


"""simple docstring"""




import copy
import os
from typing import Union

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


lowercase_	   =     logging.get_logger(__name__)

lowercase_	   =     {
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """git_vision_model"""




      def __init__(    self:	Dict						,				a:	List[str]=768						,				a:	Tuple=3072						,				a:	Union[str, Any]=12						,				a:	str=12						,				a:	Tuple=3						,				a:	List[str]=224						,				a:	str=16						,				a:	str="quick_gelu"						,				a:	Dict=1e-5						,				a:	Any=0.0						,				a:	Tuple=0.0_2						,				**a:	Tuple						,				):
       super().__init__(**__A    )

       __lowerCamelCase		:							Tuple		 =						hidden_size
       __lowerCamelCase		:							Dict		 =						intermediate_size
       __lowerCamelCase		:							Optional[Any]		 =						num_hidden_layers
       __lowerCamelCase		:							List[Any]		 =						num_attention_heads
       __lowerCamelCase		:							int		 =						num_channels
       __lowerCamelCase		:							Dict		 =						patch_size
       __lowerCamelCase		:							Tuple		 =						image_size
       __lowerCamelCase		:							Tuple		 =						initializer_range
       __lowerCamelCase		:							Tuple		 =						attention_dropout
       __lowerCamelCase		:							int		 =						layer_norm_eps
       __lowerCamelCase		:							List[Any]		 =						hidden_act




      @classmethod
      def 					_snake_case     (    cls:	Any						,				a:	Union[str, os.PathLike]						,				**a:	List[str]    ):
       cls._set_token_in_kwargs(__A    )

       __lowerCamelCase ,					__lowerCamelCase		:							Optional[Any]		 =						cls.get_config_dict(__A						,				**__A    )

       # get the vision config dict if we are loading from GITConfig
       if config_dict.get('model_type'    ) == "git":
        __lowerCamelCase		:							int		 =						config_dict['vision_config']

       if "model_type" in config_dict and hasattr(cls						,				'model_type'    ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
            F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

       return cls.from_dict(__A						,				**__A    )



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """git"""




      def __init__(    self:	int						,				a:	List[str]=None						,				a:	str=3_0522						,				a:	Any=768						,				a:	Tuple=6						,				a:	Union[str, Any]=12						,				a:	Dict=3072						,				a:	int="gelu"						,				a:	int=0.1						,				a:	List[str]=0.1						,				a:	Union[str, Any]=1024						,				a:	List[Any]=0.0_2						,				a:	str=1e-12						,				a:	List[str]=0						,				a:	Union[str, Any]="absolute"						,				a:	Tuple=True						,				a:	Union[str, Any]=False						,				a:	Tuple=101						,				a:	Any=102						,				a:	Optional[int]=None						,				**a:	str						,				):
       super().__init__(bos_token_id=__A						,				eos_token_id=__A						,				pad_token_id=__A						,				**__A    )

       if vision_config is None:
        __lowerCamelCase		:							Optional[int]		 =						{}
        logger.info('vision_config is None. initializing the GitVisionConfig with default values.'    )

       __lowerCamelCase		:							Tuple		 =						GitVisionConfig(**__A    )
       __lowerCamelCase		:							Dict		 =						vocab_size
       __lowerCamelCase		:							Union[str, Any]		 =						hidden_size
       __lowerCamelCase		:							Optional[Any]		 =						num_hidden_layers
       __lowerCamelCase		:							Optional[int]		 =						num_attention_heads
       __lowerCamelCase		:							List[str]		 =						hidden_act
       __lowerCamelCase		:							str		 =						intermediate_size
       __lowerCamelCase		:							Dict		 =						hidden_dropout_prob
       __lowerCamelCase		:							Union[str, Any]		 =						attention_probs_dropout_prob
       __lowerCamelCase		:							Optional[Any]		 =						max_position_embeddings
       __lowerCamelCase		:							List[Any]		 =						initializer_range
       __lowerCamelCase		:							Optional[int]		 =						layer_norm_eps
       __lowerCamelCase		:							List[Any]		 =						position_embedding_type
       __lowerCamelCase		:							List[Any]		 =						use_cache
       __lowerCamelCase		:							Tuple		 =						tie_word_embeddings
       __lowerCamelCase		:							Any		 =						num_image_with_embedding

       __lowerCamelCase		:							Optional[Any]		 =						bos_token_id
       __lowerCamelCase		:							List[str]		 =						eos_token_id




      def 					_snake_case     (    self:	List[Any]    ):
       __lowerCamelCase		:							str		 =						copy.deepcopy(self.__dict__    )
       __lowerCamelCase		:							Tuple		 =						self.vision_config.to_dict()
       __lowerCamelCase		:							Optional[int]		 =						self.__class__.model_type
       return output





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


lowercase_	   =     {
    'n_samples': 6_4,
    'horizon': 3_2,
    'num_inference_steps': 2_0,
    '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__":
       lowercase_	   =     'hopper-medium-v2'
       lowercase_	   =     gym.make(env_name)

       lowercase_	   =     ValueGuidedRLPipeline.from_pretrained(
           'bglick13/hopper-medium-v2-value-function-hor32',
           env=env,
       )

       env.seed(0)
       lowercase_	   =     env.reset()
       lowercase_	   =     0
       lowercase_	   =     0
       lowercase_	   =     1_0_0_0
       lowercase_	   =     [obs.copy()]
       try:
              for t in tqdm.tqdm(range(T)):
                     # call the policy
                     lowercase_	   =     pipeline(obs, planning_horizon=3_2)

                     # execute action in environment
                     lowercase_   ,lowercase_   ,lowercase_   ,lowercase_	   =     env.step(denorm_actions)
                     lowercase_	   =     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())

                     lowercase_	   =     next_observation
       except KeyboardInterrupt:
              pass

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

