




"""simple docstring"""

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 				lowercase_       (     __lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase    :     Optional[Any]        =	(PNDMScheduler,)
 UpperCAmelCase    :     str        =	(('''num_inference_steps''', 50),)

 def 			lowerCAmelCase_							( self       :       List[str]    ,							**_UpperCAmelCase       :       str		):
       _A					   =   {
           """num_train_timesteps""": 1_000,
           """beta_start""": 0.0001,
           """beta_end""": 0.02,
           """beta_schedule""": """linear""",
       }

       config.update(**A__		)
       return config

 def 			lowerCAmelCase_							( self       :       Any    ,							_UpperCAmelCase       :       Any=0    ,							**_UpperCAmelCase       :       Any		):
       _A					   =   dict(self.forward_default_kwargs		)
       _A					   =   kwargs.pop('num_inference_steps'    ,							A__		)
       _A					   =   self.dummy_sample
       _A					   =   0.1 * sample
       _A					   =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
             _A					   =   self.get_scheduler_config(**A__		)
             _A					   =   scheduler_class(**A__		)
             scheduler.set_timesteps(A__		)
             # copy over dummy past residuals
             _A					   =   dummy_past_residuals[:]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(A__		)
                   _A					   =   scheduler_class.from_pretrained(A__		)
                   new_scheduler.set_timesteps(A__		)
                   # copy over dummy past residuals
                   _A					   =   dummy_past_residuals[:]

             _A					   =   scheduler.step_prk(A__    ,							A__    ,							A__    ,							**A__		).prev_sample
             _A					   =   new_scheduler.step_prk(A__    ,							A__    ,							A__    ,							**A__		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

             _A					   =   scheduler.step_plms(A__    ,							A__    ,							A__    ,							**A__		).prev_sample
             _A					   =   new_scheduler.step_plms(A__    ,							A__    ,							A__    ,							**A__		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

 def 			lowerCAmelCase_							( self       :       Dict		):
       pass

 def 			lowerCAmelCase_							( self       :       Optional[Any]    ,							_UpperCAmelCase       :       Optional[Any]=0    ,							**_UpperCAmelCase       :       int		):
       _A					   =   dict(self.forward_default_kwargs		)
       _A					   =   kwargs.pop('num_inference_steps'    ,							A__		)
       _A					   =   self.dummy_sample
       _A					   =   0.1 * sample
       _A					   =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
             _A					   =   self.get_scheduler_config()
             _A					   =   scheduler_class(**A__		)
             scheduler.set_timesteps(A__		)

             # copy over dummy past residuals (must be after setting timesteps)
             _A					   =   dummy_past_residuals[:]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(A__		)
                   _A					   =   scheduler_class.from_pretrained(A__		)
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(A__		)

                   # copy over dummy past residual (must be after setting timesteps)
                   _A					   =   dummy_past_residuals[:]

             _A					   =   scheduler.step_prk(A__    ,							A__    ,							A__    ,							**A__		).prev_sample
             _A					   =   new_scheduler.step_prk(A__    ,							A__    ,							A__    ,							**A__		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

             _A					   =   scheduler.step_plms(A__    ,							A__    ,							A__    ,							**A__		).prev_sample
             _A					   =   new_scheduler.step_plms(A__    ,							A__    ,							A__    ,							**A__		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

 def 			lowerCAmelCase_							( self       :       Dict    ,							**_UpperCAmelCase       :       List[Any]		):
       _A					   =   self.scheduler_classes[0]
       _A					   =   self.get_scheduler_config(**A__		)
       _A					   =   scheduler_class(**A__		)

       _A					   =   10
       _A					   =   self.dummy_model()
       _A					   =   self.dummy_sample_deter
       scheduler.set_timesteps(A__		)

       for i, t in enumerate(scheduler.prk_timesteps		):
             _A					   =   model(A__    ,							A__		)
             _A					   =   scheduler.step_prk(A__    ,							A__    ,							A__		).prev_sample

       for i, t in enumerate(scheduler.plms_timesteps		):
             _A					   =   model(A__    ,							A__		)
             _A					   =   scheduler.step_plms(A__    ,							A__    ,							A__		).prev_sample

       return sample

 def 			lowerCAmelCase_							( self       :       Dict		):
       _A					   =   dict(self.forward_default_kwargs		)

       _A					   =   kwargs.pop('num_inference_steps'    ,							A__		)

       for scheduler_class in self.scheduler_classes:
             _A					   =   self.get_scheduler_config()
             _A					   =   scheduler_class(**A__		)

             _A					   =   self.dummy_sample
             _A					   =   0.1 * sample

             if num_inference_steps is not None and hasattr(A__    ,							'set_timesteps'		):
                   scheduler.set_timesteps(A__		)
             elif num_inference_steps is not None and not hasattr(A__    ,							'set_timesteps'		):
                   _A					   =   num_inference_steps

             # copy over dummy past residuals (must be done after set_timesteps)
             _A					   =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
             _A					   =   dummy_past_residuals[:]

             _A					   =   scheduler.step_prk(A__    ,							0    ,							A__    ,							**A__		).prev_sample
             _A					   =   scheduler.step_prk(A__    ,							1    ,							A__    ,							**A__		).prev_sample

             self.assertEqual(output_a.shape    ,							sample.shape		)
             self.assertEqual(output_a.shape    ,							output_a.shape		)

             _A					   =   scheduler.step_plms(A__    ,							0    ,							A__    ,							**A__		).prev_sample
             _A					   =   scheduler.step_plms(A__    ,							1    ,							A__    ,							**A__		).prev_sample

             self.assertEqual(output_a.shape    ,							sample.shape		)
             self.assertEqual(output_a.shape    ,							output_a.shape		)

 def 			lowerCAmelCase_							( self       :       int		):
       for timesteps in [100, 1_000]:
             self.check_over_configs(num_train_timesteps=A__		)

 def 			lowerCAmelCase_							( self       :       List[Any]		):
       for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=A__		)

       _A					   =   self.scheduler_classes[0]
       _A					   =   self.get_scheduler_config(steps_offset=1		)
       _A					   =   scheduler_class(**A__		)
       scheduler.set_timesteps(10		)
       assert torch.equal(
           scheduler.timesteps    ,							torch.LongTensor(
               [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]		)    ,							)

 def 			lowerCAmelCase_							( self       :       Optional[Any]		):
       for beta_start, beta_end in zip([0.0001, 0.001]    ,							[0.002, 0.02]		):
             self.check_over_configs(beta_start=A__    ,							beta_end=A__		)

 def 			lowerCAmelCase_							( self       :       Union[str, Any]		):
       for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=A__		)

 def 			lowerCAmelCase_							( self       :       Tuple		):
       for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=A__		)

 def 			lowerCAmelCase_							( self       :       List[Any]		):
       for t in [1, 5, 10]:
             self.check_over_forward(time_step=A__		)

 def 			lowerCAmelCase_							( self       :       Tuple		):
       for t, num_inference_steps in zip([1, 5, 10]    ,							[10, 50, 100]		):
             self.check_over_forward(num_inference_steps=A__		)

 def 			lowerCAmelCase_							( self       :       Union[str, Any]		):
       # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
       _A					   =   27

       for scheduler_class in self.scheduler_classes:
             _A					   =   self.dummy_sample
             _A					   =   0.1 * sample

             _A					   =   self.get_scheduler_config()
             _A					   =   scheduler_class(**A__		)

             scheduler.set_timesteps(A__		)

             # before power of 3 fix, would error on first step, so we only need to do two
             for i, t in enumerate(scheduler.prk_timesteps[:2]		):
                   _A					   =   scheduler.step_prk(A__    ,							A__    ,							A__		).prev_sample

 def 			lowerCAmelCase_							( self       :       Optional[int]		):
       with self.assertRaises(A__		):
             _A					   =   self.scheduler_classes[0]
             _A					   =   self.get_scheduler_config()
             _A					   =   scheduler_class(**A__		)

             scheduler.step_plms(self.dummy_sample    ,							1    ,							self.dummy_sample		).prev_sample

 def 			lowerCAmelCase_							( self       :       int		):
       _A					   =   self.full_loop()
       _A					   =   torch.sum(torch.abs(A__		)		)
       _A					   =   torch.mean(torch.abs(A__		)		)

       assert abs(result_sum.item() - 198.1_318		) < 1E-2
       assert abs(result_mean.item() - 0.2580		) < 1E-3

 def 			lowerCAmelCase_							( self       :       List[str]		):
       _A					   =   self.full_loop(prediction_type='v_prediction'		)
       _A					   =   torch.sum(torch.abs(A__		)		)
       _A					   =   torch.mean(torch.abs(A__		)		)

       assert abs(result_sum.item() - 67.3_986		) < 1E-2
       assert abs(result_mean.item() - 0.0878		) < 1E-3

 def 			lowerCAmelCase_							( self       :       Optional[Any]		):
       # We specify different beta, so that the first alpha is 0.99
       _A					   =   self.full_loop(set_alpha_to_one=A__    ,							beta_start=0.01		)
       _A					   =   torch.sum(torch.abs(A__		)		)
       _A					   =   torch.mean(torch.abs(A__		)		)

       assert abs(result_sum.item() - 230.0_399		) < 1E-2
       assert abs(result_mean.item() - 0.2995		) < 1E-3



 def 			lowerCAmelCase_							( self       :       Union[str, Any]		):
       # We specify different beta, so that the first alpha is 0.99
       _A					   =   self.full_loop(set_alpha_to_one=A__    ,							beta_start=0.01		)
       _A					   =   torch.sum(torch.abs(A__		)		)
       _A					   =   torch.mean(torch.abs(A__		)		)

       assert abs(result_sum.item() - 186.9_482		) < 1E-2
       assert abs(result_mean.item() - 0.2434		) < 1E-3




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


A_       : List[str]         =			{
    '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_       : List[Any]         =			gym.make(env_name)

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

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

			# execute action in environment
			A_       ,			A_       ,			A_       ,			A_       : Dict         =			env.step(denorm_actions)
			A_       : List[str]         =			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_       : int         =			next_observation
	except KeyboardInterrupt:
		pass

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



