




import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					42
					_lowerCamelCase						    =					None






def    snake_case   (				A		,  A=0.999		,  A="cosine"		,  )				->				Tuple:




	'''simple docstring'''


	if alpha_transform_type == "cosine":

		def alpha_bar_fn(A     ):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(A     ):
			return math.exp(t * -12.0     )

	else:
		raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"     )

	lowerCamelCase_				:						Union[str, Any]				  =				[]
	for i in range(A     ):
		lowerCamelCase_				:						List[Any]				  =				i / num_diffusion_timesteps
		lowerCamelCase_				:						List[str]				  =				(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(A     ) / alpha_bar_fn(A     )		,  A     )     )
	return torch.tensor(A		,  dtype=torch.floataa     )



class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase  ,	lowerCamelCase    ):
					_lowerCamelCase						    =					[e.name for e in KarrasDiffusionSchedulers]
					_lowerCamelCase						    =					1

					@register_to_config
					def __init__(							self					,			__lowercase = 1_000					,			__lowercase = 0.00_01					,			__lowercase = 0.02					,			__lowercase = "linear"					,			__lowercase = None					,			__lowercase = False					,			__lowercase = "epsilon"					,			__lowercase = "linspace"					,			__lowercase = 0					,			):







						'''simple docstring'''




						if trained_betas is not None:
							lowerCamelCase_				:						Any				  =				torch.tensor(__lowercase					,			dtype=torch.floataa   )
						elif beta_schedule == "linear":
							lowerCamelCase_				:						Optional[Any]				  =				torch.linspace(__lowercase					,			__lowercase					,			__lowercase					,			dtype=torch.floataa   )
						elif beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							lowerCamelCase_				:						Dict				  =				(
							    torch.linspace(beta_start**0.5					,			beta_end**0.5					,			__lowercase					,			dtype=torch.floataa   ) ** 2
							)
						elif beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							lowerCamelCase_				:						Dict				  =				betas_for_alpha_bar(__lowercase   )
						else:
							raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"   )

						lowerCamelCase_				:						Tuple				  =				1.0 - self.betas
						lowerCamelCase_				:						int				  =				torch.cumprod(self.alphas					,			dim=0   )

						lowerCamelCase_				:						Dict				  =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
						lowerCamelCase_				:						Any				  =				np.concatenate([sigmas[::-1], [0.0]]   ).astype(np.floataa   )
						lowerCamelCase_				:						List[Any]				  =				torch.from_numpy(__lowercase   )

						# setable values
						lowerCamelCase_				:						Optional[int]				  =				None
						lowerCamelCase_				:						Dict				  =				use_karras_sigmas
						self.set_timesteps(__lowercase					,			__lowercase   )
						lowerCamelCase_				:						List[Any]				  =				[]
						lowerCamelCase_				:						Optional[int]				  =				False

					@property
					def       lowercase_				(							self   ):







						'''simple docstring'''




						if self.config.timestep_spacing in ["linspace", "trailing"]:
							return self.sigmas.max()

						return (self.sigmas.max() ** 2 + 1) ** 0.5

					def       lowercase_				(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						if isinstance(__lowercase					,			torch.Tensor   ):
							lowerCamelCase_				:						Optional[int]				  =				timestep.to(self.timesteps.device   )
						lowerCamelCase_				:						str				  =				(self.timesteps == timestep).nonzero().item()
						lowerCamelCase_				:						int				  =				self.sigmas[step_index]
						lowerCamelCase_				:						Any				  =				sample / ((sigma**2 + 1) ** 0.5)
						lowerCamelCase_				:						Dict				  =				True
						return sample

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						def lms_derivative(__lowercase   ):
							lowerCamelCase_				:						List[str]				  =				1.0
							for k in range(__lowercase   ):
								if current_order == k:
									continue
								prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
							return prod

						lowerCamelCase_				:						Optional[Any]				  =				integrate.quad(__lowercase					,			self.sigmas[t]					,			self.sigmas[t + 1]					,			epsrel=1E-4   )[0]

						return integrated_coeff

					def       lowercase_				(							self					,			__lowercase					,			__lowercase = None   ):







						'''simple docstring'''




						lowerCamelCase_				:						Any				  =				num_inference_steps

						# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
						if self.config.timestep_spacing == "linspace":
							lowerCamelCase_				:						Any				  =				np.linspace(0					,			self.config.num_train_timesteps - 1					,			__lowercase					,			dtype=__lowercase   )[
							    ::-1
							].copy()
						elif self.config.timestep_spacing == "leading":
							lowerCamelCase_				:						List[Any]				  =				self.config.num_train_timesteps // self.num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# casting to int to avoid issues when num_inference_step is power of 3
							lowerCamelCase_				:						Tuple				  =				(np.arange(0					,			__lowercase   ) * step_ratio).round()[::-1].copy().astype(__lowercase   )
							timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
							lowerCamelCase_				:						Union[str, Any]				  =				self.config.num_train_timesteps / self.num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# casting to int to avoid issues when num_inference_step is power of 3
							lowerCamelCase_				:						List[str]				  =				(np.arange(self.config.num_train_timesteps					,			0					,			-step_ratio   )).round().copy().astype(__lowercase   )
							timesteps -= 1
						else:
							raise ValueError(
							    f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."   )

						lowerCamelCase_				:						Optional[int]				  =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
						lowerCamelCase_				:						List[str]				  =				np.log(__lowercase   )
						lowerCamelCase_				:						Optional[Any]				  =				np.interp(__lowercase					,			np.arange(0					,			len(__lowercase   )   )					,			__lowercase   )

						if self.use_karras_sigmas:
							lowerCamelCase_				:						Dict				  =				self._convert_to_karras(in_sigmas=__lowercase   )
							lowerCamelCase_				:						int				  =				np.array([self._sigma_to_t(__lowercase					,			__lowercase   ) for sigma in sigmas]   )

						lowerCamelCase_				:						List[Any]				  =				np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )

						lowerCamelCase_				:						str				  =				torch.from_numpy(__lowercase   ).to(device=__lowercase   )
						if str(__lowercase   ).startswith('mps'   ):
							# mps does not support float64
							lowerCamelCase_				:						Union[str, Any]				  =				torch.from_numpy(__lowercase   ).to(__lowercase					,			dtype=torch.floataa   )
						else:
							lowerCamelCase_				:						Optional[Any]				  =				torch.from_numpy(__lowercase   ).to(device=__lowercase   )

						lowerCamelCase_				:						Tuple				  =				[]

					def       lowercase_				(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						str				  =				np.log(__lowercase   )

						# get distribution
						lowerCamelCase_				:						Union[str, Any]				  =				log_sigma - log_sigmas[:, np.newaxis]

						# get sigmas range
						lowerCamelCase_				:						Dict				  =				np.cumsum((dists >= 0)					,			axis=0   ).argmax(axis=0   ).clip(max=log_sigmas.shape[0] - 2   )
						lowerCamelCase_				:						str				  =				low_idx + 1

						lowerCamelCase_				:						Dict				  =				log_sigmas[low_idx]
						lowerCamelCase_				:						List[Any]				  =				log_sigmas[high_idx]

						# interpolate sigmas
						lowerCamelCase_				:						int				  =				(low - log_sigma) / (low - high)
						lowerCamelCase_				:						Optional[int]				  =				np.clip(__lowercase					,			0					,			1   )

						# transform interpolation to time range
						lowerCamelCase_				:						Tuple				  =				(1 - w) * low_idx + w * high_idx
						lowerCamelCase_				:						Dict				  =				t.reshape(sigma.shape   )
						return t

					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						float				  =				in_sigmas[-1].item()
						lowerCamelCase_				:						float				  =				in_sigmas[0].item()

						lowerCamelCase_				:						List[Any]				  =				7.0  # 7.0 is the value used in the paper
						lowerCamelCase_				:						str				  =				np.linspace(0					,			1					,			self.num_inference_steps   )
						lowerCamelCase_				:						List[str]				  =				sigma_min ** (1 / rho)
						lowerCamelCase_				:						Any				  =				sigma_max ** (1 / rho)
						lowerCamelCase_				:						str				  =				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
						return sigmas

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase = 4					,			__lowercase = True					,			):







						'''simple docstring'''




						if not self.is_scale_input_called:
							warnings.warn(
							    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
							    'See `StableDiffusionPipeline` for a usage example.'   )

						if isinstance(__lowercase					,			torch.Tensor   ):
							lowerCamelCase_				:						List[Any]				  =				timestep.to(self.timesteps.device   )
						lowerCamelCase_				:						Union[str, Any]				  =				(self.timesteps == timestep).nonzero().item()
						lowerCamelCase_				:						Dict				  =				self.sigmas[step_index]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
							lowerCamelCase_				:						int				  =				sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
							# * c_out + input * c_skip
							lowerCamelCase_				:						List[Any]				  =				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
						elif self.config.prediction_type == "sample":
							lowerCamelCase_				:						Tuple				  =				model_output
						else:
							raise ValueError(
							    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"   )

						# 2. Convert to an ODE derivative
						lowerCamelCase_				:						int				  =				(sample - pred_original_sample) / sigma
						self.derivatives.append(__lowercase   )
						if len(self.derivatives   ) > order:
							self.derivatives.pop(0   )

						# 3. Compute linear multistep coefficients
						lowerCamelCase_				:						str				  =				min(step_index + 1					,			__lowercase   )
						lowerCamelCase_				:						int				  =				[self.get_lms_coefficient(__lowercase					,			__lowercase					,			__lowercase   ) for curr_order in range(__lowercase   )]

						# 4. Compute previous sample based on the derivatives path
						lowerCamelCase_				:						Optional[Any]				  =				sample + sum(
						    coeff * derivative for coeff, derivative in zip(__lowercase					,			reversed(self.derivatives   )   )   )

						if not return_dict:
							return (prev_sample,)

						return LMSDiscreteSchedulerOutput(prev_sample=__lowercase					,			pred_original_sample=__lowercase   )

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase					,			):







						'''simple docstring'''




						lowerCamelCase_				:						Any				  =				self.sigmas.to(device=original_samples.device					,			dtype=original_samples.dtype   )
						if original_samples.device.type == "mps" and torch.is_floating_point(__lowercase   ):
							# mps does not support float64
							lowerCamelCase_				:						int				  =				self.timesteps.to(original_samples.device					,			dtype=torch.floataa   )
							lowerCamelCase_				:						List[Any]				  =				timesteps.to(original_samples.device					,			dtype=torch.floataa   )
						else:
							lowerCamelCase_				:						Tuple				  =				self.timesteps.to(original_samples.device   )
							lowerCamelCase_				:						List[str]				  =				timesteps.to(original_samples.device   )

						lowerCamelCase_				:						Union[str, Any]				  =				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

						lowerCamelCase_				:						List[Any]				  =				sigmas[step_indices].flatten()
						while len(sigma.shape   ) < len(original_samples.shape   ):
							lowerCamelCase_				:						Any				  =				sigma.unsqueeze(-1   )

						lowerCamelCase_				:						str				  =				original_samples + noise * sigma
						return noisy_samples





					def __len__(							self   ):







						'''simple docstring'''




						return self.config.num_train_timesteps

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 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	Optional[Any]:






      '''simple docstring'''





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

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

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

      UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase     :			int     	=model(
          states=__lowerCAmelCase		,       actions=__lowerCAmelCase		,       rewards=__lowerCAmelCase		,       returns_to_go=__lowerCAmelCase		,       timesteps=__lowerCAmelCase		,       attention_mask=__lowerCAmelCase		,       return_dict=__lowerCAmelCase		,       )

      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         =     10_00
__snake_case         =     '''cuda'''
__snake_case         =     1000.0  # normalization for rewards/returns
__snake_case         =     36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__snake_case         =     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,
    ]
)
__snake_case         =     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,
    ]
)
__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(10):
     __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
