



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}''')












'''simple docstring'''
import os

import numpy
import onnx
def 				lowercase__   (   __lowercase						:  Optional[int]				,  __lowercase						:  Union[str, Any]     ) ->      Dict:



       """simple docstring"""






       __UpperCamelCase       =  a.name
       __UpperCamelCase       =  b.name

       __UpperCamelCase       =  ''
       __UpperCamelCase       =  ''

       __UpperCamelCase       =  a == b

       __UpperCamelCase       =  name_a
       __UpperCamelCase       =  name_b

       return res
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  int				,  __lowercase						:  List[Any]     ) ->      Optional[int]:



       """simple docstring"""






       for i, input_name in enumerate(node_proto.input     ):
              if input_name == name:
                     node_proto.input.insert(__lowercase				,  __lowercase     )
                     node_proto.input.pop(i + 1     )

       if node_proto.op_type == "If":
              _graph_replace_input_with(node_proto.attribute[0].g				,  __lowercase				,  __lowercase     )
              _graph_replace_input_with(node_proto.attribute[1].g				,  __lowercase				,  __lowercase     )
       if node_proto.op_type == "Loop":
              _graph_replace_input_with(node_proto.attribute[0].g				,  __lowercase				,  __lowercase     )
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  List[Any]				,  __lowercase						:  Dict     ) ->      int:



       """simple docstring"""






       for n in graph_proto.node:
              _node_replace_input_with(__lowercase				,  __lowercase				,  __lowercase     )
def 				lowercase__   (   __lowercase						:  List[str]				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  str     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  list(model.graph.initializer     )
       __UpperCamelCase       =  list(model_without_ext.graph.initializer     )
       for i, ref_i in ind_to_replace:
              assert inits_with_data[i].name == inits[i].name
              assert inits_with_data[ref_i].name == inits[ref_i].name
              assert i > ref_i

              __UpperCamelCase       =  inits[i].name
              __UpperCamelCase       =  inits[ref_i].name

              model_without_ext.graph.initializer.remove(inits[i]     )

              # for n in model.graph.node:
              _graph_replace_input_with(model_without_ext.graph				,  __lowercase				,  __lowercase     )


def 				lowercase__   (   __lowercase						:  Dict     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  os.path.dirname(__lowercase     )
       __UpperCamelCase       =  os.path.basename(__lowercase     )

       __UpperCamelCase       =  onnx.load(os.path.join(__lowercase				,  __lowercase     )     )

       __UpperCamelCase       =  list(model.graph.initializer     )

       __UpperCamelCase       =  set()
       __UpperCamelCase       =  {}
       __UpperCamelCase       =  []

       __UpperCamelCase       =  0

       for i in range(len(__lowercase     )     ):
              if i in dup_set:
                     continue

              for j in range(i + 1				,  len(__lowercase     )     ):
                     if j in dup_set:
                            continue
                     if _is_equal_tensor_proto(inits[i]				,  inits[j]     ):
                            dup_set.add(__lowercase     )
                            dup_set.add(__lowercase     )

                            __UpperCamelCase       =  inits[j].data_type
                            __UpperCamelCase       =  numpy.prod(inits[j].dims     )
                            if dtype == 1:
                                   mem_size *= 4
                            elif dtype == 6:
                                   mem_size *= 4
                            elif dtype == 7 or dtype == 11:
                                   mem_size *= 8
                            else:
                                   print('unexpected data type: '				,  __lowercase     )
                            total_reduced_size += mem_size

                            __UpperCamelCase       =  inits[i].name
                            __UpperCamelCase       =  inits[j].name

                            if name_i in dup_map:
                                   dup_map[name_i].append(__lowercase     )
                            else:
                                   __UpperCamelCase       =  [name_j]
                            ind_to_replace.append((j, i)     )

       print('total reduced size: '				,  total_reduced_size / 1024 / 1024 / 1024				,  'GB'     )

       __UpperCamelCase       =  sorted(__lowercase     )
       _remove_dup_initializers_from_model(__lowercase				,  __lowercase				,  __lowercase     )

       __UpperCamelCase       =  'optimized_' + model_file_name
       __UpperCamelCase       =  os.path.join(__lowercase				,  __lowercase     )
       onnx.save(__lowercase				,  __lowercase     )

       return new_model
