



'''simple docstring'''





from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map

def 			a_	(				_lowerCAmelCase			) -> Dict[str, torch.Tensor]:
  __lowerCamelCase			:			Optional[int]       			=					[]
  __lowerCamelCase			:			List[Any]       			=					[]
  __lowerCamelCase			:			List[Any]       			=					[]

  for rt in rc.restypes:
    __lowerCamelCase			:			int       			=					rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
    restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]			)
    __lowerCamelCase			:			Optional[int]       			=					{name: i for i, name in enumerate(_lowerCAmelCase			)}
    restype_atomaa_to_atomaa_list.append(
        [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]			)

    restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]			)

  # Add dummy mapping for restype 'UNK'
  restype_atomaa_to_atomaa_list.append([0] * 14			)
  restype_atomaa_to_atomaa_list.append([0] * 37			)
  restype_atomaa_mask_list.append([0.0] * 14			)

  __lowerCamelCase			:			List[Any]       			=					torch.tensor(
      _lowerCAmelCase			,dtype=torch.intaa			,device=protein['aatype'].device			,)
  __lowerCamelCase			:			Tuple       			=					torch.tensor(
      _lowerCAmelCase			,dtype=torch.intaa			,device=protein['aatype'].device			,)
  __lowerCamelCase			:			Any       			=					torch.tensor(
      _lowerCAmelCase			,dtype=torch.floataa			,device=protein['aatype'].device			,)
  __lowerCamelCase			:			List[Any]       			=					protein['aatype'].to(torch.long			)

  # create the mapping for (residx, atom14) --> atom37, i.e. an array
  # with shape (num_res, 14) containing the atom37 indices for this protein
  __lowerCamelCase			:			Dict       			=					restype_atomaa_to_atomaa[protein_aatype]
  __lowerCamelCase			:			List[Any]       			=					restype_atomaa_mask[protein_aatype]

  __lowerCamelCase			:			int       			=					residx_atomaa_mask
  __lowerCamelCase			:			List[Any]       			=					residx_atomaa_to_atomaa.long()

  # create the gather indices for mapping back
  __lowerCamelCase			:			Optional[int]       			=					restype_atomaa_to_atomaa[protein_aatype]
  __lowerCamelCase			:			Optional[int]       			=					residx_atomaa_to_atomaa.long()

  # create the corresponding mask
  __lowerCamelCase			:			Optional[int]       			=					torch.zeros([21, 37]			,dtype=torch.floataa			,device=protein['aatype'].device			)
  for restype, restype_letter in enumerate(rc.restypes			):
    __lowerCamelCase			:			Union[str, Any]       			=					rc.restype_atoa[restype_letter]
    __lowerCamelCase			:			Optional[Any]       			=					rc.residue_atoms[restype_name]
    for atom_name in atom_names:
      __lowerCamelCase			:			Optional[int]       			=					rc.atom_order[atom_name]
      __lowerCamelCase			:			List[str]       			=					1

  __lowerCamelCase			:			Any       			=					restype_atomaa_mask[protein_aatype]
  __lowerCamelCase			:			Optional[int]       			=					residx_atomaa_mask

  return protein


def 			a_	(				_lowerCAmelCase			) -> Dict[str, np.ndarray]:
  __lowerCamelCase			:			List[Any]       			=					tree_map(lambda _lowerCAmelCase			: torch.tensor(_lowerCAmelCase			,device=batch['aatype'].device			)			,_lowerCAmelCase			,np.ndarray			)
  __lowerCamelCase			:			Optional[Any]       			=					tensor_tree_map(lambda _lowerCAmelCase			: np.array(_lowerCAmelCase			)			,make_atomaa_masks(_lowerCAmelCase			)			)
  return out











'''simple docstring'''





import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase											=		logging.get_logger(__name__)

_UpperCamelCase											=		{'vocab_file': 'vocab.json'}

_UpperCamelCase											=		{
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

_UpperCamelCase											=		{'mgp-str': 27}






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =VOCAB_FILES_NAMES
  a_				   =PRETRAINED_VOCAB_FILES_MAP
  a_				   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




  def __init__(					self :      Optional[Any]     ,      _a :      Optional[Any]     ,      _a :      Any="[GO]"     ,      _a :      Optional[Any]="[GO]"     ,      _a :      Optional[int]="[s]"     ,      _a :      Optional[int]="[GO]"     ,      **_a :      Union[str, Any]  )   ->   Optional[int]:
    super().__init__(
        unk_token=_a     ,      bos_token=_a     ,      eos_token=_a     ,      pad_token=_a     ,      **_a     ,      )

    with open(_a     ,      encoding='utf-8'  ) as vocab_handle:
      __lowerCamelCase			:			List[str]       			=					json.load(_a  )
    __lowerCamelCase			:			Tuple       			=					{v: k for k, v in self.vocab.items()}




  @property
  def 		_lowercase	(					self :      Union[str, Any]  )   ->   List[str]:
    return len(self.vocab  )




  def 		_lowercase	(					self :      Union[str, Any]  )   ->   Tuple:
    return dict(self.vocab     ,      **self.added_tokens_encoder  )




  def 		_lowercase	(					self :      Dict     ,      _a :      Tuple  )   ->   int:
    __lowerCamelCase			:			Optional[Any]       			=					[]
    for s in text:
      char_tokens.extend(_a  )
    return char_tokens




  def 		_lowercase	(					self :      Optional[Any]     ,      _a :      Any  )   ->   Dict:
    return self.vocab.get(_a     ,      self.vocab.get(self.unk_token  )  )




  def 		_lowercase	(					self :      List[Any]     ,      _a :      Dict  )   ->   Optional[int]:
    return self.decoder.get(_a  )




  def 		_lowercase	(					self :      Union[str, Any]     ,      _a :      str     ,      _a :      Optional[str] = None  )   ->   Tuple[str]:
    if not os.path.isdir(_a  ):
      logger.error('Vocabulary path ({}) should be a directory'.format(_a  )  )
      return
    __lowerCamelCase			:			List[Any]       			=					os.path.join(
        _a     ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

    with open(_a     ,      'w'     ,      encoding='utf-8'  ) as f:
      f.write(json.dumps(self.vocab     ,      indent=2     ,      sort_keys=_a     ,      ensure_ascii=_a  ) + '\n'  )

    return (vocab_file,)






