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_			(		lowerCAmelCase_: str ,	lowerCAmelCase_: Optional[Any] ,	lowerCAmelCase_: Union[str, Any]     ):
      ...






@overload
def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Any ,	lowerCAmelCase_: Union[str, Any] ,	lowerCAmelCase_: Union[str, Any]     ):
      ...






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Dict ,	lowerCAmelCase_: List[str] ,	lowerCAmelCase_: str     ):
      snake_case_     :			Optional[int]       =       aatype == rc.restype_order["G"]
      snake_case_     :			Union[str, Any]       =       rc.atom_order["CA"]
      snake_case_     :			Union[str, Any]       =       rc.atom_order["CB"]
      snake_case_     :			Dict       =       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:
            snake_case_     :			List[Any]       =       torch.where(
                UpperCamelCase_ ,	all_atom_masks[..., ca_idx] ,	all_atom_masks[..., cb_idx] ,	)
            return pseudo_beta, pseudo_beta_mask
      else:
            return pseudo_beta






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[str] ,	lowerCAmelCase_: Union[str, Any]     ):
      snake_case_     :			Optional[int]       =       batched_gather(
          UpperCamelCase_ ,	batch["residx_atom37_to_atom14"] ,	dim=-2 ,	no_batch_dims=len(atomaa.shape[:-2]     ) ,	)

      snake_case_     :			List[Any]       =       atomaa_data * batch["atom37_atom_exists"][..., None]

      return atomaa_data






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Dict     ):
      snake_case_     :			Dict       =       template_feats["template_aatype"]
      snake_case_     :			int       =       template_feats["template_torsion_angles_sin_cos"]
      snake_case_     :			Optional[int]       =       template_feats["template_alt_torsion_angles_sin_cos"]
      snake_case_     :			Optional[int]       =       template_feats["template_torsion_angles_mask"]
      snake_case_     :			List[Any]       =       torch.cat(
          [
              nn.functional.one_hot(UpperCamelCase_ ,	2_2     ),
              torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2] ,	1_4     ),
              alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2] ,	1_4     ),
              torsion_angles_mask,
          ] ,	dim=-1 ,	)

      return template_angle_feat






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[str] ,	lowerCAmelCase_: Optional[int] ,	lowerCAmelCase_: Dict ,	lowerCAmelCase_: Any ,	lowerCAmelCase_: List[Any] = False ,	lowerCAmelCase_: Optional[Any] = 1e-20 ,	lowerCAmelCase_: Optional[int] = 1e8 ,	):
      snake_case_     :			Dict       =       batch["template_pseudo_beta_mask"]
      snake_case_     :			Union[str, Any]       =       template_mask[..., None] * template_mask[..., None, :]

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

      snake_case_     :			int       =       [dgram, template_mask_ad[..., None]]

      snake_case_     :			Optional[int]       =       nn.functional.one_hot(
          batch["template_aatype"] ,	rc.restype_num + 2 ,	)

      snake_case_     :			str       =       batch["template_aatype"].shape[-1]
      to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2] ,	UpperCamelCase_ ,	-1 ,	-1     )     )
      to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2] ,	-1 ,	UpperCamelCase_ ,	-1     )     )

      snake_case_  ,snake_case_  ,snake_case_     :			List[str]       =       [rc.atom_order[a] for a in ["N", "CA", "C"]]
      snake_case_     :			List[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=UpperCamelCase_ ,	)
      snake_case_     :			Optional[Any]       =       rigids.get_trans()[..., None, :, :]
      snake_case_     :			Dict       =       rigids[..., None].invert_apply(UpperCamelCase_     )

      snake_case_     :			Dict       =       torch.rsqrt(eps + torch.sum(rigid_vec**2 ,	dim=-1     )     )

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

      snake_case_     :			Any       =       inv_distance_scalar * template_mask_ad
      snake_case_     :			Any       =       rigid_vec * inv_distance_scalar[..., None]

      if not use_unit_vector:
            snake_case_     :			Union[str, Any]       =       unit_vector * 0.0

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

      snake_case_     :			Union[str, Any]       =       torch.cat(UpperCamelCase_ ,	dim=-1     )
      snake_case_     :			Optional[int]       =       act * template_mask_ad[..., None]

      return act






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: int     ):
      snake_case_     :			int       =       nn.functional.one_hot(batch["extra_msa"] ,	2_3     )
      snake_case_     :			Optional[int]       =       [
          msa_ahot,
          batch["extra_has_deletion"].unsqueeze(-1     ),
          batch["extra_deletion_value"].unsqueeze(-1     ),
      ]
      return torch.cat(UpperCamelCase_ ,	dim=-1     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: int ,	lowerCAmelCase_: List[Any] ,	lowerCAmelCase_: Tuple ,	lowerCAmelCase_: Tuple ,	):
      snake_case_     :			List[str]       =       rrgdf[aatype, ...]

      # [*, N, 8] transformations, i.e.
      #   One [*, N, 8, 3, 3] rotation matrix and
      #   One [*, N, 8, 3]    translation matrix
      snake_case_     :			str       =       r.from_tensor_axa(UpperCamelCase_     )

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

      # [*, N, 8, 2]
      snake_case_     :			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.

      snake_case_     :			str       =       alpha.new_zeros(default_r.get_rots().get_rot_mats().shape     )
      snake_case_     :			Any       =       1
      snake_case_     :			Any       =       alpha[..., 1]
      snake_case_     :			Union[str, Any]       =       -alpha[..., 0]
      snake_case_     :			Union[str, Any]       =       alpha

      snake_case_     :			List[Any]       =       default_r.compose(Rigid(Rotation(rot_mats=UpperCamelCase_     ) ,	UpperCamelCase_     )     )

      snake_case_     :			Dict       =       all_frames[..., 5]
      snake_case_     :			Tuple       =       all_frames[..., 6]
      snake_case_     :			str       =       all_frames[..., 7]

      snake_case_     :			Union[str, Any]       =       all_frames[..., 4]
      snake_case_     :			Dict       =       chia_frame_to_bb.compose(UpperCamelCase_     )
      snake_case_     :			Optional[int]       =       chia_frame_to_bb.compose(UpperCamelCase_     )
      snake_case_     :			Optional[int]       =       chia_frame_to_bb.compose(UpperCamelCase_     )

      snake_case_     :			Dict       =       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 ,	)

      snake_case_     :			List[str]       =       r[..., None].compose(UpperCamelCase_     )

      return all_frames_to_global






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[Any] ,	lowerCAmelCase_: Union[str, Any] ,	lowerCAmelCase_: Tuple ,	lowerCAmelCase_: int ,	lowerCAmelCase_: int ,	lowerCAmelCase_: Optional[Any] ,	):
      snake_case_     :			List[str]       =       group_idx[aatype, ...]

      # [*, N, 14, 8]
      snake_case_     :			List[Any]       =       nn.functional.one_hot(
          UpperCamelCase_ ,	num_classes=default_frames.shape[-3] ,	)

      # [*, N, 14, 8]
      snake_case_     :			str       =       r[..., None, :] * group_mask_one_hot

      # [*, N, 14]
      snake_case_     :			int       =       t_atoms_to_global.map_tensor_fn(lambda lowerCAmelCase_     : torch.sum(UpperCamelCase_ ,	dim=-1     )     )

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

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

      return pred_positions
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


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






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: int ,	lowerCAmelCase_: bool = False     ):
      if device_specific:
            seed += AcceleratorState().process_index
      random.seed(lowerCAmelCase_     )
      np.random.seed(lowerCAmelCase_     )
      torch.manual_seed(lowerCAmelCase_     )
      if is_xpu_available():
            torch.xpu.manual_seed_all(lowerCAmelCase_     )
      elif is_npu_available():
            torch.npu.manual_seed_all(lowerCAmelCase_     )
      else:
            torch.cuda.manual_seed_all(lowerCAmelCase_     )
      # ^^ safe to call this function even if cuda is not available
      if is_tpu_available():
            xm.set_rng_state(lowerCAmelCase_     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Optional[RNGType] = None ,	lowerCAmelCase_: Optional[torch.Generator] = None     ):
      # Get the proper rng state
      if rng_type == RNGType.TORCH:
            snake_case_     :			List[Any]       =       torch.get_rng_state()
      elif rng_type == RNGType.CUDA:
            snake_case_     :			Dict       =       torch.cuda.get_rng_state()
      elif rng_type == RNGType.XLA:
            assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
            snake_case_     :			Tuple       =       torch.tensor(xm.get_rng_state()     )
      elif rng_type == RNGType.NPU:
            assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
            snake_case_     :			List[str]       =       torch.npu.get_rng_state()
      elif rng_type == RNGType.XPU:
            assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
            snake_case_     :			List[str]       =       torch.xpu.get_rng_state()
      elif rng_type == RNGType.GENERATOR:
            assert generator is not None, "Need a generator to synchronize its seed."
            snake_case_     :			Optional[Any]       =       generator.get_state()

      # Broadcast the rng state from device 0 to other devices
      snake_case_     :			List[Any]       =       AcceleratorState()
      if state.distributed_type == DistributedType.TPU:
            snake_case_     :			int       =       rng_state.to(xm.xla_device()     )
            xm.collective_broadcast([rng_state]     )
            xm.mark_step()
            snake_case_     :			List[str]       =       rng_state.cpu()
      elif (
          state.distributed_type in CUDA_DISTRIBUTED_TYPES
          or state.distributed_type == DistributedType.MULTI_NPU
          or state.distributed_type == DistributedType.MULTI_XPU
      ):
            snake_case_     :			Dict       =       rng_state.to(state.device     )
            torch.distributed.broadcast(lowerCAmelCase_ ,	0     )
            snake_case_     :			Dict       =       rng_state.cpu()
      elif state.distributed_type == DistributedType.MULTI_CPU:
            torch.distributed.broadcast(lowerCAmelCase_ ,	0     )

      # Set the broadcast rng state
      if rng_type == RNGType.TORCH:
            torch.set_rng_state(lowerCAmelCase_     )
      elif rng_type == RNGType.CUDA:
            torch.cuda.set_rng_state(lowerCAmelCase_     )
      elif rng_type == RNGType.NPU:
            torch.npu.set_rng_state(lowerCAmelCase_     )
      elif rng_type == RNGType.XPU:
            torch.xpu.set_rng_state(lowerCAmelCase_     )
      elif rng_type == RNGType.XLA:
            xm.set_rng_state(rng_state.item()     )
      elif rng_type == RNGType.GENERATOR:
            generator.set_state(lowerCAmelCase_     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[Union[str, RNGType]] ,	lowerCAmelCase_: Optional[torch.Generator] = None     ):
      for rng_type in rng_types:
            synchronize_rng_state(RNGType(lowerCAmelCase_     ) ,	generator=lowerCAmelCase_     )



