

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       (					__A ,       __A ,       __A	):
    ...





@overload
def 							__a       (					__A ,       __A ,       __A	):
    ...





def 							__a       (					__A ,       __A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						aatype == rc.restype_order["G"]
    SCREAMING_SNAKE_CASE__					     =						rc.atom_order["CA"]
    SCREAMING_SNAKE_CASE__					     =						rc.atom_order["CB"]
    SCREAMING_SNAKE_CASE__					     =						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:
        SCREAMING_SNAKE_CASE__					     =						torch.where(
            __A ,       all_atom_masks[..., ca_idx] ,       all_atom_masks[..., cb_idx] ,       )
        return pseudo_beta, pseudo_beta_mask
    else:
        return pseudo_beta





def 							__a       (					__A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						batched_gather(
        __A ,       batch["residx_atom37_to_atom14"] ,       dim=-2 ,       no_batch_dims=len(atomaa.shape[:-2]	) ,       )

    SCREAMING_SNAKE_CASE__					     =						atomaa_data * batch["atom37_atom_exists"][..., None]

    return atomaa_data





def 							__a       (					__A	):
    SCREAMING_SNAKE_CASE__					     =						template_feats["template_aatype"]
    SCREAMING_SNAKE_CASE__					     =						template_feats["template_torsion_angles_sin_cos"]
    SCREAMING_SNAKE_CASE__					     =						template_feats["template_alt_torsion_angles_sin_cos"]
    SCREAMING_SNAKE_CASE__					     =						template_feats["template_torsion_angles_mask"]
    SCREAMING_SNAKE_CASE__					     =						torch.cat(
        [
            nn.functional.one_hot(__A ,       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       (					__A ,       __A ,       __A ,       __A ,       __A = False ,       __A = 1E-2_0 ,       __A = 1E8 ,       ):
    SCREAMING_SNAKE_CASE__					     =						batch["template_pseudo_beta_mask"]
    SCREAMING_SNAKE_CASE__					     =						template_mask[..., None] * template_mask[..., None, :]

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

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

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

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

    SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						[rc.atom_order[a] for a in ["N", "CA", "C"]]
    SCREAMING_SNAKE_CASE__					     =						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=__A ,       )
    SCREAMING_SNAKE_CASE__					     =						rigids.get_trans()[..., None, :, :]
    SCREAMING_SNAKE_CASE__					     =						rigids[..., None].invert_apply(__A	)

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

    SCREAMING_SNAKE_CASE__					     =						batch["template_all_atom_mask"]
    SCREAMING_SNAKE_CASE__					     =						t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
    SCREAMING_SNAKE_CASE__					     =						template_mask[..., None] * template_mask[..., None, :]

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

    if not use_unit_vector:
        SCREAMING_SNAKE_CASE__					     =						unit_vector * 0.0

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

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

    return act





def 							__a       (					__A	):
    SCREAMING_SNAKE_CASE__					     =						nn.functional.one_hot(batch["extra_msa"] ,       23	)
    SCREAMING_SNAKE_CASE__					     =						[
        msa_ahot,
        batch["extra_has_deletion"].unsqueeze(-1	),
        batch["extra_deletion_value"].unsqueeze(-1	),
    ]
    return torch.cat(__A ,       dim=-1	)





def 							__a       (					__A ,       __A ,       __A ,       __A ,       ):
    SCREAMING_SNAKE_CASE__					     =						rrgdf[aatype, ...]

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

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

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

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

    SCREAMING_SNAKE_CASE__					     =						default_r.compose(Rigid(Rotation(rot_mats=__A	) ,       __A	)	)

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

    SCREAMING_SNAKE_CASE__					     =						all_frames[..., 4]
    SCREAMING_SNAKE_CASE__					     =						chia_frame_to_bb.compose(__A	)
    SCREAMING_SNAKE_CASE__					     =						chia_frame_to_bb.compose(__A	)
    SCREAMING_SNAKE_CASE__					     =						chia_frame_to_bb.compose(__A	)

    SCREAMING_SNAKE_CASE__					     =						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 ,       )

    SCREAMING_SNAKE_CASE__					     =						r[..., None].compose(__A	)

    return all_frames_to_global





def 							__a       (					__A ,       __A ,       __A ,       __A ,       __A ,       __A ,       ):
    SCREAMING_SNAKE_CASE__					     =						group_idx[aatype, ...]

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

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

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

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

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

    return pred_positions





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


A__        =			logging.get_logger(__name__)

A__        =			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

A__        =			{
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "allenai/longformer-base-4096": (
            "https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
    },
}

A__        =			{
    "allenai/longformer-base-4096": 4096,
    "allenai/longformer-large-4096": 4096,
    "allenai/longformer-large-4096-finetuned-triviaqa": 4096,
    "allenai/longformer-base-4096-extra.pos.embd.only": 4096,
    "allenai/longformer-large-4096-extra.pos.embd.only": 4096,
}







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: str										=      VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__				: Optional[int]										=      PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__				: Any										=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__				: List[str]										=      ["""input_ids""", """attention_mask"""]
  SCREAMING_SNAKE_CASE__				: List[str]										=      LongformerTokenizer



  def __init__(	self    :				List[Any]     ,  A_    :				Any=None     ,  A_    :				Union[str, Any]=None     ,  A_    :				Optional[int]=None     ,  A_    :				int="replace"     ,  A_    :				Any="<s>"     ,  A_    :				int="</s>"     ,  A_    :				str="</s>"     ,  A_    :				Optional[Any]="<s>"     ,  A_    :				Optional[int]="<unk>"     ,  A_    :				Optional[int]="<pad>"     ,  A_    :				List[str]="<mask>"     ,  A_    :				Optional[int]=False     ,  A_    :				List[str]=True     ,  **A_    :				List[str]     ,  )					-> Any:
      super().__init__(
          A_     ,  A_     ,  tokenizer_file=A_     ,  errors=A_     ,  bos_token=A_     ,  eos_token=A_     ,  sep_token=A_     ,  cls_token=A_     ,  unk_token=A_     ,  pad_token=A_     ,  mask_token=A_     ,  add_prefix_space=A_     ,  trim_offsets=A_     ,  **A_     ,  )

      SCREAMING_SNAKE_CASE__					     =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
      if pre_tok_state.get("add_prefix_space"     ,  A_	) != add_prefix_space:
          SCREAMING_SNAKE_CASE__					     =						getattr(A_     ,  pre_tok_state.pop("type"	)	)
          SCREAMING_SNAKE_CASE__					     =						add_prefix_space
          SCREAMING_SNAKE_CASE__					     =						pre_tok_class(**A_	)

      SCREAMING_SNAKE_CASE__					     =						add_prefix_space

      SCREAMING_SNAKE_CASE__					     =						"post_processor"
      SCREAMING_SNAKE_CASE__					     =						getattr(self.backend_tokenizer     ,  A_     ,  A_	)
      if tokenizer_component_instance:
          SCREAMING_SNAKE_CASE__					     =						json.loads(tokenizer_component_instance.__getstate__()	)

          # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
          if "sep" in state:
              SCREAMING_SNAKE_CASE__					     =						tuple(state["sep"]	)
          if "cls" in state:
              SCREAMING_SNAKE_CASE__					     =						tuple(state["cls"]	)

          SCREAMING_SNAKE_CASE__					     =						False

          if state.get("add_prefix_space"     ,  A_	) != add_prefix_space:
              SCREAMING_SNAKE_CASE__					     =						add_prefix_space
              SCREAMING_SNAKE_CASE__					     =						True

          if state.get("trim_offsets"     ,  A_	) != trim_offsets:
              SCREAMING_SNAKE_CASE__					     =						trim_offsets
              SCREAMING_SNAKE_CASE__					     =						True

          if changes_to_apply:
              SCREAMING_SNAKE_CASE__					     =						getattr(A_     ,  state.pop("type"	)	)
              SCREAMING_SNAKE_CASE__					     =						component_class(**A_	)
              setattr(self.backend_tokenizer     ,  A_     ,  A_	)



  @property
  def      _UpperCamelCase    (	self    :				Dict	)					-> str:
      if self._mask_token is None:
          if self.verbose:
              logger.error("Using mask_token, but it is not set yet."	)
          return None
      return str(self._mask_token	)



  @mask_token.setter
  def      _UpperCamelCase    (	self    :				int     ,  A_    :				Optional[Any]	)					-> int:
      SCREAMING_SNAKE_CASE__					     =						AddedToken(A_     ,  lstrip=A_     ,  rstrip=A_	) if isinstance(A_     ,  A_	) else value
      SCREAMING_SNAKE_CASE__					     =						value



  def      _UpperCamelCase    (	self    :				int     ,  *A_    :				int     ,  **A_    :				Dict	)					-> BatchEncoding:
      SCREAMING_SNAKE_CASE__					     =						kwargs.get("is_split_into_words"     ,  A_	)
      assert self.add_prefix_space or not is_split_into_words, (
          f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*A_     ,  **A_	)



  def      _UpperCamelCase    (	self    :				Dict     ,  *A_    :				int     ,  **A_    :				Union[str, Any]	)					-> BatchEncoding:
      SCREAMING_SNAKE_CASE__					     =						kwargs.get("is_split_into_words"     ,  A_	)

      assert self.add_prefix_space or not is_split_into_words, (
          f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*A_     ,  **A_	)



  def      _UpperCamelCase    (	self    :				int     ,  A_    :				str     ,  A_    :				Optional[str] = None	)					-> Tuple[str]:
      SCREAMING_SNAKE_CASE__					     =						self._tokenizer.model.save(A_     ,  name=A_	)
      return tuple(A_	)



  def      _UpperCamelCase    (	self    :				Tuple     ,  A_    :				List[str]     ,  A_    :				str=None	)					-> List[str]:
      SCREAMING_SNAKE_CASE__					     =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
          return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







  def      _UpperCamelCase    (	self    :				List[str]     ,  A_    :				List[int]     ,  A_    :				Optional[List[int]] = None	)					-> List[int]:
      SCREAMING_SNAKE_CASE__					     =						[self.sep_token_id]
      SCREAMING_SNAKE_CASE__					     =						[self.cls_token_id]

      if token_ids_a is None:
          return len(cls + token_ids_a + sep	) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]


