





"""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     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						torch.Tensor:




      """simple docstring"""

      ...




@overload
def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						Tuple[torch.Tensor, torch.Tensor]:




      """simple docstring"""

      ...




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						str:




      """simple docstring"""

      _UpperCamelCase					:		int  =				aatype == rc.restype_order["G"]
      _UpperCamelCase					:		Optional[int]  =				rc.atom_order["CA"]
      _UpperCamelCase					:		Tuple  =				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					:		List[str]  =				torch.where(
                lowercase_						,all_atom_masks[..., ca_idx]						,all_atom_masks[..., cb_idx]						,)
            return pseudo_beta, pseudo_beta_mask
      else:
            return pseudo_beta




def     lowercase__			(    lowercase_						,lowercase_	)    ->						torch.Tensor:




      """simple docstring"""

      _UpperCamelCase					:		str  =				batched_gather(
          lowercase_						,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     lowercase__			(    lowercase_	)    ->						torch.Tensor:




      """simple docstring"""

      _UpperCamelCase					:		int  =				template_feats["template_aatype"]
      _UpperCamelCase					:		List[str]  =				template_feats["template_torsion_angles_sin_cos"]
      _UpperCamelCase					:		int  =				template_feats["template_alt_torsion_angles_sin_cos"]
      _UpperCamelCase					:		Any  =				template_feats["template_torsion_angles_mask"]
      _UpperCamelCase					:		Optional[Any]  =				torch.cat(
          [
              nn.functional.one_hot(lowercase_						,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     lowercase__			(    lowercase_						,lowercase_						,lowercase_						,lowercase_						,lowercase_ = False						,lowercase_ = 1e-20						,lowercase_ = 1e8						,)    ->						torch.Tensor:




      """simple docstring"""

      _UpperCamelCase					:		Union[str, Any]  =				batch["template_pseudo_beta_mask"]
      _UpperCamelCase					:		List[str]  =				template_mask[..., None] * template_mask[..., None, :]

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

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

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

      _UpperCamelCase					:		Union[str, Any]  =				batch["template_aatype"].shape[-1]
      to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]						,lowercase_						,-1						,-1	)	)
      to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]						,-1						,lowercase_						,-1	)	)

      _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		Any  =				[rc.atom_order[a] for a in ["N", "CA", "C"]]
      _UpperCamelCase					:		Optional[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=lowercase_						,)
      _UpperCamelCase					:		Tuple  =				rigids.get_trans()[..., None, :, :]
      _UpperCamelCase					:		Tuple  =				rigids[..., None].invert_apply(lowercase_	)

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

      _UpperCamelCase					:		List[str]  =				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					:		Optional[int]  =				inv_distance_scalar * template_mask_ad
      _UpperCamelCase					:		Union[str, Any]  =				rigid_vec * inv_distance_scalar[..., None]

      if not use_unit_vector:
            _UpperCamelCase					:		Optional[int]  =				unit_vector * 0.0

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

      _UpperCamelCase					:		int  =				torch.cat(lowercase_						,dim=-1	)
      _UpperCamelCase					:		Tuple  =				act * template_mask_ad[..., None]

      return act




def     lowercase__			(    lowercase_	)    ->						torch.Tensor:




      """simple docstring"""

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




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_						,lowercase_						,)    ->						Rigid:




      """simple docstring"""

      _UpperCamelCase					:		Optional[Any]  =				rrgdf[aatype, ...]

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

      _UpperCamelCase					:		Any  =				alpha.new_zeros((*((1,) * len(alpha.shape[:-1]	)), 2)	)
      _UpperCamelCase					:		Optional[int]  =				1

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

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

      _UpperCamelCase					:		List[str]  =				default_r.compose(Rigid(Rotation(rot_mats=lowercase_	)						,lowercase_	)	)

      _UpperCamelCase					:		List[str]  =				all_frames[..., 5]
      _UpperCamelCase					:		Optional[int]  =				all_frames[..., 6]
      _UpperCamelCase					:		Dict  =				all_frames[..., 7]

      _UpperCamelCase					:		str  =				all_frames[..., 4]
      _UpperCamelCase					:		int  =				chia_frame_to_bb.compose(lowercase_	)
      _UpperCamelCase					:		Optional[Any]  =				chia_frame_to_bb.compose(lowercase_	)
      _UpperCamelCase					:		Tuple  =				chia_frame_to_bb.compose(lowercase_	)

      _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					:		Optional[Any]  =				r[..., None].compose(lowercase_	)

      return all_frames_to_global




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_						,lowercase_						,lowercase_						,lowercase_						,)    ->						torch.Tensor:




      """simple docstring"""

      _UpperCamelCase					:		List[str]  =				group_idx[aatype, ...]

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

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

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

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

      # [*, N, 14, 3]
      _UpperCamelCase					:		List[Any]  =				lit_positions[aatype, ...]
      _UpperCamelCase					:		Optional[Any]  =				t_atoms_to_global.apply(lowercase_	)
      _UpperCamelCase					:		Tuple  =				pred_positions * atom_mask

      return pred_positions







"""simple docstring"""


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_reformer import ReformerTokenizer
else:
  lowerCamelCase__      =      None


lowerCamelCase__      =      logging.get_logger(__name__)


lowerCamelCase__      =      "▁"

lowerCamelCase__      =      {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase__      =      {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

lowerCamelCase__      =      {
    "google/reformer-crime-and-punishment": 52_4288,
}



class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:List[str]		   =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__							:Any		   =    PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__							:List[Any]		   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__							:List[Any]		   =    ["input_ids", "attention_mask"]
  SCREAMING_SNAKE_CASE__							:str		   =    ReformerTokenizer

  def __init__(						self      :				List[str]    ,      __a      :				str=None    ,      __a      :				List[Any]=None    ,      __a      :				Optional[int]="</s>"    ,      __a      :				str="<unk>"    ,      __a      :				Dict=[]    ,      **__a      :				Tuple    ,      )						-> Optional[int]:
        super().__init__(
            __a    ,      tokenizer_file=__a    ,      eos_token=__a    ,      unk_token=__a    ,      additional_special_tokens=__a    ,      **__a    ,      )

        _UpperCamelCase					:		Dict  =				vocab_file
        _UpperCamelCase					:		Optional[Any]  =				False if not self.vocab_file else True


  def       __SCREAMING_SNAKE_CASE (						self      :				Dict    ,      __a      :				str    ,      __a      :				Optional[str] = None		)						-> Tuple[str]:
        if not self.can_save_slow_tokenizer:
              raise ValueError(
                  "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                  "tokenizer."		)

        if not os.path.isdir(__a		):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
              return
        _UpperCamelCase					:		Optional[Any]  =				os.path.join(
            __a    ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

        if os.path.abspath(self.vocab_file		) != os.path.abspath(__a		):
              copyfile(self.vocab_file    ,      __a		)

        return (out_vocab_file,)
