

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        __SCREAMING_SNAKE_CASE    (      a__						:      List[str] ,a__						:      Optional[Any] ,a__						:      Tuple ,a__						:      List[str] ,a__						:      Tuple ,a__						:      str					)				->							Dict:
	# we don't care about the past rewards in this model

	__A   : Optional[int]								= states.reshape(1 ,-1 ,model.config.state_dim					)
	__A   : List[Any]								= actions.reshape(1 ,-1 ,model.config.act_dim					)
	__A   : Any								= returns_to_go.reshape(1 ,-1 ,1					)
	__A   : Any								= timesteps.reshape(1 ,-1					)

	if model.config.max_length is not None:
		__A   : Optional[Any]								= states[:, -model.config.max_length :]
		__A   : List[str]								= actions[:, -model.config.max_length :]
		__A   : int								= returns_to_go[:, -model.config.max_length :]
		__A   : Any								= timesteps[:, -model.config.max_length :]

		# pad all tokens to sequence length
		__A   : List[Any]								= torch.cat(
		    [torch.zeros(model.config.max_length - states.shape[1]					), torch.ones(states.shape[1]					)]					)
		__A   : Tuple								= attention_mask.to(dtype=torch.long ,device=states.device					).reshape(1 ,-1					)
		__A   : Any								= 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					)
		__A   : Optional[int]								= 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					)
		__A   : Optional[Any]								= 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					)
		__A   : str								= 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:
		__A   : Union[str, Any]								= None

	__A						,							__A						,							__A   : Optional[int]								= model(
	    states=a__ ,actions=a__ ,rewards=a__ ,returns_to_go=a__ ,timesteps=a__ ,attention_mask=a__ ,return_dict=a__ ,)

	return action_preds[0, -1]


# build the environment

UpperCAmelCase_       :				Dict		   =      gym.make('''Hopper-v3''')
UpperCAmelCase_       :				Any		   =      env.observation_space.shape[0]
UpperCAmelCase_       :				Optional[Any]		   =      env.action_space.shape[0]
UpperCAmelCase_       :				List[str]		   =      1_000
UpperCAmelCase_       :				int		   =      '''cuda'''
UpperCAmelCase_       :				Optional[Any]		   =      1000.0  # normalization for rewards/returns
UpperCAmelCase_       :				Optional[int]		   =      3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCAmelCase_       :				Optional[Any]		   =      np.array(
    [
        1.31_1279,
        -0.0846_9521,
        -0.538_2719,
        -0.0720_1576,
        0.0493_2366,
        2.106_6856,
        -0.1501_7354,
        0.0087_8345,
        -0.284_8186,
        -0.1854_0096,
        -0.2846_1286,
    ]
)
UpperCAmelCase_       :				str		   =      np.array(
    [
        0.1779_0751,
        0.0544_4621,
        0.2129_7139,
        0.1453_0419,
        0.612_4444,
        0.8517_4465,
        1.451_5252,
        0.675_1696,
        1.53_6239,
        1.616_0746,
        5.607_2536,
    ]
)
UpperCAmelCase_       :				List[Any]		   =      torch.from_numpy(state_mean).to(device=device)
UpperCAmelCase_       :				Optional[Any]		   =      torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
UpperCAmelCase_       :				int		   =      DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
UpperCAmelCase_       :				Union[str, Any]		   =      model.to(device)
model.eval()

for ep in range(10):
			UpperCAmelCase_   ,		UpperCAmelCase_       :				Optional[int]		   =      0, 0
			UpperCAmelCase_       :				Dict		   =      env.reset()
			UpperCAmelCase_       :				Union[str, Any]		   =      torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
			UpperCAmelCase_       :				List[Any]		   =      torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
			UpperCAmelCase_       :				List[str]		   =      torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
			UpperCAmelCase_       :				Tuple		   =      torch.zeros(0, device=device, dtype=torch.floataa)

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

						UpperCAmelCase_       :				Dict		   =      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),
						)
						UpperCAmelCase_       :				Optional[int]		   =      action
						UpperCAmelCase_       :				Optional[int]		   =      action.detach().cpu().numpy()

						UpperCAmelCase_   ,		UpperCAmelCase_   ,		UpperCAmelCase_   ,		UpperCAmelCase_       :				Tuple		   =      env.step(action)

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

						UpperCAmelCase_       :				Optional[int]		   =      target_return[0, -1] - (reward / scale)
						UpperCAmelCase_       :				Union[str, Any]		   =      torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
						UpperCAmelCase_       :				Optional[Any]		   =      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








# 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     lowerCamelCase_			:
	_lowercase		:      CommonSchedulerState
	_lowercase		:      jnp.ndarray

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




	@classmethod
	def 					lowerCAmelCase_					(    cls	:    int				,			__A	:    CommonSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
		return cls(
		    common=__A				,			final_alpha_cumprod=__A				,			init_noise_sigma=__A				,			timesteps=__A				,			)






@dataclass
class     lowerCamelCase_			(  _lowercase  ):
	_lowercase		:      DDIMSchedulerState






class     lowerCamelCase_			(  _lowercase	,			_lowercase  ):
	_lowercase		:      Union[str, Any]    				=							[e.name for e in FlaxKarrasDiffusionSchedulers]

	_lowercase		:      jnp.dtype




	@property
	def 					lowerCAmelCase_					(    self	:    Tuple       ):
		return True




	@register_to_config
	def __init__(    self	:    Any				,			__A	:    int = 1000				,			__A	:    float = 0.0_0_0_1				,			__A	:    float = 0.0_2				,			__A	:    str = "linear"				,			__A	:    Optional[jnp.ndarray] = None				,			__A	:    bool = True				,			__A	:    int = 0				,			__A	:    str = "epsilon"				,			__A	:    jnp.dtype = jnp.floataa				,			):
		__A   : Optional[int]								= dtype




	def 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    Optional[CommonSchedulerState] = None       ):
		if common is None:
			__A   : Any								= 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.
		__A   : Tuple								= (
		    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
		__A   : List[str]								= jnp.array(1.0				,			dtype=self.dtype       )

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

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




	def 					lowerCAmelCase_					(    self	:    Union[str, Any]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    Optional[int] = None       ):
		return sample




	def 					lowerCAmelCase_					(    self	:    Tuple				,			__A	:    DDIMSchedulerState				,			__A	:    int				,			__A	:    Tuple = ()       ):
		__A   : Union[str, Any]								= 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
		__A   : Optional[Any]								= (jnp.arange(0				,			__A       ) * step_ratio).round()[::-1] + self.config.steps_offset

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




	def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    DDIMSchedulerState				,			__A	:    str				,			__A	:    List[Any]       ):
		__A   : Tuple								= state.common.alphas_cumprod[timestep]
		__A   : Tuple								= jnp.where(
		    prev_timestep >= 0				,			state.common.alphas_cumprod[prev_timestep]				,			state.final_alpha_cumprod       )
		__A   : List[Any]								= 1 - alpha_prod_t
		__A   : Tuple								= 1 - alpha_prod_t_prev

		__A   : List[Any]								= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

		return variance




	def 					lowerCAmelCase_					(    self	:    List[Any]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    int				,			__A	:    jnp.ndarray				,			__A	:    float = 0.0				,			__A	:    bool = True				,			):
		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)
		__A   : Optional[Any]								= timestep - self.config.num_train_timesteps // state.num_inference_steps

		__A   : Optional[int]								= state.common.alphas_cumprod
		__A   : Optional[int]								= state.final_alpha_cumprod

		# 2. compute alphas, betas
		__A   : Optional[int]								= alphas_cumprod[timestep]
		__A   : str								= jnp.where(prev_timestep >= 0				,			alphas_cumprod[prev_timestep]				,			__A       )

		__A   : Any								= 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":
			__A   : Any								= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
			__A   : List[Any]								= model_output
		elif self.config.prediction_type == "sample":
			__A   : Dict								= model_output
			__A   : Tuple								= (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
		elif self.config.prediction_type == "v_prediction":
			__A   : int								= (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
			__A   : str								= (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)
		__A   : Union[str, Any]								= self._get_variance(__A				,			__A				,			__A       )
		__A   : Any								= eta * variance ** 0.5

		# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
		__A   : Any								= (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
		__A   : Optional[int]								= alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

		if not return_dict:
			return (prev_sample, state)

		return FlaxDDIMSchedulerOutput(prev_sample=__A				,			state=__A       )




	def 					lowerCAmelCase_					(    self	:    List[str]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
		return add_noise_common(state.common				,			__A				,			__A				,			__A       )




	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    DDIMSchedulerState				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			__A	:    jnp.ndarray				,			):
		return get_velocity_common(state.common				,			__A				,			__A				,			__A       )




	def __len__(    self	:    Union[str, Any]       ):
		return self.config.num_train_timesteps





