





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


UpperCAmelCase	:	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__":
     UpperCAmelCase	:	Dict		     =       """hopper-medium-v2"""
     UpperCAmelCase	:	int		     =       gym.make(env_name)

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

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

               # execute action in environment
               UpperCAmelCase	:	Union[str, Any]		     =       env.step(denorm_actions)
               UpperCAmelCase	:	Any		     =       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	:	List[str]		     =       next_observation
     except KeyboardInterrupt:
          pass

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








"""simple docstring"""




import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin

@require_jieba
class 		a__						(      a_,			unittest.TestCase						):
   __lowerCAmelCase            =				CpmAntTokenizer
   __lowerCAmelCase            =				False
   def 			__magic_name__			(			self				):
          super().setUp()

          lowercase    :				List[str]       		=							[
              "<d>",
              "</d>",
              "<s>",
              "</s>",
              "</_>",
              "<unk>",
              "<pad>",
              "</n>",
              "我",
              "是",
              "C",
              "P",
              "M",
              "A",
              "n",
              "t",
          ]
          lowercase    :				Dict       		=							os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["vocab_file"]				)
          with open(self.vocab_file	,    "w"	,    encoding="utf-8"				) as vocab_writer:
                 vocab_writer.write("".join([x + "\n" for x in vocab_tokens]				)				)

   @tooslow
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"				)
          lowercase    :				str       		=							"今天天气真好！"
          lowercase    :				Optional[int]       		=							["今天", "天气", "真", "好", "！"]
          lowercase    :				int       		=							tokenizer.tokenize(_a				)
          self.assertListEqual(_a	,    _a				)
          lowercase    :				Any       		=							"今天天气真好！"
          lowercase    :				int       		=							[tokenizer.bos_token] + tokens

          lowercase    :				List[str]       		=							[6, 9_802, 14_962, 2_082, 831, 244]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_a				)	,    _a				)

          lowercase    :				str       		=							tokenizer.decode(_a				)
          self.assertEqual(_a	,    _a				)


