__  :				List[Any] = None					,				SCREAMING_SNAKE_CASE__  :				List[str] = None					,				SCREAMING_SNAKE_CASE__  :				int = "student_t"					,				SCREAMING_SNAKE_CASE__  :				Optional[Any] = "nll"					,				SCREAMING_SNAKE_CASE__  :				int = 1					,				SCREAMING_SNAKE_CASE__  :				List[Any] = None					,				SCREAMING_SNAKE_CASE__  :				List[str] = "mean"					,				SCREAMING_SNAKE_CASE__  :				Union[str, Any] = 0					,				SCREAMING_SNAKE_CASE__  :				List[Any] = 0					,				SCREAMING_SNAKE_CASE__  :				Optional[int] = 0					,				SCREAMING_SNAKE_CASE__  :				Tuple = 0					,				SCREAMING_SNAKE_CASE__  :				Optional[Any] = None					,				SCREAMING_SNAKE_CASE__  :				str = None					,				SCREAMING_SNAKE_CASE__  :				Tuple = 6_4					,				SCREAMING_SNAKE_CASE__  :				str = 3_2					,				SCREAMING_SNAKE_CASE__  :				Any = 3_2					,				SCREAMING_SNAKE_CASE__  :				Union[str, Any] = 2					,				SCREAMING_SNAKE_CASE__  :				Dict = 2					,				SCREAMING_SNAKE_CASE__  :				List[Any] = 2					,				SCREAMING_SNAKE_CASE__  :				List[Any] = 2					,				SCREAMING_SNAKE_CASE__  :				Any = True					,				SCREAMING_SNAKE_CASE__  :				Tuple = "gelu"					,				SCREAMING_SNAKE_CASE__  :				Optional[Any] = 0.0_5					,				SCREAMING_SNAKE_CASE__  :				Tuple = 0.1					,				SCREAMING_SNAKE_CASE__  :				Optional[int] = 0.1					,				SCREAMING_SNAKE_CASE__  :				Union[str, Any] = 0.1					,				SCREAMING_SNAKE_CASE__  :				str = 0.1					,				SCREAMING_SNAKE_CASE__  :				Union[str, Any] = 1_0_0					,				SCREAMING_SNAKE_CASE__  :				Tuple = 0.0_2					,				SCREAMING_SNAKE_CASE__  :				Dict=True					,				SCREAMING_SNAKE_CASE__  :				Dict = "prob"					,				SCREAMING_SNAKE_CASE__  :				List[str] = 5					,				SCREAMING_SNAKE_CASE__  :				List[Any] = True					,				**SCREAMING_SNAKE_CASE__  :				Any					,				):





       '''simple docstring'''

       __a   			= prediction_length
       __a   			= context_length or prediction_length
       __a   			= distribution_output
       __a   			= loss
       __a   			= input_size
       __a   			= num_time_features
       __a   			= lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
       __a   			= scaling
       __a   			= num_dynamic_real_features
       __a   			= num_static_real_features
       __a   			= num_static_categorical_features

       # set cardinality
       if cardinality and num_static_categorical_features > 0:
            if len(SCREAMING_SNAKE_CASE__    ) != num_static_categorical_features:
                 raise ValueError(
                     """The cardinality should be a list of the same length as `num_static_categorical_features`"""    )
            __a   			= cardinality
       else:
            __a   			= [0]

       # set embedding_dimension
       if embedding_dimension and num_static_categorical_features > 0:
            if len(SCREAMING_SNAKE_CASE__    ) != num_static_categorical_features:
                 raise ValueError(
                     """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""    )
            __a   			= embedding_dimension
       else:
            __a   			= [min(5_0					,				(cat + 1) // 2    ) for cat in self.cardinality]

       __a   			= num_parallel_samples

       # Transformer architecture configuration
       __a   			= input_size * len(self.lags_sequence    ) + self._number_of_features
       __a   			= d_model
       __a   			= encoder_attention_heads
       __a   			= decoder_attention_heads
       __a   			= encoder_ffn_dim
       __a   			= decoder_ffn_dim
       __a   			= encoder_layers
       __a   			= decoder_layers

       __a   			= dropout
       __a   			= attention_dropout
       __a   			= activation_dropout
       __a   			= encoder_layerdrop
       __a   			= decoder_layerdrop

       __a   			= activation_function
       __a   			= init_std

       __a   			= use_cache

       # Informer
       __a   			= attention_type
       __a   			= sampling_factor
       __a   			= distil

       super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__    )


  @property
  def __a    (       self  :				int    ):





       '''simple docstring'''

       return (
           sum(self.embedding_dimension    )
           + self.num_dynamic_real_features
           + self.num_time_features
           + self.num_static_real_features
           + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
       )









import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


_UpperCAmelCase						:  List[Any]									=			{
    """n_samples""": 64,
    """horizon""": 32,
    """num_inference_steps""": 20,
    """n_guide_steps""": 2,  # can set to 0 for faster sampling, does not use value network
    """scale_grad_by_std""": True,
    """scale""": 0.1,
    """eta""": 0.0,
    """t_grad_cutoff""": 2,
    """device""": """cpu""",
}


if __name__ == "__main__":
					_UpperCAmelCase						:  List[str]									=			"""hopper-medium-v2"""
					_UpperCAmelCase						:  Any									=			gym.make(env_name)

					_UpperCAmelCase						:  Tuple									=			ValueGuidedRLPipeline.from_pretrained(
					    """bglick13/hopper-medium-v2-value-function-hor32""",
					    env=env,
					)

					env.seed(0)
					_UpperCAmelCase						:  Tuple									=			env.reset()
					_UpperCAmelCase						:  str									=			0
					_UpperCAmelCase						:  Union[str, Any]									=			0
					_UpperCAmelCase						:  List[str]									=			1000
					_UpperCAmelCase						:  Optional[int]									=			[obs.copy()]
					try:
										for t in tqdm.tqdm(range(T)):
															# call the policy
															_UpperCAmelCase						:  int									=			pipeline(obs, planning_horizon=32)

															# execute action in environment
															_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase						:  str									=			env.step(denorm_actions)
															_UpperCAmelCase						:  Tuple									=			env.get_normalized_score(total_reward)

															# update return
															total_reward += reward
															total_score += score
															print(
															    F'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
															    F''' {total_score}'''
															)

															# save observations for rendering
															rollout.append(next_observation.copy())

															_UpperCAmelCase						:  Dict									=			next_observation
					except KeyboardInterrupt:
										pass

					print(F'''Total reward: {total_reward}''')




