
"""simple docstring"""




# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)




@flax.struct.dataclass
class 					UpperCamelCase     :

				"""simple docstring"""



				UpperCAmelCase_    :				CommonSchedulerState
				UpperCAmelCase_    :				jnp.ndarray

				# setable values
				UpperCAmelCase_    :				jnp.ndarray
				UpperCAmelCase_    :				jnp.ndarray
				UpperCAmelCase_    :				Optional[int]						  =    None


				@classmethod
				def 							A (    cls		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     )   ->   List[str]:
									"""simple docstring"""







									return cls(
									    common=lowercase__		,     final_alpha_cumprod=lowercase__		,     init_noise_sigma=lowercase__		,     timesteps=lowercase__		,     )






@dataclass
class 					UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				DDIMSchedulerState






class 					UpperCamelCase     (	__magic_name__  ,				__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				Optional[int]						  =    [e.name for e in FlaxKarrasDiffusionSchedulers]

				UpperCAmelCase_    :				jnp.dtype


				@property
				def 							A (    self      )   ->   str:
									"""simple docstring"""







									return True


				@register_to_config
				def __init__(    self		,     lowercase__ = 1000		,     lowercase__ = 0.0_001		,     lowercase__ = 0.02		,     lowercase__ = "linear"		,     lowercase__ = None		,     lowercase__ = True		,     lowercase__ = 0		,     lowercase__ = "epsilon"		,     lowercase__ = jnp.floataa		,     )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     dtype


				def 							A (    self		,     lowercase__ = None      )   ->   DDIMSchedulerState:
									"""simple docstring"""







									if common is None:
														SCREAMING_SNAKE_CASE           =     CommonSchedulerState.create(self      )

									# At every step in ddim, we are looking into the previous alphas_cumprod
									# For the final step, there is no previous alphas_cumprod because we are already at 0
									# `set_alpha_to_one` decides whether we set this parameter simply to one or
									# whether we use the final alpha of the "non-previous" one.
									SCREAMING_SNAKE_CASE           =     (
									    jnp.array(1.0		,     dtype=self.dtype      ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
									)

									# standard deviation of the initial noise distribution
									SCREAMING_SNAKE_CASE           =     jnp.array(1.0		,     dtype=self.dtype      )

									SCREAMING_SNAKE_CASE           =     jnp.arange(0		,     self.config.num_train_timesteps      ).round()[::-1]

									return DDIMSchedulerState.create(
									    common=lowercase__		,     final_alpha_cumprod=lowercase__		,     init_noise_sigma=lowercase__		,     timesteps=lowercase__		,     )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__ = None      )   ->   jnp.ndarray:
									"""simple docstring"""







									return sample


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__ = ()      )   ->   DDIMSchedulerState:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self.config.num_train_timesteps // num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# rounding to avoid issues when num_inference_step is power of 3
									SCREAMING_SNAKE_CASE           =     (jnp.arange(0		,     lowercase__      ) * step_ratio).round()[::-1] + self.config.steps_offset

									return state.replace(
									    num_inference_steps=lowercase__		,     timesteps=lowercase__		,     )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     state.common.alphas_cumprod[timestep]
									SCREAMING_SNAKE_CASE           =     jnp.where(
									    prev_timestep >= 0		,     state.common.alphas_cumprod[prev_timestep]		,     state.final_alpha_cumprod      )
									SCREAMING_SNAKE_CASE           =     1 - alpha_prod_t
									SCREAMING_SNAKE_CASE           =     1 - alpha_prod_t_prev

									SCREAMING_SNAKE_CASE           =     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

									return variance


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__ = 0.0		,     lowercase__ = True		,     )   ->   Union[FlaxDDIMSchedulerOutput, Tuple]:
									"""simple docstring"""







									if state.num_inference_steps is None:
														raise ValueError(
														    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'      )

									# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
									# Ideally, read DDIM paper in-detail understanding

									# Notation (<variable name> -> <name in paper>
									# - pred_noise_t -> e_theta(x_t, t)
									# - pred_original_sample -> f_theta(x_t, t) or x_0
									# - std_dev_t -> sigma_t
									# - eta -> η
									# - pred_sample_direction -> "direction pointing to x_t"
									# - pred_prev_sample -> "x_t-1"

									# 1. get previous step value (=t-1)
									SCREAMING_SNAKE_CASE           =     timestep - self.config.num_train_timesteps // state.num_inference_steps

									SCREAMING_SNAKE_CASE           =     state.common.alphas_cumprod
									SCREAMING_SNAKE_CASE           =     state.final_alpha_cumprod

									# 2. compute alphas, betas
									SCREAMING_SNAKE_CASE           =     alphas_cumprod[timestep]
									SCREAMING_SNAKE_CASE           =     jnp.where(prev_timestep >= 0		,     alphas_cumprod[prev_timestep]		,     lowercase__      )

									SCREAMING_SNAKE_CASE           =     1 - alpha_prod_t

									# 3. compute predicted original sample from predicted noise also called
									# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
									if self.config.prediction_type == "epsilon":
														SCREAMING_SNAKE_CASE           =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
														SCREAMING_SNAKE_CASE           =     model_output
									elif self.config.prediction_type == "sample":
														SCREAMING_SNAKE_CASE           =     model_output
														SCREAMING_SNAKE_CASE           =     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
									elif self.config.prediction_type == "v_prediction":
														SCREAMING_SNAKE_CASE           =     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
														SCREAMING_SNAKE_CASE           =     (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
									else:
														raise ValueError(
														    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
														    ' `v_prediction`'      )

									# 4. compute variance: "sigma_t(η)" -> see formula (16)
									# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
									SCREAMING_SNAKE_CASE           =     self._get_variance(lowercase__		,     lowercase__		,     lowercase__      )
									SCREAMING_SNAKE_CASE           =     eta * variance ** 0.5

									# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
									SCREAMING_SNAKE_CASE           =     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

									# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
									SCREAMING_SNAKE_CASE           =     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

									if not return_dict:
														return (prev_sample, state)

									return FlaxDDIMSchedulerOutput(prev_sample=lowercase__		,     state=lowercase__      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     )   ->   jnp.ndarray:
									"""simple docstring"""







									return add_noise_common(state.common		,     lowercase__		,     lowercase__		,     lowercase__      )


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     )   ->   jnp.ndarray:
									"""simple docstring"""







									return get_velocity_common(state.common		,     lowercase__		,     lowercase__		,     lowercase__      )




				def __len__(    self      )   ->   int:
									"""simple docstring"""







									return self.config.num_train_timesteps


"""simple docstring"""




import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					# we don't care about the past rewards in this model

					SCREAMING_SNAKE_CASE           =     states.reshape(1,					-1,					model.config.state_dim      )
					SCREAMING_SNAKE_CASE           =     actions.reshape(1,					-1,					model.config.act_dim      )
					SCREAMING_SNAKE_CASE           =     returns_to_go.reshape(1,					-1,					1      )
					SCREAMING_SNAKE_CASE           =     timesteps.reshape(1,					-1      )

					if model.config.max_length is not None:
										SCREAMING_SNAKE_CASE           =     states[:, -model.config.max_length :]
										SCREAMING_SNAKE_CASE           =     actions[:, -model.config.max_length :]
										SCREAMING_SNAKE_CASE           =     returns_to_go[:, -model.config.max_length :]
										SCREAMING_SNAKE_CASE           =     timesteps[:, -model.config.max_length :]

										# pad all tokens to sequence length
										SCREAMING_SNAKE_CASE           =     torch.cat(
										    [torch.zeros(model.config.max_length - states.shape[1]      ), torch.ones(states.shape[1]      )]      )
										SCREAMING_SNAKE_CASE           =     attention_mask.to(dtype=torch.long,					device=states.device      ).reshape(1,					-1      )
										SCREAMING_SNAKE_CASE           =     torch.cat(
										    [
										        torch.zeros(
										            (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim),					device=states.device,					),
										        states,
										    ],					dim=1,					).to(dtype=torch.floataa      )
										SCREAMING_SNAKE_CASE           =     torch.cat(
										    [
										        torch.zeros(
										            (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim),					device=actions.device,					),
										        actions,
										    ],					dim=1,					).to(dtype=torch.floataa      )
										SCREAMING_SNAKE_CASE           =     torch.cat(
										    [
										        torch.zeros(
										            (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1),					device=returns_to_go.device,					),
										        returns_to_go,
										    ],					dim=1,					).to(dtype=torch.floataa      )
										SCREAMING_SNAKE_CASE           =     torch.cat(
										    [
										        torch.zeros(
										            (timesteps.shape[0], model.config.max_length - timesteps.shape[1]),					device=timesteps.device      ),
										        timesteps,
										    ],					dim=1,					).to(dtype=torch.long      )
					else:
										SCREAMING_SNAKE_CASE           =     None

					SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     model(
					    states=SCREAMING_SNAKE_CASE_,					actions=SCREAMING_SNAKE_CASE_,					rewards=SCREAMING_SNAKE_CASE_,					returns_to_go=SCREAMING_SNAKE_CASE_,					timesteps=SCREAMING_SNAKE_CASE_,					attention_mask=SCREAMING_SNAKE_CASE_,					return_dict=SCREAMING_SNAKE_CASE_,					)

					return action_preds[0, -1]


# build the environment

snake_case			       =		gym.make('Hopper-v3')
snake_case			       =		env.observation_space.shape[0]
snake_case			       =		env.action_space.shape[0]
snake_case			       =		1_0_0_0
snake_case			       =		'cuda'
snake_case			       =		1_0_0_0.0  # normalization for rewards/returns
snake_case			       =		3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
snake_case			       =		np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
snake_case			       =		np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
snake_case			       =		torch.from_numpy(state_mean).to(device=device)
snake_case			       =		torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
snake_case			       =		DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
snake_case			       =		model.to(device)
model.eval()

for ep in range(1_0):
	snake_case       ,      snake_case			       =		0, 0
	snake_case			       =		env.reset()
	snake_case			       =		torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
	snake_case			       =		torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
	snake_case			       =		torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
	snake_case			       =		torch.zeros(0, device=device, dtype=torch.floataa)

	snake_case			       =		torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
	for t in range(max_ep_len):
		env.render()
		# add padding
		snake_case			       =		torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
		snake_case			       =		torch.cat([rewards, torch.zeros(1, device=device)])

		snake_case			       =		get_action(
		    model,
		    (states.to(dtype=torch.floataa) - state_mean) / state_std,
		    actions.to(dtype=torch.floataa),
		    rewards.to(dtype=torch.floataa),
		    target_return.to(dtype=torch.floataa),
		    timesteps.to(dtype=torch.long),
		)
		snake_case			       =		action
		snake_case			       =		action.detach().cpu().numpy()

		snake_case       ,      snake_case       ,      snake_case       ,      snake_case			       =		env.step(action)

		snake_case			       =		torch.from_numpy(state).to(device=device).reshape(1, state_dim)
		snake_case			       =		torch.cat([states, cur_state], dim=0)
		snake_case			       =		reward

		snake_case			       =		target_return[0, -1] - (reward / scale)
		snake_case			       =		torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
		snake_case			       =		torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

		episode_return += reward
		episode_length += 1

		if done:
			break
