
"""simple docstring"""







import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


_SCREAMING_SNAKE_CASE						       =      logging.get_logger(__name__)







class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   'linear'
					_SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   'cosine'
					_SCREAMING_SNAKE_CASE       :					List[Any]     							=   'cosine_with_restarts'
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   'polynomial'
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   'constant'
					_SCREAMING_SNAKE_CASE       :					Optional[Any]     							=   'constant_with_warmup'
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   'piecewise_constant'




def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = -1 )  ->     Union[str, Any]:







							"""simple docstring"""


							return LambdaLR(SCREAMING_SNAKE_CASE							,    lambda SCREAMING_SNAKE_CASE : 1							,    last_epoch=SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = -1 )  ->     int:







							"""simple docstring"""


							def lr_lambda(SCREAMING_SNAKE_CASE ):
														if current_step < num_warmup_steps:
																					return float(SCREAMING_SNAKE_CASE ) / float(max(1.0							,    SCREAMING_SNAKE_CASE ) )
														return 1.0

							return LambdaLR(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    last_epoch=SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = -1 )  ->     Optional[int]:







							"""simple docstring"""


							__snake_case     			=       {}
							__snake_case     			=       step_rules.split("," )
							for rule_str in rule_list[:-1]:
														__snake_case			,   __snake_case     			=       rule_str.split(":" )
														__snake_case     			=       int(SCREAMING_SNAKE_CASE )
														__snake_case     			=       float(SCREAMING_SNAKE_CASE )
														__snake_case     			=       value
							__snake_case     			=       float(rule_list[-1] )

							def create_rules_function(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ):
														def rule_func(SCREAMING_SNAKE_CASE ) -> float:
																					__snake_case     			=       sorted(rules_dict.keys() )
																					for i, sorted_step in enumerate(SCREAMING_SNAKE_CASE ):
																												if steps < sorted_step:
																																			return rules_dict[sorted_steps[i]]
																					return last_lr_multiple

														return rule_func

							__snake_case     			=       create_rules_function(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

							return LambdaLR(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    last_epoch=SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=-1 )  ->     Optional[Any]:







							"""simple docstring"""


							def lr_lambda(SCREAMING_SNAKE_CASE ):
														if current_step < num_warmup_steps:
																					return float(SCREAMING_SNAKE_CASE ) / float(max(1							,    SCREAMING_SNAKE_CASE ) )
														return max(
														    0.0							,    float(num_training_steps - current_step ) / float(max(1							,    num_training_steps - num_warmup_steps ) ) )

							return LambdaLR(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = 0.5							,    SCREAMING_SNAKE_CASE = -1 )  ->     int:







							"""simple docstring"""


							def lr_lambda(SCREAMING_SNAKE_CASE ):
														if current_step < num_warmup_steps:
																					return float(SCREAMING_SNAKE_CASE ) / float(max(1							,    SCREAMING_SNAKE_CASE ) )
														__snake_case     			=       float(current_step - num_warmup_steps ) / float(max(1							,    num_training_steps - num_warmup_steps ) )
														return max(0.0							,    0.5 * (1.0 + math.cos(math.pi * float(SCREAMING_SNAKE_CASE ) * 2.0 * progress )) )

							return LambdaLR(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = 1							,    SCREAMING_SNAKE_CASE = -1 )  ->     Any:







							"""simple docstring"""


							def lr_lambda(SCREAMING_SNAKE_CASE ):
														if current_step < num_warmup_steps:
																					return float(SCREAMING_SNAKE_CASE ) / float(max(1							,    SCREAMING_SNAKE_CASE ) )
														__snake_case     			=       float(current_step - num_warmup_steps ) / float(max(1							,    num_training_steps - num_warmup_steps ) )
														if progress >= 1.0:
																					return 0.0
														return max(0.0							,    0.5 * (1.0 + math.cos(math.pi * ((float(SCREAMING_SNAKE_CASE ) * progress) % 1.0) )) )

							return LambdaLR(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=1e-7							,    SCREAMING_SNAKE_CASE=1.0							,    SCREAMING_SNAKE_CASE=-1 )  ->     List[Any]:







							"""simple docstring"""


							__snake_case     			=       optimizer.defaults["lr"]
							if not (lr_init > lr_end):
														raise ValueError(F'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})''' )

							def lr_lambda(SCREAMING_SNAKE_CASE ):
														if current_step < num_warmup_steps:
																					return float(SCREAMING_SNAKE_CASE ) / float(max(1							,    SCREAMING_SNAKE_CASE ) )
														elif current_step > num_training_steps:
																					return lr_end / lr_init  # as LambdaLR multiplies by lr_init
														else:
																					__snake_case     			=       lr_init - lr_end
																					__snake_case     			=       num_training_steps - num_warmup_steps
																					__snake_case     			=       1 - (current_step - num_warmup_steps) / decay_steps
																					__snake_case     			=       lr_range * pct_remaining**power + lr_end
																					return decay / lr_init  # as LambdaLR multiplies by lr_init

							return LambdaLR(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )


_SCREAMING_SNAKE_CASE						       =      {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}



def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = None							,    SCREAMING_SNAKE_CASE = None							,    SCREAMING_SNAKE_CASE = None							,    SCREAMING_SNAKE_CASE = 1							,    SCREAMING_SNAKE_CASE = 1.0							,    SCREAMING_SNAKE_CASE = -1							,    )  ->     Tuple:







							"""simple docstring"""


							__snake_case     			=       SchedulerType(SCREAMING_SNAKE_CASE )
							__snake_case     			=       TYPE_TO_SCHEDULER_FUNCTION[name]
							if name == SchedulerType.CONSTANT:
														return schedule_func(SCREAMING_SNAKE_CASE							,    last_epoch=SCREAMING_SNAKE_CASE )

							if name == SchedulerType.PIECEWISE_CONSTANT:
														return schedule_func(SCREAMING_SNAKE_CASE							,    step_rules=SCREAMING_SNAKE_CASE							,    last_epoch=SCREAMING_SNAKE_CASE )

							# All other schedulers require `num_warmup_steps`
							if num_warmup_steps is None:
														raise ValueError(F'''{name} requires `num_warmup_steps`, please provide that argument.''' )

							if name == SchedulerType.CONSTANT_WITH_WARMUP:
														return schedule_func(SCREAMING_SNAKE_CASE							,    num_warmup_steps=SCREAMING_SNAKE_CASE							,    last_epoch=SCREAMING_SNAKE_CASE )

							# All other schedulers require `num_training_steps`
							if num_training_steps is None:
														raise ValueError(F'''{name} requires `num_training_steps`, please provide that argument.''' )

							if name == SchedulerType.COSINE_WITH_RESTARTS:
														return schedule_func(
														    SCREAMING_SNAKE_CASE							,    num_warmup_steps=SCREAMING_SNAKE_CASE							,    num_training_steps=SCREAMING_SNAKE_CASE							,    num_cycles=SCREAMING_SNAKE_CASE							,    last_epoch=SCREAMING_SNAKE_CASE							,    )

							if name == SchedulerType.POLYNOMIAL:
														return schedule_func(
														    SCREAMING_SNAKE_CASE							,    num_warmup_steps=SCREAMING_SNAKE_CASE							,    num_training_steps=SCREAMING_SNAKE_CASE							,    power=SCREAMING_SNAKE_CASE							,    last_epoch=SCREAMING_SNAKE_CASE							,    )

							return schedule_func(
							    SCREAMING_SNAKE_CASE							,    num_warmup_steps=SCREAMING_SNAKE_CASE							,    num_training_steps=SCREAMING_SNAKE_CASE							,    last_epoch=SCREAMING_SNAKE_CASE )


"""simple docstring"""







import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler







class 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							Any							,						snake_case_   :							UNetaDModel							,						snake_case_   :							UNetaDModel							,						snake_case_   :							DDPMScheduler							,						snake_case_   :							Any							,						):
												super().__init__()
												__snake_case     			=       value_function
												__snake_case     			=       unet
												__snake_case     			=       scheduler
												__snake_case     			=       env
												__snake_case     			=       env.get_dataset()
												__snake_case     			=       {}
												for key in self.data.keys():
																			try:
																										__snake_case     			=       self.data[key].mean()
																			except:  # noqa: E722
																										pass
												__snake_case     			=       {}
												for key in self.data.keys():
																			try:
																										__snake_case     			=       self.data[key].std()
																			except:  # noqa: E722
																										pass
												__snake_case     			=       env.observation_space.shape[0]
												__snake_case     			=       env.action_space.shape[0]





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							str							,						snake_case_   :							Any    ):
												return (x_in - self.means[key]) / self.stds[key]





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Optional[Any]    ):
												return x_in * self.stds[key] + self.means[key]





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							Dict    ):
												if type(snake_case_    ) is dict:
																			return {k: self.to_torch(snake_case_    ) for k, v in x_in.items()}
												elif torch.is_tensor(snake_case_    ):
																			return x_in.to(self.unet.device    )
												return torch.tensor(snake_case_							,						device=self.unet.device    )





					def 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							Tuple							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Union[str, Any]    ):
												for key, val in cond.items():
																			__snake_case     			=       val.clone()
												return x_in





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Tuple							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Any							,						snake_case_   :							Optional[Any]    ):
												__snake_case     			=       x.shape[0]
												__snake_case     			=       None
												for i in tqdm.tqdm(self.scheduler.timesteps    ):
																			# create batch of timesteps to pass into model
																			__snake_case     			=       torch.full((batch_size,)							,						snake_case_							,						device=self.unet.device							,						dtype=torch.long    )
																			for _ in range(snake_case_    ):
																										with torch.enable_grad():
																																	x.requires_grad_()

																																	# permute to match dimension for pre-trained models
																																	__snake_case     			=       self.value_function(x.permute(0							,						2							,						1    )							,						snake_case_    ).sample
																																	__snake_case     			=       torch.autograd.grad([y.sum()]							,						[x]    )[0]

																																	__snake_case     			=       self.scheduler._get_variance(snake_case_    )
																																	__snake_case     			=       torch.exp(0.5 * posterior_variance    )
																																	__snake_case     			=       model_std * grad

																										__snake_case     			=       0
																										__snake_case     			=       x.detach()
																										__snake_case     			=       x + scale * grad
																										__snake_case     			=       self.reset_xa(snake_case_							,						snake_case_							,						self.action_dim    )

																			__snake_case     			=       self.unet(x.permute(0							,						2							,						1    )							,						snake_case_    ).sample.permute(0							,						2							,						1    )

																			# TODO: verify deprecation of this kwarg
																			__snake_case     			=       self.scheduler.step(snake_case_							,						snake_case_							,						snake_case_							,						predict_epsilon=snake_case_    )["prev_sample"]

																			# apply conditions to the trajectory (set the initial state)
																			__snake_case     			=       self.reset_xa(snake_case_							,						snake_case_							,						self.action_dim    )
																			__snake_case     			=       self.to_torch(snake_case_    )
												return x, y





					def __call__(				self   :							Union[str, Any]							,						snake_case_   :							List[Any]							,						snake_case_   :							Any=64							,						snake_case_   :							str=32							,						snake_case_   :							List[str]=2							,						snake_case_   :							Tuple=0.1    ):
												# normalize the observations and create  batch dimension
												__snake_case     			=       self.normalize(snake_case_							,						"observations"    )
												__snake_case     			=       obs[None].repeat(snake_case_							,						axis=0    )

												__snake_case     			=       {0: self.to_torch(snake_case_    )}
												__snake_case     			=       (batch_size, planning_horizon, self.state_dim + self.action_dim)

												# generate initial noise and apply our conditions (to make the trajectories start at current state)
												__snake_case     			=       randn_tensor(snake_case_							,						device=self.unet.device    )
												__snake_case     			=       self.reset_xa(snake_case_							,						snake_case_							,						self.action_dim    )
												__snake_case     			=       self.to_torch(snake_case_    )

												# run the diffusion process
												__snake_case			,   __snake_case     			=       self.run_diffusion(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )

												# sort output trajectories by value
												__snake_case     			=       y.argsort(0							,						descending=snake_case_    ).squeeze()
												__snake_case     			=       x[sorted_idx]
												__snake_case     			=       sorted_values[:, :, : self.action_dim]
												__snake_case     			=       actions.detach().cpu().numpy()
												__snake_case     			=       self.de_normalize(snake_case_							,						key="actions"    )

												# select the action with the highest value
												if y is not None:
																			__snake_case     			=       0
												else:
																			# if we didn't run value guiding, select a random action
																			__snake_case     			=       np.random.randint(0							,						snake_case_    )

												__snake_case     			=       denorm_actions[selected_index, 0]
												return denorm_actions
