'''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 	_UpperCAmelCase       (				_UpperCamelCase       :      torch.Tensor,  _UpperCamelCase       :      torch.Tensor,  _UpperCamelCase       :      None  )      -> torch.Tensor:
    ...





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





def 	_UpperCAmelCase       (				_UpperCamelCase       :      Tuple,  _UpperCamelCase       :      Any,  _UpperCamelCase       :      List[Any]  )      -> Optional[int]:
    A_      =  aatype == rc.restype_order['''G''']
    A_      =  rc.atom_order['''CA''']
    A_      =  rc.atom_order['''CB''']
    A_      =  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:
        A_      =  torch.where(
            _UpperCamelCase,  all_atom_masks[..., ca_idx],  all_atom_masks[..., cb_idx],  )
        return pseudo_beta, pseudo_beta_mask
    else:
        return pseudo_beta





def 	_UpperCAmelCase       (				_UpperCamelCase       :      torch.Tensor,  _UpperCamelCase       :      Dict[str, torch.Tensor]  )      -> torch.Tensor:
    A_      =  batched_gather(
        _UpperCamelCase,  batch['''residx_atom37_to_atom14'''],  dim=-2,  no_batch_dims=len(atomaa.shape[:-2]  ),  )

    A_      =  atomaa_data * batch['''atom37_atom_exists'''][..., None]

    return atomaa_data





def 	_UpperCAmelCase       (				_UpperCamelCase       :      Dict[str, torch.Tensor]  )      -> torch.Tensor:
    A_      =  template_feats['''template_aatype''']
    A_      =  template_feats['''template_torsion_angles_sin_cos''']
    A_      =  template_feats['''template_alt_torsion_angles_sin_cos''']
    A_      =  template_feats['''template_torsion_angles_mask''']
    A_      =  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 	_UpperCAmelCase       (				_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:
    A_      =  batch['''template_pseudo_beta_mask''']
    A_      =  template_mask[..., None] * template_mask[..., None, :]

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

    A_      =  [dgram, template_mask_ad[..., None]]

    A_      =  nn.functional.one_hot(
        batch['''template_aatype'''],  rc.restype_num + 2,  )

    A_      =  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  )  )

    A_  ,A_  ,A_      =  [rc.atom_order[a] for a in ['''N''', '''CA''', '''C''']]
    A_      =  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,  )
    A_      =  rigids.get_trans()[..., None, :, :]
    A_      =  rigids[..., None].invert_apply(_UpperCamelCase  )

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

    A_      =  batch['''template_all_atom_mask''']
    A_      =  t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
    A_      =  template_mask[..., None] * template_mask[..., None, :]

    A_      =  inv_distance_scalar * template_mask_ad
    A_      =  rigid_vec * inv_distance_scalar[..., None]

    if not use_unit_vector:
        A_      =  unit_vector * 0.0

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

    A_      =  torch.cat(_UpperCamelCase,  dim=-1  )
    A_      =  act * template_mask_ad[..., None]

    return act





def 	_UpperCAmelCase       (				_UpperCamelCase       :      Dict[str, torch.Tensor]  )      -> torch.Tensor:
    A_      =  nn.functional.one_hot(batch['''extra_msa'''],  23  )
    A_      =  [
        msa_ahot,
        batch['''extra_has_deletion'''].unsqueeze(-1  ),
        batch['''extra_deletion_value'''].unsqueeze(-1  ),
    ]
    return torch.cat(_UpperCamelCase,  dim=-1  )





def 	_UpperCAmelCase       (				_UpperCamelCase       :      Rigid,  _UpperCamelCase       :      torch.Tensor,  _UpperCamelCase       :      torch.Tensor,  _UpperCamelCase       :      torch.Tensor,  )      -> Rigid:
    # [*, N, 8, 4, 4]
    A_      =  rrgdf[aatype, ...]

    # [*, N, 8] transformations, i.e.
    #   One [*, N, 8, 3, 3] rotation matrix and
    #   One [*, N, 8, 3]    translation matrix
    A_      =  r.from_tensor_axa(_UpperCamelCase  )

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

    # [*, N, 8, 2]
    A_      =  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.

    A_      =  alpha.new_zeros(default_r.get_rots().get_rot_mats().shape  )
    A_      =  1
    A_      =  alpha[..., 1]
    A_      =  -alpha[..., 0]
    A_      =  alpha

    A_      =  default_r.compose(Rigid(Rotation(rot_mats=_UpperCamelCase  ),  _UpperCamelCase  )  )

    A_      =  all_frames[..., 5]
    A_      =  all_frames[..., 6]
    A_      =  all_frames[..., 7]

    A_      =  all_frames[..., 4]
    A_      =  chia_frame_to_bb.compose(_UpperCamelCase  )
    A_      =  chia_frame_to_bb.compose(_UpperCamelCase  )
    A_      =  chia_frame_to_bb.compose(_UpperCamelCase  )

    A_      =  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,  )

    A_      =  r[..., None].compose(_UpperCamelCase  )

    return all_frames_to_global





def 	_UpperCAmelCase       (				_UpperCamelCase       :      Rigid,  _UpperCamelCase       :      torch.Tensor,  _UpperCamelCase       :      torch.Tensor,  _UpperCamelCase       :      torch.Tensor,  _UpperCamelCase       :      torch.Tensor,  _UpperCamelCase       :      torch.Tensor,  )      -> torch.Tensor:
    # [*, N, 14]
    A_      =  group_idx[aatype, ...]

    # [*, N, 14, 8]
    A_      =  nn.functional.one_hot(
        _UpperCamelCase,  num_classes=default_frames.shape[-3],  )

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

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

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

    # [*, N, 14, 3]
    A_      =  lit_positions[aatype, ...]
    A_      =  t_atoms_to_global.apply(_UpperCamelCase  )
    A_      =  pred_positions * atom_mask

    return pred_positions







'''simple docstring'''




import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__snake_case  : Optional[int]		       =    logging.getLogger(__name__)




class       __UpperCAmelCase							:





       '''simple docstring'''



       def __init__( self					)						->	List[str]:
           A_      =  False

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	str:
           if not self.initialized:
               A_      =  RagRetriever(
                   _SCREAMING_SNAKE_CASE       ,	question_encoder_tokenizer=_SCREAMING_SNAKE_CASE       ,	generator_tokenizer=_SCREAMING_SNAKE_CASE       ,	index=_SCREAMING_SNAKE_CASE       ,	init_retrieval=_SCREAMING_SNAKE_CASE       ,	)
               A_      =  True

       def    __A			( self					)						->	List[Any]:
           self.retriever.index.init_index()

       def    __A			( self					)						->	Dict:
           # delete the old self.retriever object before assigning the new index
           del self.retriever
           A_      =  False

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	Optional[int]:
           A_  ,A_      =  self.retriever._main_retrieve(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
           A_      =  self.retriever.index.get_doc_dicts(_SCREAMING_SNAKE_CASE					)
           return doc_ids, retrieved_doc_embeds, doc_dicts




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None					)						->	List[Any]:
           if index is not None and index.is_initialized() and len(_SCREAMING_SNAKE_CASE					) > 0:
               raise ValueError(
                   '''When using Ray for distributed fine-tuning, '''
                   '''you\'ll need to provide the paths instead, '''
                   '''as the dataset and the index are loaded '''
                   '''separately. More info in examples/rag/use_own_knowledge_dataset.py '''					)

           super().__init__(
               _SCREAMING_SNAKE_CASE       ,	question_encoder_tokenizer=_SCREAMING_SNAKE_CASE       ,	generator_tokenizer=_SCREAMING_SNAKE_CASE       ,	index=_SCREAMING_SNAKE_CASE       ,	init_retrieval=_SCREAMING_SNAKE_CASE       ,	)

           A_      =  retrieval_workers
           A_      =  question_encoder_tokenizer
           A_      =  generator_tokenizer
           if len(self.retrieval_workers					) > 0:
               ray.get(
                   [
                       worker.create_rag_retriever.remote(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
                       for worker in self.retrieval_workers
                   ]					)

       def    __A			( self					)						->	List[Any]:
           logger.info('''initializing retrieval'''					)

           if len(self.retrieval_workers					) > 0:
               ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]					)
           else:
               # Non-distributed training. Load index into this same process.
               self.index.init_index()

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	Optional[int]:
           if len(self.retrieval_workers					) > 0:
               # Select a random retrieval actor.
               A_      =  self.retrieval_workers[random.randint(0       ,	len(self.retrieval_workers					) - 1					)]
               A_  ,A_  ,A_      =  ray.get(
                   random_worker.retrieve.remote(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)					)
           else:
               A_  ,A_      =  self._main_retrieve(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
               A_      =  self.index.get_doc_dicts(_SCREAMING_SNAKE_CASE					)
           return retrieved_doc_embeds, doc_ids, doc_dicts

       @classmethod
       def    __A			( cls       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None       ,	**_SCREAMING_SNAKE_CASE					)						->	List[Any]:
           return super(_SCREAMING_SNAKE_CASE       ,	cls					).get_tokenizers(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)

       @classmethod
       def    __A			( cls       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None       ,	**_SCREAMING_SNAKE_CASE					)						->	int:
           A_      =  kwargs.pop('''config'''       ,	_SCREAMING_SNAKE_CASE					) or RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
           A_      =  RagTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE       ,	config=_SCREAMING_SNAKE_CASE					)
           A_      =  rag_tokenizer.question_encoder
           A_      =  rag_tokenizer.generator

           if indexed_dataset is not None:
               A_      =  '''custom'''
               A_      =  CustomHFIndex(config.retrieval_vector_size       ,	_SCREAMING_SNAKE_CASE					)
           else:
               A_      =  cls._build_index(_SCREAMING_SNAKE_CASE					)

           return cls(
               _SCREAMING_SNAKE_CASE       ,	question_encoder_tokenizer=_SCREAMING_SNAKE_CASE       ,	generator_tokenizer=_SCREAMING_SNAKE_CASE       ,	retrieval_workers=_SCREAMING_SNAKE_CASE       ,	index=_SCREAMING_SNAKE_CASE       ,	)

       def    __A			( self					)						->	Optional[int]:
           logger.info('''re-loading the new dataset with embeddings'''					)
           # access from the training loop

           ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]					)

           # build the index object again
           A_      =  self._build_index(self.config					)

           ray.get(
               [
                   worker.create_rag_retriever.remote(
                       self.config       ,	self.question_encoder_tokenizer       ,	self.generator_tokenizer       ,	_SCREAMING_SNAKE_CASE					)
                   for worker in self.retrieval_workers
               ]					)






