





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  __UpperCAmelCase(   lowercase_   ):
       _lowerCamelCase				:  List[str]						=		[]
       _lowerCamelCase				:  Any						=		[]
       _lowerCamelCase				:  Optional[int]						=		[]

       for rt in rc.restypes:
              _lowerCamelCase				:  Dict						=		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				:  int						=		{name: i for i, name in enumerate(lowercase_   )}
              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				:  Tuple						=		torch.tensor(
           lowercase_			,     dtype=torch.intaa			,     device=protein['''aatype'''].device			,     )
       _lowerCamelCase				:  int						=		torch.tensor(
           lowercase_			,     dtype=torch.intaa			,     device=protein['''aatype'''].device			,     )
       _lowerCamelCase				:  Tuple						=		torch.tensor(
           lowercase_			,     dtype=torch.floataa			,     device=protein['''aatype'''].device			,     )
       _lowerCamelCase				:  int						=		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				:  Union[str, Any]						=		restype_atomaa_to_atomaa[protein_aatype]
       _lowerCamelCase				:  str						=		restype_atomaa_mask[protein_aatype]

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

       # create the gather indices for mapping back
       _lowerCamelCase				:  int						=		restype_atomaa_to_atomaa[protein_aatype]
       _lowerCamelCase				:  Union[str, Any]						=		residx_atomaa_to_atomaa.long()

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

       _lowerCamelCase				:  str						=		restype_atomaa_mask[protein_aatype]
       _lowerCamelCase				:  int						=		residx_atomaa_mask

       return protein




def  __UpperCAmelCase(   lowercase_   ):
       _lowerCamelCase				:  List[str]						=		tree_map(lambda lowercase_   : torch.tensor(lowercase_			,     device=batch['''aatype'''].device   )			,     lowercase_			,     np.ndarray   )
       _lowerCamelCase				:  Any						=		tensor_tree_map(lambda lowercase_   : np.array(lowercase_   )			,     make_atomaa_masks(lowercase_   )   )
       return out








import copy
import random

from transformers import CLIPTokenizer







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				*a__   ,				**a__):



             """simple docstring"""





             super().__init__(*a__   ,				**a__)
             _lowerCamelCase				:  Optional[Any]						=		{}


      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				**a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		super().add_tokens(a__   ,				*a__   ,				**a__)
             if num_added_tokens == 0:
                    raise ValueError(
                        F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
                        ''' `placeholder_token` that is not already in the tokenizer.''')


      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				a__=1   ,				**a__):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		[]
             if num_vec_per_token == 1:
                    self.try_adding_tokens(a__   ,				*a__   ,				**a__)
                    output.append(a__)
             else:
                    _lowerCamelCase				:  Dict						=		[]
                    for i in range(a__):
                           _lowerCamelCase				:  Optional[Any]						=		placeholder_token + F"""_{i}"""
                           self.try_adding_tokens(a__   ,				*a__   ,				**a__)
                           output.append(a__)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
             for token in self.token_map:
                    if token in placeholder_token:
                           raise ValueError(
                               F"""The tokenizer already has placeholder token {token} that can get confused with"""
                               F""" {placeholder_token}keep placeholder tokens independent""")
             _lowerCamelCase				:  int						=		output


      def 					__snake_case      (		self   ,				a__   ,				a__=False   ,				a__=1.0):



             """simple docstring"""





             if isinstance(a__   ,				a__):
                    _lowerCamelCase				:  Tuple						=		[]
                    for i in range(len(a__)):
                           output.append(self.replace_placeholder_tokens_in_text(text[i]   ,				vector_shuffle=a__))
                    return output
             for placeholder_token in self.token_map:
                    if placeholder_token in text:
                           _lowerCamelCase				:  Union[str, Any]						=		self.token_map[placeholder_token]
                           _lowerCamelCase				:  Optional[Any]						=		tokens[: 1 + int(len(a__) * prop_tokens_to_load)]
                           if vector_shuffle:
                                  _lowerCamelCase				:  int						=		copy.copy(a__)
                                  random.shuffle(a__)
                           _lowerCamelCase				:  List[str]						=		text.replace(a__   ,				''' '''.join(a__))
             return text


      def __call__(		self   ,				a__   ,				*a__   ,				a__=False   ,				a__=1.0   ,				**a__):



             """simple docstring"""





             return super().__call__(
                 self.replace_placeholder_tokens_in_text(
                     a__   ,				vector_shuffle=a__   ,				prop_tokens_to_load=a__)   ,				*a__   ,				**a__   ,				)



      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				a__=False   ,				a__=1.0   ,				**a__):



             """simple docstring"""





             return super().encode(
                 self.replace_placeholder_tokens_in_text(
                     a__   ,				vector_shuffle=a__   ,				prop_tokens_to_load=a__)   ,				*a__   ,				**a__   ,				)

