





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase	:	Any		     =       logging.get_logger(__name__)

UpperCAmelCase	:	str		     =       {
    """kssteven/ibert-roberta-base""": """https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json""",
    """kssteven/ibert-roberta-large""": """https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json""",
    """kssteven/ibert-roberta-large-mnli""": (
        """https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"""
    ),
}







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					int						=			"""ibert"""







      def __init__(    self		,      lowerCAmelCase__=3_0_5_2_2		,      lowerCAmelCase__=7_6_8		,      lowerCAmelCase__=1_2		,      lowerCAmelCase__=1_2		,      lowerCAmelCase__=3_0_7_2		,      lowerCAmelCase__="gelu"		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=5_1_2		,      lowerCAmelCase__=2		,      lowerCAmelCase__=0.02		,      lowerCAmelCase__=1E-12		,      lowerCAmelCase__=1		,      lowerCAmelCase__=0		,      lowerCAmelCase__=2		,      lowerCAmelCase__="absolute"		,      lowerCAmelCase__=False		,      lowerCAmelCase__="none"		,      **lowerCAmelCase__		,      )   ->						Any:



        '''simple docstring'''




        super().__init__(pad_token_id=lowerCAmelCase__		,      bos_token_id=lowerCAmelCase__		,      eos_token_id=lowerCAmelCase__		,      **lowerCAmelCase__							)

        a__	:					str										=vocab_size
        a__	:					Optional[Any]										=hidden_size
        a__	:					str										=num_hidden_layers
        a__	:					List[Any]										=num_attention_heads
        a__	:					Any										=hidden_act
        a__	:					List[str]										=intermediate_size
        a__	:					Tuple										=hidden_dropout_prob
        a__	:					Any										=attention_probs_dropout_prob
        a__	:					Union[str, Any]										=max_position_embeddings
        a__	:					Tuple										=type_vocab_size
        a__	:					Tuple										=initializer_range
        a__	:					Union[str, Any]										=layer_norm_eps
        a__	:					Optional[int]										=position_embedding_type
        a__	:					str										=quant_mode
        a__	:					str										=force_dequant







class 				__lowerCAmelCase       (    UpperCamelCase__):







      @property
      def    _lowercase  (    self							)   ->						Mapping[str, Mapping[int, str]]:



        '''simple docstring'''




        if self.task == "multiple-choice":
          a__	:					Optional[Any]										={0: "batch", 1: "choice", 2: "sequence"}
        else:
          a__	:					Tuple										={0: "batch", 1: "sequence"}
        return OrderedDict(
            [
                ("input_ids", dynamic_axis),
                ("attention_mask", dynamic_axis),
            ]							)











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


A__  :  Union[str, 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__":
						A__  :  Optional[int]								=					'''hopper-medium-v2'''
						A__  :  int								=					gym.make(env_name)

						A__  :  Optional[int]								=					ValueGuidedRLPipeline.from_pretrained(
						    '''bglick13/hopper-medium-v2-value-function-hor32''',
						    env=env,
						)

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

																		# execute action in environment
																		A__					,     A__					,     A__					,     A__  :  str								=					env.step(denorm_actions)
																		A__  :  Dict								=					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())

																		A__  :  List[str]								=					next_observation
						except KeyboardInterrupt:
												pass

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





