


"""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__		(					_UpperCAmelCase    :      torch.Tensor				,    _UpperCAmelCase    :      torch.Tensor				,    _UpperCAmelCase    :      None	)							->	torch.Tensor:

	'''simple docstring'''


	...



@overload
def 			A__		(					_UpperCAmelCase    :      torch.Tensor				,    _UpperCAmelCase    :      torch.Tensor				,    _UpperCAmelCase    :      torch.Tensor	)							->	Tuple[torch.Tensor, torch.Tensor]:

	'''simple docstring'''


	...



def 			A__		(					_UpperCAmelCase    :      int				,    _UpperCAmelCase    :      List[str]				,    _UpperCAmelCase    :      Optional[int]	)							->	List[str]:

	'''simple docstring'''


	snake_case__  :						Optional[Any]		=      aatype == rc.restype_order["G"]
	snake_case__  :						Union[str, Any]		=      rc.atom_order["CA"]
	snake_case__  :						Dict		=      rc.atom_order["CB"]
	snake_case__  :						Union[str, 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:
		snake_case__  :						Optional[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 			A__		(					_UpperCAmelCase    :      torch.Tensor				,    _UpperCAmelCase    :      Dict[str, torch.Tensor]	)							->	torch.Tensor:

	'''simple docstring'''


	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 			A__		(					_UpperCAmelCase    :      Dict[str, torch.Tensor]	)							->	torch.Tensor:

	'''simple docstring'''


	snake_case__  :						int		=      template_feats["template_aatype"]
	snake_case__  :						Optional[int]		=      template_feats["template_torsion_angles_sin_cos"]
	snake_case__  :						Any		=      template_feats["template_alt_torsion_angles_sin_cos"]
	snake_case__  :						str		=      template_feats["template_torsion_angles_mask"]
	snake_case__  :						Tuple		=      torch.cat(
	    [
	        nn.functional.one_hot(_UpperCAmelCase				,    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__		(					_UpperCAmelCase    :      Dict[str, torch.Tensor]				,    _UpperCAmelCase    :      torch.types.Number				,    _UpperCAmelCase    :      torch.types.Number				,    _UpperCAmelCase    :      int				,    _UpperCAmelCase    :      bool = False				,    _UpperCAmelCase    :      float = 1e-20				,    _UpperCAmelCase    :      float = 1e8				,    )							->	torch.Tensor:

	'''simple docstring'''


	snake_case__  :						List[Any]		=      batch["template_pseudo_beta_mask"]
	snake_case__  :						int		=      template_mask[..., None] * template_mask[..., None, :]

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

	snake_case__  :						Tuple		=      [dgram, template_mask_ad[..., None]]

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

	snake_case__  :						int		=      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[Any]		=      [rc.atom_order[a] for a in ["N", "CA", "C"]]
	snake_case__  :						Any		=      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__  :						str		=      rigids.get_trans()[..., None, :, :]
	snake_case__  :						Dict		=      rigids[..., None].invert_apply(_UpperCAmelCase	)

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

	snake_case__  :						Dict		=      batch["template_all_atom_mask"]
	snake_case__  :						List[str]		=      t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
	snake_case__  :						Any		=      template_mask[..., None] * template_mask[..., None, :]

	snake_case__  :						int		=      inv_distance_scalar * template_mask_ad
	snake_case__  :						List[str]		=      rigid_vec * inv_distance_scalar[..., None]

	if not use_unit_vector:
		snake_case__  :						Optional[Any]		=      unit_vector * 0.0

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

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

	return act



def 			A__		(					_UpperCAmelCase    :      Dict[str, torch.Tensor]	)							->	torch.Tensor:

	'''simple docstring'''


	snake_case__  :						torch.LongTensor		=      nn.functional.one_hot(batch["extra_msa"]				,    23	)
	snake_case__  :						Optional[Any]		=      [
	    msa_ahot,
	    batch["extra_has_deletion"].unsqueeze(-1	),
	    batch["extra_deletion_value"].unsqueeze(-1	),
	]
	return torch.cat(_UpperCAmelCase				,    dim=-1	)



def 			A__		(					_UpperCAmelCase    :      Rigid				,    _UpperCAmelCase    :      torch.Tensor				,    _UpperCAmelCase    :      torch.Tensor				,    _UpperCAmelCase    :      torch.Tensor				,    )							->	Rigid:

	'''simple docstring'''


	snake_case__  :						Optional[int]		=      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__  :						List[Any]		=      alpha.new_zeros((*((1,) * len(alpha.shape[:-1]	)), 2)	)
	snake_case__  :						Dict		=      1

	# [*, N, 8, 2]
	snake_case__  :						str		=      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__  :						Tuple		=      alpha.new_zeros(default_r.get_rots().get_rot_mats().shape	)
	snake_case__  :						str		=      1
	snake_case__  :						str		=      alpha[..., 1]
	snake_case__  :						Optional[int]		=      -alpha[..., 0]
	snake_case__  :						List[Any]		=      alpha

	snake_case__  :						str		=      default_r.compose(Rigid(Rotation(rot_mats=_UpperCAmelCase	)				,    _UpperCAmelCase	)	)

	snake_case__  :						Optional[int]		=      all_frames[..., 5]
	snake_case__  :						List[str]		=      all_frames[..., 6]
	snake_case__  :						Tuple		=      all_frames[..., 7]

	snake_case__  :						Optional[Any]		=      all_frames[..., 4]
	snake_case__  :						str		=      chia_frame_to_bb.compose(_UpperCAmelCase	)
	snake_case__  :						List[Any]		=      chia_frame_to_bb.compose(_UpperCAmelCase	)
	snake_case__  :						Union[str, Any]		=      chia_frame_to_bb.compose(_UpperCAmelCase	)

	snake_case__  :						Any		=      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__  :						Tuple		=      r[..., None].compose(_UpperCAmelCase	)

	return all_frames_to_global



def 			A__		(					_UpperCAmelCase    :      Rigid				,    _UpperCAmelCase    :      torch.Tensor				,    _UpperCAmelCase    :      torch.Tensor				,    _UpperCAmelCase    :      torch.Tensor				,    _UpperCAmelCase    :      torch.Tensor				,    _UpperCAmelCase    :      torch.Tensor				,    )							->	torch.Tensor:

	'''simple docstring'''


	snake_case__  :						Optional[int]		=      group_idx[aatype, ...]

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

	# [*, N, 14, 8]
	snake_case__  :						int		=      r[..., None, :] * group_mask_one_hot

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

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

	# [*, N, 14, 3]
	snake_case__  :						List[Any]		=      lit_positions[aatype, ...]
	snake_case__  :						Union[str, Any]		=      t_atoms_to_global.apply(_UpperCAmelCase	)
	snake_case__  :						Dict		=      pred_positions * atom_mask

	return pred_positions











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE     : Tuple      ={
    """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : Dict      =["""LlamaTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : int      =["""LlamaTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : Optional[Any]      =[
          """OpenLlamaForCausalLM""",
          """OpenLlamaModel""",
          """OpenLlamaPreTrainedModel""",
          """OpenLlamaForSequenceClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from transformers import LlamaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from transformers import LlamaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_open_llama import (
                OpenLlamaForCausalLM,
                OpenLlamaForSequenceClassification,
                OpenLlamaModel,
                OpenLlamaPreTrainedModel,
            )


else:
      import sys

      __SCREAMING_SNAKE_CASE     : Union[str, Any]      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)