import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = (PNDMScheduler,)
				lowerCamelCase_    = (('''num_inference_steps''', 5_0),)
				def   lowerCAmelCase_       (						self		,     **lowercase ):



						"""simple docstring"""





						A_		:    int         =				{
						    'num_train_timesteps': 1_0_0_0,
						    'beta_start': 0.0001,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						}

						config.update(**lowercase )
						return config
				def   lowerCAmelCase_       (						self		,     lowercase=0		,     **lowercase ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				dict(self.forward_default_kwargs )
						A_		:    int         =				kwargs.pop('num_inference_steps'		,     lowercase )
						A_		:    int         =				self.dummy_sample
						A_		:    Optional[Any]         =				0.1 * sample
						A_		:    Dict         =				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

						for scheduler_class in self.scheduler_classes:
								A_		:    Union[str, Any]         =				self.get_scheduler_config(**lowercase )
								A_		:    Any         =				scheduler_class(**lowercase )
								scheduler.set_timesteps(lowercase )
								# copy over dummy past residuals
								A_		:    List[Any]         =				dummy_past_residuals[:]

								with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowercase )
										A_		:    Optional[int]         =				scheduler_class.from_pretrained(lowercase )
										new_scheduler.set_timesteps(lowercase )
										# copy over dummy past residuals
										A_		:    str         =				dummy_past_residuals[:]

								A_		:    Tuple         =				scheduler.step_prk(lowercase		,     lowercase		,     lowercase		,     **lowercase ).prev_sample
								A_		:    int         =				new_scheduler.step_prk(lowercase		,     lowercase		,     lowercase		,     **lowercase ).prev_sample

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

								A_		:    Tuple         =				scheduler.step_plms(lowercase		,     lowercase		,     lowercase		,     **lowercase ).prev_sample
								A_		:    Optional[Any]         =				new_scheduler.step_plms(lowercase		,     lowercase		,     lowercase		,     **lowercase ).prev_sample

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



						"""simple docstring"""





						pass
				def   lowerCAmelCase_       (						self		,     lowercase=0		,     **lowercase ):



						"""simple docstring"""





						A_		:    Dict         =				dict(self.forward_default_kwargs )
						A_		:    Optional[Any]         =				kwargs.pop('num_inference_steps'		,     lowercase )
						A_		:    List[str]         =				self.dummy_sample
						A_		:    Union[str, Any]         =				0.1 * sample
						A_		:    Dict         =				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

						for scheduler_class in self.scheduler_classes:
								A_		:    List[Any]         =				self.get_scheduler_config()
								A_		:    Any         =				scheduler_class(**lowercase )
								scheduler.set_timesteps(lowercase )

								# copy over dummy past residuals (must be after setting timesteps)
								A_		:    Union[str, Any]         =				dummy_past_residuals[:]

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

										# copy over dummy past residual (must be after setting timesteps)
										A_		:    Union[str, Any]         =				dummy_past_residuals[:]

								A_		:    Any         =				scheduler.step_prk(lowercase		,     lowercase		,     lowercase		,     **lowercase ).prev_sample
								A_		:    List[Any]         =				new_scheduler.step_prk(lowercase		,     lowercase		,     lowercase		,     **lowercase ).prev_sample

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

								A_		:    List[Any]         =				scheduler.step_plms(lowercase		,     lowercase		,     lowercase		,     **lowercase ).prev_sample
								A_		:    str         =				new_scheduler.step_plms(lowercase		,     lowercase		,     lowercase		,     **lowercase ).prev_sample

								assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
				def   lowerCAmelCase_       (						self		,     **lowercase ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				self.scheduler_classes[0]
						A_		:    Optional[Any]         =				self.get_scheduler_config(**lowercase )
						A_		:    Optional[int]         =				scheduler_class(**lowercase )

						A_		:    str         =				1_0
						A_		:    str         =				self.dummy_model()
						A_		:    Any         =				self.dummy_sample_deter
						scheduler.set_timesteps(lowercase )

						for i, t in enumerate(scheduler.prk_timesteps ):
								A_		:    Optional[Any]         =				model(lowercase		,     lowercase )
								A_		:    Any         =				scheduler.step_prk(lowercase		,     lowercase		,     lowercase ).prev_sample

						for i, t in enumerate(scheduler.plms_timesteps ):
								A_		:    List[str]         =				model(lowercase		,     lowercase )
								A_		:    List[str]         =				scheduler.step_plms(lowercase		,     lowercase		,     lowercase ).prev_sample

						return sample
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Dict         =				dict(self.forward_default_kwargs )

						A_		:    Union[str, Any]         =				kwargs.pop('num_inference_steps'		,     lowercase )

						for scheduler_class in self.scheduler_classes:
								A_		:    Any         =				self.get_scheduler_config()
								A_		:    Any         =				scheduler_class(**lowercase )

								A_		:    Any         =				self.dummy_sample
								A_		:    Dict         =				0.1 * sample

								if num_inference_steps is not None and hasattr(lowercase		,     'set_timesteps' ):
										scheduler.set_timesteps(lowercase )
								elif num_inference_steps is not None and not hasattr(lowercase		,     'set_timesteps' ):
										A_		:    Optional[Any]         =				num_inference_steps

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

								A_		:    int         =				scheduler.step_prk(lowercase		,     0		,     lowercase		,     **lowercase ).prev_sample
								A_		:    str         =				scheduler.step_prk(lowercase		,     1		,     lowercase		,     **lowercase ).prev_sample

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

								A_		:    Tuple         =				scheduler.step_plms(lowercase		,     0		,     lowercase		,     **lowercase ).prev_sample
								A_		:    int         =				scheduler.step_plms(lowercase		,     1		,     lowercase		,     **lowercase ).prev_sample

								self.assertEqual(output_a.shape		,     sample.shape )
								self.assertEqual(output_a.shape		,     output_a.shape )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						for timesteps in [1_0_0, 1_0_0_0]:
								self.check_over_configs(num_train_timesteps=lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						for steps_offset in [0, 1]:
								self.check_over_configs(steps_offset=lowercase )

						A_		:    List[str]         =				self.scheduler_classes[0]
						A_		:    str         =				self.get_scheduler_config(steps_offset=1 )
						A_		:    Dict         =				scheduler_class(**lowercase )
						scheduler.set_timesteps(1_0 )
						assert torch.equal(
						    scheduler.timesteps		,     torch.LongTensor(
						        [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1] )		,     )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





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



						"""simple docstring"""





						for schedule in ["linear", "squaredcos_cap_v2"]:
								self.check_over_configs(beta_schedule=lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





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



						"""simple docstring"""





						for t in [1, 5, 1_0]:
								self.check_over_forward(time_step=lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						for t, num_inference_steps in zip([1, 5, 1_0]		,     [1_0, 5_0, 1_0_0] ):
								self.check_over_forward(num_inference_steps=lowercase )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Dict         =				2_7

						for scheduler_class in self.scheduler_classes:
								A_		:    List[Any]         =				self.dummy_sample
								A_		:    Optional[Any]         =				0.1 * sample

								A_		:    List[str]         =				self.get_scheduler_config()
								A_		:    Union[str, Any]         =				scheduler_class(**lowercase )

								scheduler.set_timesteps(lowercase )

								# 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_		:    List[str]         =				scheduler.step_prk(lowercase		,     lowercase		,     lowercase ).prev_sample
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						with self.assertRaises(lowercase ):
								A_		:    str         =				self.scheduler_classes[0]
								A_		:    Union[str, Any]         =				self.get_scheduler_config()
								A_		:    List[str]         =				scheduler_class(**lowercase )

								scheduler.step_plms(self.dummy_sample		,     1		,     self.dummy_sample ).prev_sample
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Dict         =				self.full_loop()
						A_		:    List[Any]         =				torch.sum(torch.abs(lowercase ) )
						A_		:    Union[str, Any]         =				torch.mean(torch.abs(lowercase ) )

						assert abs(result_sum.item() - 198.1318 ) < 1E-2
						assert abs(result_mean.item() - 0.2580 ) < 1E-3
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				self.full_loop(prediction_type='v_prediction' )
						A_		:    Optional[int]         =				torch.sum(torch.abs(lowercase ) )
						A_		:    int         =				torch.mean(torch.abs(lowercase ) )

						assert abs(result_sum.item() - 67.3986 ) < 1E-2
						assert abs(result_mean.item() - 0.0878 ) < 1E-3
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				self.full_loop(set_alpha_to_one=lowercase		,     beta_start=0.01 )
						A_		:    Tuple         =				torch.sum(torch.abs(lowercase ) )
						A_		:    Union[str, Any]         =				torch.mean(torch.abs(lowercase ) )

						assert abs(result_sum.item() - 230.0399 ) < 1E-2
						assert abs(result_mean.item() - 0.2995 ) < 1E-3







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				self.full_loop(set_alpha_to_one=lowercase		,     beta_start=0.01 )
						A_		:    Union[str, Any]         =				torch.sum(torch.abs(lowercase ) )
						A_		:    Tuple         =				torch.mean(torch.abs(lowercase ) )

						assert abs(result_sum.item() - 186.9482 ) < 1E-2
						assert abs(result_mean.item() - 0.2434 ) < 1E-3







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


_UpperCAmelCase            =		{
    """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            =		"""hopper-medium-v2"""
	_UpperCAmelCase            =		gym.make(env_name)

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

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

			# execute action in environment
			_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase            =		env.step(denorm_actions)
			_UpperCAmelCase            =		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            =		next_observation
	except KeyboardInterrupt:
		pass

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






