




"""simple docstring"""

import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


__lowerCamelCase								=						get_logger(__name__)



def 				a (						__snake_case     :				Tuple,    __snake_case     :				Union[str, Any],    __snake_case     :				Tuple,    __snake_case     :				Dict,    __snake_case     :				Any,    __snake_case     :				Optional[Any] = None,    ):



    '''simple docstring'''





    for i, state in enumerate(lowerCamelCase__  ):
        UpperCAmelCase_					:List[str]      			=      f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
        UpperCAmelCase_					:Any      			=      os.path.join(lowerCamelCase__,    lowerCamelCase__  )
        save(lowerCamelCase__,    lowerCamelCase__  )
        logger.info(f'Model weights saved in {output_model_file}'  )
    # Optimizer states
    for i, opt in enumerate(lowerCamelCase__  ):
        UpperCAmelCase_					:Union[str, Any]      			=      opt.state_dict()
        UpperCAmelCase_					:Optional[int]      			=      f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
        UpperCAmelCase_					:Optional[int]      			=      os.path.join(lowerCamelCase__,    lowerCamelCase__  )
        save(lowerCamelCase__,    lowerCamelCase__  )
        logger.info(f'Optimizer state saved in {output_optimizer_file}'  )
    # Scheduler states
    for i, scheduler in enumerate(lowerCamelCase__  ):
        UpperCAmelCase_					:List[Any]      			=      scheduler.state_dict()
        UpperCAmelCase_					:Dict      			=      f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
        UpperCAmelCase_					:Optional[int]      			=      os.path.join(lowerCamelCase__,    lowerCamelCase__  )
        save(lowerCamelCase__,    lowerCamelCase__  )
        logger.info(f'Scheduler state saved in {output_scheduler_file}'  )
    # GradScaler state
    if scaler is not None:
        UpperCAmelCase_					:Tuple      			=      scaler.state_dict()
        UpperCAmelCase_					:int      			=      os.path.join(lowerCamelCase__,    lowerCamelCase__  )
        torch.save(lowerCamelCase__,    lowerCamelCase__  )
        logger.info(f'Gradient scaler state saved in {output_scaler_file}'  )
    # Random number generator states
    UpperCAmelCase_					:List[Any]      			=      {}
    UpperCAmelCase_					:Any      			=      f'{RNG_STATE_NAME}_{process_index}.pkl'
    UpperCAmelCase_					:Optional[int]      			=      random.getstate()
    UpperCAmelCase_					:Any      			=      np.random.get_state()
    UpperCAmelCase_					:Optional[int]      			=      torch.get_rng_state()
    if is_xpu_available():
        UpperCAmelCase_					:int      			=      torch.xpu.get_rng_state_all()
    else:
        UpperCAmelCase_					:int      			=      torch.cuda.get_rng_state_all()
    if is_tpu_available():
        UpperCAmelCase_					:List[Any]      			=      xm.get_rng_state()
    UpperCAmelCase_					:Dict      			=      os.path.join(lowerCamelCase__,    lowerCamelCase__  )
    torch.save(lowerCamelCase__,    lowerCamelCase__  )
    logger.info(f'Random states saved in {output_states_file}'  )
    return output_dir



def 				a (						__snake_case     :				int,    __snake_case     :				Optional[Any],    __snake_case     :				str,    __snake_case     :				Union[str, Any],    __snake_case     :				str,    __snake_case     :				Tuple=None,    __snake_case     :				Union[str, Any]=None,    **__snake_case     :				Any,    ):



    '''simple docstring'''





    if map_location not in [None, "cpu", "on_device"]:
        raise TypeError(
            '''Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'''  )
    if map_location is None:
        UpperCAmelCase_					:List[str]      			=      '''cpu'''
    elif map_location == "on_device":
        UpperCAmelCase_					:Optional[Any]      			=      PartialState().device
    # Model states
    for i, model in enumerate(lowerCamelCase__  ):
        UpperCAmelCase_					:List[str]      			=      f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
        UpperCAmelCase_					:Optional[Any]      			=      os.path.join(lowerCamelCase__,    lowerCamelCase__  )
        models[i].load_state_dict(torch.load(lowerCamelCase__,    map_location=lowerCamelCase__  ),    **lowerCamelCase__  )
    logger.info('''All model weights loaded successfully'''  )

    # Optimizer states
    for i, opt in enumerate(lowerCamelCase__  ):
        UpperCAmelCase_					:Optional[Any]      			=      f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
        UpperCAmelCase_					:Tuple      			=      os.path.join(lowerCamelCase__,    lowerCamelCase__  )
        UpperCAmelCase_					:Tuple      			=      torch.load(lowerCamelCase__,    map_location=lowerCamelCase__  )
        optimizers[i].load_state_dict(lowerCamelCase__  )
    logger.info('''All optimizer states loaded successfully'''  )

    # Scheduler states
    for i, scheduler in enumerate(lowerCamelCase__  ):
        UpperCAmelCase_					:List[Any]      			=      f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
        UpperCAmelCase_					:Dict      			=      os.path.join(lowerCamelCase__,    lowerCamelCase__  )
        scheduler.load_state_dict(torch.load(lowerCamelCase__  )  )
    logger.info('''All scheduler states loaded successfully'''  )

    # GradScaler state
    if scaler is not None:
        UpperCAmelCase_					:Tuple      			=      os.path.join(lowerCamelCase__,    lowerCamelCase__  )
        scaler.load_state_dict(torch.load(lowerCamelCase__  )  )
        logger.info('''GradScaler state loaded successfully'''  )

    # Random states
    try:
        UpperCAmelCase_					:Dict      			=      torch.load(os.path.join(lowerCamelCase__,    f'{RNG_STATE_NAME}_{process_index}.pkl'  )  )
        random.setstate(states['''random_state''']  )
        np.random.set_state(states['''numpy_random_seed''']  )
        torch.set_rng_state(states['''torch_manual_seed''']  )
        if is_xpu_available():
            torch.xpu.set_rng_state_all(states['''torch_xpu_manual_seed''']  )
        else:
            torch.cuda.set_rng_state_all(states['''torch_cuda_manual_seed''']  )
        if is_tpu_available():
            xm.set_rng_state(states['''xm_seed''']  )
        logger.info('''All random states loaded successfully'''  )
    except Exception:
        logger.info('''Could not load random states'''  )



def 				a (						__snake_case     :				Optional[Any],    __snake_case     :				Optional[Any],    __snake_case     :				Optional[Any] = 0  ):



    '''simple docstring'''





    UpperCAmelCase_					:List[str]      			=      Path(lowerCamelCase__  ) / f'custom_checkpoint_{index}.pkl'
    logger.info(f'Saving the state of {get_pretty_name(lowerCamelCase__  )} to {save_location}'  )
    torch.save(obj.state_dict(),    lowerCamelCase__  )





def 				a (						__snake_case     :				List[Any],    __snake_case     :				Tuple,    __snake_case     :				Any = 0  ):



    '''simple docstring'''





    UpperCAmelCase_					:List[str]      			=      f'{path}/custom_checkpoint_{index}.pkl'
    logger.info(f'Loading the state of {get_pretty_name(lowerCamelCase__  )} from {load_location}'  )
    obj.load_state_dict(torch.load(lowerCamelCase__,    map_location='''cpu'''  )  )








"""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 				a (						__snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				None  ):



				'''simple docstring'''





				...



@overload
def 				a (						__snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor  ):



				'''simple docstring'''





				...



def 				a (						__snake_case     :				List[str],    __snake_case     :				Optional[int],    __snake_case     :				str  ):



				'''simple docstring'''





				UpperCAmelCase_					:int      			=      aatype == rc.restype_order['''G''']
				UpperCAmelCase_					:Optional[Any]      			=      rc.atom_order['''CA''']
				UpperCAmelCase_					:Optional[Any]      			=      rc.atom_order['''CB''']
				UpperCAmelCase_					:Optional[Any]      			=      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_					: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 				a (						__snake_case     :				torch.Tensor,    __snake_case     :				Dict[str, torch.Tensor]  ):



				'''simple docstring'''





				UpperCAmelCase_					:List[Any]      			=      batched_gather(
				    __snake_case,    batch['''residx_atom37_to_atom14'''],    dim=-2,    no_batch_dims=len(atomaa.shape[:-2]  ),    )

				UpperCAmelCase_					:Optional[Any]      			=      atomaa_data * batch['''atom37_atom_exists'''][..., None]

				return atomaa_data



def 				a (						__snake_case     :				Dict[str, torch.Tensor]  ):



				'''simple docstring'''





				UpperCAmelCase_					:str      			=      template_feats['''template_aatype''']
				UpperCAmelCase_					:Union[str, Any]      			=      template_feats['''template_torsion_angles_sin_cos''']
				UpperCAmelCase_					:Tuple      			=      template_feats['''template_alt_torsion_angles_sin_cos''']
				UpperCAmelCase_					:Tuple      			=      template_feats['''template_torsion_angles_mask''']
				UpperCAmelCase_					:int      			=      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 				a (						__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-20,    __snake_case     :				float = 1E8,    ):



				'''simple docstring'''





				UpperCAmelCase_					:List[str]      			=      batch['''template_pseudo_beta_mask''']
				UpperCAmelCase_					:Any      			=      template_mask[..., None] * template_mask[..., None, :]

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

				UpperCAmelCase_					:List[str]      			=      [dgram, template_mask_ad[..., None]]

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

				UpperCAmelCase_					:Optional[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_					:Optional[int]      			=      [rc.atom_order[a] for a in ['''N''', '''CA''', '''C''']]
				UpperCAmelCase_					:str      			=      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_					:Dict      			=      rigids.get_trans()[..., None, :, :]
				UpperCAmelCase_					:Dict      			=      rigids[..., None].invert_apply(__snake_case  )

				UpperCAmelCase_					:Optional[int]      			=      torch.rsqrt(eps + torch.sum(rigid_vec**2,    dim=-1  )  )

				UpperCAmelCase_					:Optional[int]      			=      batch['''template_all_atom_mask''']
				UpperCAmelCase_					:Tuple      			=      t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
				UpperCAmelCase_					:int      			=      template_mask[..., None] * template_mask[..., None, :]

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

				if not use_unit_vector:
								UpperCAmelCase_					:List[Any]      			=      unit_vector * 0.0

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

				UpperCAmelCase_					:Optional[int]      			=      torch.cat(__snake_case,    dim=-1  )
				UpperCAmelCase_					:Optional[Any]      			=      act * template_mask_ad[..., None]

				return act



def 				a (						__snake_case     :				Dict[str, torch.Tensor]  ):



				'''simple docstring'''





				UpperCAmelCase_					:torch.LongTensor      			=      nn.functional.one_hot(batch['''extra_msa'''],    23  )
				UpperCAmelCase_					:Union[str, Any]      			=      [
				    msa_ahot,
				    batch['''extra_has_deletion'''].unsqueeze(-1  ),
				    batch['''extra_deletion_value'''].unsqueeze(-1  ),
				]
				return torch.cat(__snake_case,    dim=-1  )



def 				a (						__snake_case     :				Rigid,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    ):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[int]      			=      rrgdf[aatype, ...]

				# [*, N, 8] transformations, i.e.
				#   One [*, N, 8, 3, 3] rotation matrix and
				#   One [*, N, 8, 3]    translation matrix
				UpperCAmelCase_					:Optional[int]      			=      r.from_tensor_axa(__snake_case  )

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

				# [*, N, 8, 2]
				UpperCAmelCase_					:Tuple      			=      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_					:Optional[Any]      			=      1
				UpperCAmelCase_					:int      			=      alpha[..., 1]
				UpperCAmelCase_					:Optional[int]      			=      -alpha[..., 0]
				UpperCAmelCase_					:int      			=      alpha

				UpperCAmelCase_					:List[str]      			=      default_r.compose(Rigid(Rotation(rot_mats=__snake_case  ),    __snake_case  )  )

				UpperCAmelCase_					:List[str]      			=      all_frames[..., 5]
				UpperCAmelCase_					:str      			=      all_frames[..., 6]
				UpperCAmelCase_					:List[Any]      			=      all_frames[..., 7]

				UpperCAmelCase_					:Tuple      			=      all_frames[..., 4]
				UpperCAmelCase_					:Optional[Any]      			=      chia_frame_to_bb.compose(__snake_case  )
				UpperCAmelCase_					:Optional[Any]      			=      chia_frame_to_bb.compose(__snake_case  )
				UpperCAmelCase_					:Union[str, Any]      			=      chia_frame_to_bb.compose(__snake_case  )

				UpperCAmelCase_					:List[str]      			=      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_					:Any      			=      r[..., None].compose(__snake_case  )

				return all_frames_to_global





def 				a (						__snake_case     :				Rigid,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    __snake_case     :				torch.Tensor,    ):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[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_					:Dict      			=      r[..., None, :] * group_mask_one_hot

				# [*, N, 14]
				UpperCAmelCase_					:List[Any]      			=      t_atoms_to_global.map_tensor_fn(lambda __snake_case  : torch.sum(__snake_case,    dim=-1  )  )

				# [*, N, 14, 1]
				UpperCAmelCase_					:List[Any]      			=      atom_mask[aatype, ...].unsqueeze(-1  )

				# [*, N, 14, 3]
				UpperCAmelCase_					:List[Any]      			=      lit_positions[aatype, ...]
				UpperCAmelCase_					:Optional[int]      			=      t_atoms_to_global.apply(__snake_case  )
				UpperCAmelCase_					:int      			=      pred_positions * atom_mask

				return pred_positions


