






"""simple docstring"""



from typing import Dict, Tuple, overload

import torch
import torch.types
from torch import nn

from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather


@overload
def      SCREAMING_SNAKE_CASE__    (   snake_case    :      torch.Tensor       , snake_case    :      torch.Tensor       , snake_case    :      None							)->						torch.Tensor:






     '''simple docstring'''



     ...


@overload
def      SCREAMING_SNAKE_CASE__    (   snake_case    :      torch.Tensor       , snake_case    :      torch.Tensor       , snake_case    :      torch.Tensor							)->						Tuple[torch.Tensor, torch.Tensor]:






     '''simple docstring'''



     ...


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      Tuple       , snake_case    :      Union[str, Any]       , snake_case    :      Optional[Any]							)->						Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__		:  Optional[int]				=				aatype == rc.restype_order["G"]
     UpperCAmelCase__		:  str				=				rc.atom_order["CA"]
     UpperCAmelCase__		:  Optional[Any]				=				rc.atom_order["CB"]
     UpperCAmelCase__		:  str				=				torch.where(
         is_gly[..., None].expand(*((-1,) * len(is_gly.shape							))       , 3							)       , all_atom_positions[..., ca_idx, :]       , all_atom_positions[..., cb_idx, :]       , )

     if all_atom_masks is not None:
          UpperCAmelCase__		:  List[Any]				=				torch.where(
              snake_case       , all_atom_masks[..., ca_idx]       , all_atom_masks[..., cb_idx]       , )
          return pseudo_beta, pseudo_beta_mask
     else:
          return pseudo_beta


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      torch.Tensor       , snake_case    :      Dict[str, torch.Tensor]							)->						torch.Tensor:






     '''simple docstring'''



     UpperCAmelCase__		:  int				=				batched_gather(
         snake_case       , batch["residx_atom37_to_atom14"]       , dim=-2       , no_batch_dims=len(atomaa.shape[:-2]							)       , )

     UpperCAmelCase__		:  Dict				=				atomaa_data * batch["atom37_atom_exists"][..., None]

     return atomaa_data


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      Dict[str, torch.Tensor]							)->						torch.Tensor:






     '''simple docstring'''



     UpperCAmelCase__		:  List[Any]				=				template_feats["template_aatype"]
     UpperCAmelCase__		:  List[Any]				=				template_feats["template_torsion_angles_sin_cos"]
     UpperCAmelCase__		:  Optional[Any]				=				template_feats["template_alt_torsion_angles_sin_cos"]
     UpperCAmelCase__		:  int				=				template_feats["template_torsion_angles_mask"]
     UpperCAmelCase__		:  List[Any]				=				torch.cat(
         [
             nn.functional.one_hot(snake_case       , 22							),
             torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]       , 14							),
             alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]       , 14							),
             torsion_angles_mask,
         ]       , dim=-1       , )

     return template_angle_feat


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      Dict[str, torch.Tensor]       , snake_case    :      torch.types.Number       , snake_case    :      torch.types.Number       , snake_case    :      int       , snake_case    :      bool = False       , snake_case    :      float = 1E-2_0       , snake_case    :      float = 1E8       , )->						torch.Tensor:






     '''simple docstring'''



     UpperCAmelCase__		:  Union[str, Any]				=				batch["template_pseudo_beta_mask"]
     UpperCAmelCase__		:  Optional[int]				=				template_mask[..., None] * template_mask[..., None, :]

     # Compute distogram (this seems to differ slightly from Alg. 5)
     UpperCAmelCase__		:  Any				=				batch["template_pseudo_beta"]
     UpperCAmelCase__		:  str				=				torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2       , dim=-1       , keepdim=snake_case							)
     UpperCAmelCase__		:  str				=				torch.linspace(snake_case       , snake_case       , snake_case       , device=tpb.device							) ** 2
     UpperCAmelCase__		:  Tuple				=				torch.cat([lower[1:], lower.new_tensor([inf]							)]       , dim=-1							)
     UpperCAmelCase__		:  Optional[int]				=				((dgram > lower) * (dgram < upper)).type(dgram.dtype							)

     UpperCAmelCase__		:  Union[str, Any]				=				[dgram, template_mask_ad[..., None]]

     UpperCAmelCase__		:  torch.LongTensor				=				nn.functional.one_hot(
         batch["template_aatype"]       , rc.restype_num + 2       , )

     UpperCAmelCase__		:  List[Any]				=				batch["template_aatype"].shape[-1]
     to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]       , snake_case       , -1       , -1							)							)
     to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]       , -1       , snake_case       , -1							)							)

     UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__		:  List[Any]				=				[rc.atom_order[a] for a in ["N", "CA", "C"]]
     UpperCAmelCase__		:  int				=				Rigid.make_transform_from_reference(
         n_xyz=batch["template_all_atom_positions"][..., n, :]       , ca_xyz=batch["template_all_atom_positions"][..., ca, :]       , c_xyz=batch["template_all_atom_positions"][..., c, :]       , eps=snake_case       , )
     UpperCAmelCase__		:  List[str]				=				rigids.get_trans()[..., None, :, :]
     UpperCAmelCase__		:  Dict				=				rigids[..., None].invert_apply(snake_case							)

     UpperCAmelCase__		:  List[str]				=				torch.rsqrt(eps + torch.sum(rigid_vec**2       , dim=-1							)							)

     UpperCAmelCase__		:  Optional[Any]				=				batch["template_all_atom_mask"]
     UpperCAmelCase__		:  Optional[Any]				=				t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
     UpperCAmelCase__		:  Optional[int]				=				template_mask[..., None] * template_mask[..., None, :]

     UpperCAmelCase__		:  List[str]				=				inv_distance_scalar * template_mask_ad
     UpperCAmelCase__		:  Any				=				rigid_vec * inv_distance_scalar[..., None]

     if not use_unit_vector:
          UpperCAmelCase__		:  Any				=				unit_vector * 0.0

     to_concat.extend(torch.unbind(unit_vector[..., None, :]       , dim=-1							)							)
     to_concat.append(template_mask_ad[..., None]							)

     UpperCAmelCase__		:  str				=				torch.cat(snake_case       , dim=-1							)
     UpperCAmelCase__		:  List[str]				=				act * template_mask_ad[..., None]

     return act


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      Dict[str, torch.Tensor]							)->						torch.Tensor:






     '''simple docstring'''



     UpperCAmelCase__		:  torch.LongTensor				=				nn.functional.one_hot(batch["extra_msa"]       , 23							)
     UpperCAmelCase__		:  Dict				=				[
         msa_ahot,
         batch["extra_has_deletion"].unsqueeze(-1							),
         batch["extra_deletion_value"].unsqueeze(-1							),
     ]
     return torch.cat(snake_case       , dim=-1							)


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      Rigid       , snake_case    :      torch.Tensor       , snake_case    :      torch.Tensor       , snake_case    :      torch.Tensor       , )->						Rigid:






     '''simple docstring'''



     UpperCAmelCase__		:  Union[str, Any]				=				rrgdf[aatype, ...]

     # [*, N, 8] transformations, i.e.
     #   One [*, N, 8, 3, 3] rotation matrix and
     #   One [*, N, 8, 3]    translation matrix
     UpperCAmelCase__		:  List[Any]				=				r.from_tensor_axa(snake_case							)

     UpperCAmelCase__		:  List[Any]				=				alpha.new_zeros((*((1,) * len(alpha.shape[:-1]							)), 2)							)
     UpperCAmelCase__		:  str				=				1

     # [*, N, 8, 2]
     UpperCAmelCase__		:  Union[str, Any]				=				torch.cat([bb_rot.expand(*alpha.shape[:-2]       , -1       , -1							), alpha]       , dim=-2							)

     # [*, N, 8, 3, 3]
     # Produces rotation matrices of the form:
     # [
     #   [1, 0  , 0  ],
     #   [0, a_2,-a_1],
     #   [0, a_1, a_2]
     # ]
     # This follows the original code rather than the supplement, which uses
     # different indices.

     UpperCAmelCase__		:  Any				=				alpha.new_zeros(default_r.get_rots().get_rot_mats().shape							)
     UpperCAmelCase__		:  Dict				=				1
     UpperCAmelCase__		:  Any				=				alpha[..., 1]
     UpperCAmelCase__		:  Optional[Any]				=				-alpha[..., 0]
     UpperCAmelCase__		:  Optional[Any]				=				alpha

     UpperCAmelCase__		:  str				=				default_r.compose(Rigid(Rotation(rot_mats=snake_case							)       , snake_case							)							)

     UpperCAmelCase__		:  Optional[int]				=				all_frames[..., 5]
     UpperCAmelCase__		:  str				=				all_frames[..., 6]
     UpperCAmelCase__		:  str				=				all_frames[..., 7]

     UpperCAmelCase__		:  Dict				=				all_frames[..., 4]
     UpperCAmelCase__		:  Tuple				=				chia_frame_to_bb.compose(snake_case							)
     UpperCAmelCase__		:  int				=				chia_frame_to_bb.compose(snake_case							)
     UpperCAmelCase__		:  List[Any]				=				chia_frame_to_bb.compose(snake_case							)

     UpperCAmelCase__		:  int				=				Rigid.cat(
         [
             all_frames[..., :5],
             chia_frame_to_bb.unsqueeze(-1							),
             chia_frame_to_bb.unsqueeze(-1							),
             chia_frame_to_bb.unsqueeze(-1							),
         ]       , dim=-1       , )

     UpperCAmelCase__		:  List[Any]				=				r[..., None].compose(snake_case							)

     return all_frames_to_global



def      SCREAMING_SNAKE_CASE__    (   snake_case    :      Rigid       , snake_case    :      torch.Tensor       , snake_case    :      torch.Tensor       , snake_case    :      torch.Tensor       , snake_case    :      torch.Tensor       , snake_case    :      torch.Tensor       , )->						torch.Tensor:






     '''simple docstring'''



     UpperCAmelCase__		:  List[Any]				=				group_idx[aatype, ...]

     # [*, N, 14, 8]
     UpperCAmelCase__		:  torch.LongTensor				=				nn.functional.one_hot(
         snake_case       , num_classes=default_frames.shape[-3]       , )

     # [*, N, 14, 8]
     UpperCAmelCase__		:  List[Any]				=				r[..., None, :] * group_mask_one_hot

     # [*, N, 14]
     UpperCAmelCase__		:  str				=				t_atoms_to_global.map_tensor_fn(lambda snake_case							: torch.sum(snake_case       , dim=-1							)							)

     # [*, N, 14, 1]
     UpperCAmelCase__		:  int				=				atom_mask[aatype, ...].unsqueeze(-1							)

     # [*, N, 14, 3]
     UpperCAmelCase__		:  Any				=				lit_positions[aatype, ...]
     UpperCAmelCase__		:  Union[str, Any]				=				t_atoms_to_global.apply(snake_case							)
     UpperCAmelCase__		:  Any				=				pred_positions * atom_mask

     return pred_positions










"""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      SCREAMING_SNAKE_CASE__    (   snake_case    :      Any       , snake_case    :      Optional[int]       , snake_case    :      Tuple       , snake_case    :      Union[str, Any]       , snake_case    :      Dict       , snake_case    :      str							)->						Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__		:  List[Any]				=				states.reshape(1       , -1       , model.config.state_dim							)
     UpperCAmelCase__		:  str				=				actions.reshape(1       , -1       , model.config.act_dim							)
     UpperCAmelCase__		:  Any				=				returns_to_go.reshape(1       , -1       , 1							)
     UpperCAmelCase__		:  Union[str, Any]				=				timesteps.reshape(1       , -1							)

     if model.config.max_length is not None:
          UpperCAmelCase__		:  Tuple				=				states[:, -model.config.max_length :]
          UpperCAmelCase__		:  int				=				actions[:, -model.config.max_length :]
          UpperCAmelCase__		:  Union[str, Any]				=				returns_to_go[:, -model.config.max_length :]
          UpperCAmelCase__		:  int				=				timesteps[:, -model.config.max_length :]

          # pad all tokens to sequence length
          UpperCAmelCase__		:  Optional[Any]				=				torch.cat(
              [torch.zeros(model.config.max_length - states.shape[1]							), torch.ones(states.shape[1]							)]							)
          UpperCAmelCase__		:  Optional[Any]				=				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__		:  Optional[Any]				=				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__		:  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__		:  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:
          UpperCAmelCase__		:  Any				=				None

     UpperCAmelCase__				,							UpperCAmelCase__				,							UpperCAmelCase__		:  List[str]				=				model(
         states=snake_case       , actions=snake_case       , rewards=snake_case       , returns_to_go=snake_case       , timesteps=snake_case       , attention_mask=snake_case       , return_dict=snake_case       , )

     return action_preds[0, -1]


# build the environment

_lowerCAmelCase     :     Dict            =	gym.make("""Hopper-v3""")
_lowerCAmelCase     :     Any            =	env.observation_space.shape[0]
_lowerCAmelCase     :     List[Any]            =	env.action_space.shape[0]
_lowerCAmelCase     :     Any            =	1_000
_lowerCAmelCase     :     Union[str, Any]            =	"""cuda"""
_lowerCAmelCase     :     Optional[Any]            =	10_00.0  # normalization for rewards/returns
_lowerCAmelCase     :     Optional[int]            =	3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCAmelCase     :     Any            =	np.array(
    [
        1.31_12_79,
        -0.08_46_95_21,
        -0.5_38_27_19,
        -0.07_20_15_76,
        0.04_93_23_66,
        2.1_06_68_56,
        -0.15_01_73_54,
        0.00_87_83_45,
        -0.2_84_81_86,
        -0.18_54_00_96,
        -0.28_46_12_86,
    ]
)
_lowerCAmelCase     :     Optional[Any]            =	np.array(
    [
        0.17_79_07_51,
        0.05_44_46_21,
        0.21_29_71_39,
        0.14_53_04_19,
        0.6_12_44_44,
        0.85_17_44_65,
        1.4_51_52_52,
        0.6_75_16_96,
        1.53_62_39,
        1.6_16_07_46,
        5.6_07_25_36,
    ]
)
_lowerCAmelCase     :     Any            =	torch.from_numpy(state_mean).to(device=device)
_lowerCAmelCase     :     List[Any]            =	torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
_lowerCAmelCase     :     List[str]            =	DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-medium""")
_lowerCAmelCase     :     Optional[int]            =	model.to(device)
model.eval()

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

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

  _lowerCAmelCase     :     Tuple            =	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),
  )
  _lowerCAmelCase     :     List[str]            =	action
  _lowerCAmelCase     :     Optional[int]            =	action.detach().cpu().numpy()

  _lowerCAmelCase       ,   _lowerCAmelCase       ,   _lowerCAmelCase       ,   _lowerCAmelCase     :     int            =	env.step(action)

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

  _lowerCAmelCase     :     List[str]            =	target_return[0, -1] - (reward / scale)
  _lowerCAmelCase     :     Optional[int]            =	torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  _lowerCAmelCase     :     Tuple            =	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


