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


lowerCamelCase__     :		Any        = {
    """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__":
     lowerCamelCase__     :		Any        = """hopper-medium-v2"""
     lowerCamelCase__     :		Optional[Any]        = gym.make(env_name)

     lowerCamelCase__     :		List[Any]        = ValueGuidedRLPipeline.from_pretrained(
         """bglick13/hopper-medium-v2-value-function-hor32""",
         env=env,
     )

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

               # execute action in environment
               lowerCamelCase__   ,    lowerCamelCase__   ,    lowerCamelCase__   ,    lowerCamelCase__     :		List[str]        = env.step(denorm_actions)
               lowerCamelCase__     :		Union[str, 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())

               lowerCamelCase__     :		Tuple        = next_observation
     except KeyboardInterrupt:
          pass

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








# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


lowerCamelCase__     :		Optional[int]        = [
    """EAGER""",
    """AOT_EAGER""",
    """INDUCTOR""",
    """NVFUSER""",
    """AOT_NVFUSER""",
    """AOT_CUDAGRAPHS""",
    """OFI""",
    """FX2TRT""",
    """ONNXRT""",
    """IPEX""",
]

def 		UpperCamelCase						(   lowercase_			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None )		->						Optional[Any]:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   True
  while ask_again:
    lowercase__	: Tuple     		=   input(lowercase_ )
    try:
      if default is not None and len(lowercase_ ) == 0:
        return default
      return convert_value(lowercase_ ) if convert_value is not None else result
    except Exception:
      if error_message is not None:
        print(lowercase_ )

def 		UpperCamelCase						(   lowercase_			,			lowercase_=[]			,			lowercase_=None			,			lowercase_=0 )		->						Union[str, Any]:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   BulletMenu(lowercase_			,			lowercase_ )
  lowercase__	: Any     		=   menu.run(default_choice=lowercase_ )
  return convert_value(lowercase_ ) if convert_value is not None else result

def 		UpperCamelCase						(   lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: Union[str, Any]     		=   int(lowercase_ )
  return ComputeEnvironment(["""LOCAL_MACHINE""", """AMAZON_SAGEMAKER"""][value] )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  lowercase__	: List[str]     		=   int(lowercase_ )
  return DistributedType(["""NO""", """MULTI_CPU""", """MULTI_XPU""", """MULTI_GPU""", """MULTI_NPU""", """TPU"""][value] )

def 		UpperCamelCase						(   lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: str     		=   int(lowercase_ )
  return DynamoBackend(DYNAMO_BACKENDS[value] ).value

def 		UpperCamelCase						(   lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   int(lowercase_ )
  return PrecisionType(["""no""", """fp16""", """bf16""", """fp8"""][value] )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   int(lowercase_ )
  return SageMakerDistributedType(["""NO""", """DATA_PARALLEL""", """MODEL_PARALLEL"""][value] )

def 		UpperCamelCase						(   lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  return {"yes": True, "no": False}[value.lower()]



class    _snake_case (						argparse.RawDescriptionHelpFormatter				):


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: int     		=   super()._format_usage(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   usage.replace("""<command> [<args>] """       ,	"""""")
   return usage






