'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


a=	logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
class       __lowercase		(    _lowerCamelCase    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    42






class       __lowercase		(    _lowerCamelCase      ,				_lowerCamelCase    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    1







     @register_to_config
     def __init__(		self  ,      _lowerCamelCase = 4_0  ,      _lowerCamelCase = 0.0_0_2  ,      _lowerCamelCase = 8_0.0  ,      _lowerCamelCase = 0.5  ,      _lowerCamelCase = 1.0  ,      _lowerCamelCase = 7.0  ,      _lowerCamelCase = True  ,      ):
         # standard deviation of the initial noise distribution
         __UpperCamelCase						:						Dict		     =	sigma_max

         __UpperCamelCase						:						List[str]		     =	np.linspace(0  ,      1  ,      _lowerCamelCase     )
         __UpperCamelCase						:						Optional[int]		     =	self._convert_to_karras(_lowerCamelCase     )
         __UpperCamelCase						:						Dict		     =	self.sigma_to_t(_lowerCamelCase     )

         # setable values
         __UpperCamelCase						:						List[str]		     =	None
         __UpperCamelCase						:						List[Any]		     =	torch.from_numpy(_lowerCamelCase     )
         __UpperCamelCase						:						Any		     =	torch.from_numpy(_lowerCamelCase     )
         __UpperCamelCase						:						List[str]		     =	False
         __UpperCamelCase						:						str		     =	False







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase=None     ):
         if schedule_timesteps is None:
             __UpperCamelCase						:						int		     =	self.timesteps

         __UpperCamelCase						:						Optional[Any]		     =	(schedule_timesteps == timestep).nonzero()
         return indices.item()







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         # Get sigma corresponding to timestep
         if isinstance(_lowerCamelCase  ,      torch.Tensor     ):
             __UpperCamelCase						:						Optional[Any]		     =	timestep.to(self.timesteps.device     )
         __UpperCamelCase						:						Union[str, Any]		     =	self.index_for_timestep(_lowerCamelCase     )
         __UpperCamelCase						:						Any		     =	self.sigmas[step_idx]

         __UpperCamelCase						:						int		     =	sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

         __UpperCamelCase						:						List[Any]		     =	True
         return sample







     def lowerCAmelCase  (		self  ,      _lowerCamelCase     ):
         if not isinstance(_lowerCamelCase  ,      np.ndarray     ):
             __UpperCamelCase						:						str		     =	np.array(_lowerCamelCase  ,      dtype=np.floataa     )

         __UpperCamelCase						:						Dict		     =	1_0_0_0 * 0.2_5 * np.log(sigmas + 1E-4_4     )

         return timesteps







     def lowerCAmelCase  (		self  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      ):
         if num_inference_steps is None and timesteps is None:
             raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'     )

         if num_inference_steps is not None and timesteps is not None:
             raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.'     )

         # Follow DDPMScheduler custom timesteps logic
         if timesteps is not None:
             for i in range(1  ,      len(_lowerCamelCase     )     ):
                 if timesteps[i] >= timesteps[i - 1]:
                     raise ValueError('`timesteps` must be in descending order.'     )

             if timesteps[0] >= self.config.num_train_timesteps:
                 raise ValueError(
                     f"""`timesteps` must start before `self.config.train_timesteps`:"""
                     f""" {self.config.num_train_timesteps}."""     )

             __UpperCamelCase						:						str		     =	np.array(_lowerCamelCase  ,      dtype=np.intaa     )
             __UpperCamelCase						:						Tuple		     =	True
         else:
             if num_inference_steps > self.config.num_train_timesteps:
                 raise ValueError(
                     f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
                     f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
                     f""" maximal {self.config.num_train_timesteps} timesteps."""     )

             __UpperCamelCase						:						List[str]		     =	num_inference_steps

             __UpperCamelCase						:						List[Any]		     =	self.config.num_train_timesteps // self.num_inference_steps
             __UpperCamelCase						:						List[Any]		     =	(np.arange(0  ,      _lowerCamelCase     ) * step_ratio).round()[::-1].copy().astype(np.intaa     )
             __UpperCamelCase						:						str		     =	False

         # Map timesteps to Karras sigmas directly for multistep sampling
         # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
         __UpperCamelCase						:						List[Any]		     =	self.config.num_train_timesteps
         __UpperCamelCase						:						Union[str, Any]		     =	timesteps[::-1].copy()
         __UpperCamelCase						:						Dict		     =	ramp / (num_train_timesteps - 1)
         __UpperCamelCase						:						Optional[int]		     =	self._convert_to_karras(_lowerCamelCase     )
         __UpperCamelCase						:						List[str]		     =	self.sigma_to_t(_lowerCamelCase     )

         __UpperCamelCase						:						Any		     =	np.concatenate([sigmas, [self.sigma_min]]     ).astype(np.floataa     )
         __UpperCamelCase						:						Optional[int]		     =	torch.from_numpy(_lowerCamelCase     ).to(device=_lowerCamelCase     )

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







     def lowerCAmelCase  (		self  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						float		     =	self.config.sigma_min
         __UpperCamelCase						:						float		     =	self.config.sigma_max

         __UpperCamelCase						:						List[str]		     =	self.config.rho
         __UpperCamelCase						:						int		     =	sigma_min ** (1 / rho)
         __UpperCamelCase						:						int		     =	sigma_max ** (1 / rho)
         __UpperCamelCase						:						Optional[int]		     =	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas







     def lowerCAmelCase  (		self  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						int		     =	self.config.sigma_data

         __UpperCamelCase						:						Optional[Any]		     =	sigma_data**2 / (sigma**2 + sigma_data**2)
         __UpperCamelCase						:						Tuple		     =	sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out







     def lowerCAmelCase  (		self  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						List[str]		     =	self.config.sigma_min
         __UpperCamelCase						:						str		     =	self.config.sigma_data

         __UpperCamelCase						:						List[Any]		     =	sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
         __UpperCamelCase						:						str		     =	(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
         return c_skip, c_out







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = True  ,      ):

         if (
             isinstance(_lowerCamelCase  ,      _lowerCamelCase     )
             or isinstance(_lowerCamelCase  ,      torch.IntTensor     )
             or isinstance(_lowerCamelCase  ,      torch.LongTensor     )
         ):
             raise ValueError(
                 (
                     'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                     f""" `{self.__class__}.step()` is not supported. Make sure to pass"""
                     ' one of the `scheduler.timesteps` as a timestep.'
                 )  ,      )

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

         if isinstance(_lowerCamelCase  ,      torch.Tensor     ):
             __UpperCamelCase						:						List[str]		     =	timestep.to(self.timesteps.device     )

         __UpperCamelCase						:						Union[str, Any]		     =	self.config.sigma_min
         __UpperCamelCase						:						Any		     =	self.config.sigma_max

         __UpperCamelCase						:						Optional[Any]		     =	self.index_for_timestep(_lowerCamelCase     )

         # sigma_next corresponds to next_t in original implementation
         __UpperCamelCase						:						Tuple		     =	self.sigmas[step_index]
         if step_index + 1 < self.config.num_train_timesteps:
             __UpperCamelCase						:						int		     =	self.sigmas[step_index + 1]
         else:
             # Set sigma_next to sigma_min
             __UpperCamelCase						:						Union[str, Any]		     =	self.sigmas[-1]

         # Get scalings for boundary conditions
         __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	self.get_scalings_for_boundary_condition(_lowerCamelCase     )

         # 1. Denoise model output using boundary conditions
         __UpperCamelCase						:						List[Any]		     =	c_out * model_output + c_skip * sample
         if self.config.clip_denoised:
             __UpperCamelCase						:						Tuple		     =	denoised.clamp(-1  ,      1     )

         # 2. Sample z ~ N(0, s_noise^2 * I)
         # Noise is not used for onestep sampling.
         if len(self.timesteps     ) > 1:
             __UpperCamelCase						:						Union[str, Any]		     =	randn_tensor(
                 model_output.shape  ,      dtype=model_output.dtype  ,      device=model_output.device  ,      generator=_lowerCamelCase     )
         else:
             __UpperCamelCase						:						str		     =	torch.zeros_like(_lowerCamelCase     )
         __UpperCamelCase						:						Any		     =	noise * self.config.s_noise

         __UpperCamelCase						:						str		     =	sigma_next.clamp(min=_lowerCamelCase  ,      max=_lowerCamelCase     )

         # 3. Return noisy sample
         # tau = sigma_hat, eps = sigma_min
         __UpperCamelCase						:						int		     =	denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

         if not return_dict:
             return (prev_sample,)

         return CMStochasticIterativeSchedulerOutput(prev_sample=_lowerCamelCase     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      ):
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         __UpperCamelCase						:						Optional[Any]		     =	self.sigmas.to(device=original_samples.device  ,      dtype=original_samples.dtype     )
         if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCamelCase     ):
             # mps does not support float64
             __UpperCamelCase						:						List[str]		     =	self.timesteps.to(original_samples.device  ,      dtype=torch.floataa     )
             __UpperCamelCase						:						Optional[int]		     =	timesteps.to(original_samples.device  ,      dtype=torch.floataa     )
         else:
             __UpperCamelCase						:						Optional[int]		     =	self.timesteps.to(original_samples.device     )
             __UpperCamelCase						:						Optional[Any]		     =	timesteps.to(original_samples.device     )

         __UpperCamelCase						:						Dict		     =	[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

         __UpperCamelCase						:						Dict		     =	original_samples + noise * sigma
         return noisy_samples







     def __len__(		self     ):
         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			(  _a						:						List[Any]					,  _a						:						Dict					,  _a						:						Optional[int]					,  _a						:						Dict					,  _a						:						int					,  _a						:						List[str]      ):




    """simple docstring"""



    __UpperCamelCase						:						Union[str, Any]		     =	states.reshape(1					,  -1					,  model.config.state_dim      )
    __UpperCamelCase						:						Tuple		     =	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						:						Any		     =	states[:, -model.config.max_length :]
        __UpperCamelCase						:						int		     =	actions[:, -model.config.max_length :]
        __UpperCamelCase						:						Any		     =	returns_to_go[:, -model.config.max_length :]
        __UpperCamelCase						:						Tuple		     =	timesteps[:, -model.config.max_length :]

        # pad all tokens to sequence length
        __UpperCamelCase						:						str		     =	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						:						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      )
        __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						:						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      )
        __UpperCamelCase						:						Tuple		     =	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						:						List[str]		     =	None

    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Union[str, Any]		     =	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

a=	gym.make('''Hopper-v3''')
a=	env.observation_space.shape[0]
a=	env.action_space.shape[0]
a=	1_0_0_0
a=	'''cuda'''
a=	1_000.0  # normalization for rewards/returns
a=	3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
a=	np.array(
    [
        1.311_279,
        -0.08_469_521,
        -0.5_382_719,
        -0.07_201_576,
        0.04_932_366,
        2.1_066_856,
        -0.15_017_354,
        0.00_878_345,
        -0.2_848_186,
        -0.18_540_096,
        -0.28_461_286,
    ]
)
a=	np.array(
    [
        0.17_790_751,
        0.05_444_621,
        0.21_297_139,
        0.14_530_419,
        0.6_124_444,
        0.85_174_465,
        1.4_515_252,
        0.6_751_696,
        1.536_239,
        1.6_160_746,
        5.6_072_536,
    ]
)
a=	torch.from_numpy(state_mean).to(device=device)
a=	torch.from_numpy(state_std).to(device=device)

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

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

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

    a=	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),
    )
    a=	action
    a=	action.detach().cpu().numpy()

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

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

    a=	target_return[0, -1] - (reward / scale)
    a=	torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    a=	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



