


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       lowercase_		(		_A			:    Dict[str, torch.Tensor]						):

      """simple docstring"""




      lowerCamelCase__		:				Any    =  []
      lowerCamelCase__		:				List[Any]    =  []
      lowerCamelCase__		:				Optional[Any]    =  []

      for rt in rc.restypes:
            lowerCamelCase__		:				List[str]    =  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(__a						)}
            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__		:				Union[str, Any]    =  torch.tensor(
          __a    ,					dtype=torch.intaa    ,					device=protein["aatype"].device    ,					)
      lowerCamelCase__		:				int    =  torch.tensor(
          __a    ,					dtype=torch.intaa    ,					device=protein["aatype"].device    ,					)
      lowerCamelCase__		:				Any    =  torch.tensor(
          __a    ,					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__		:				List[str]    =  restype_atomaa_to_atomaa[protein_aatype]
      lowerCamelCase__		:				Optional[int]    =  restype_atomaa_mask[protein_aatype]

      lowerCamelCase__		:				Optional[int]    =  residx_atomaa_mask
      lowerCamelCase__		:				int    =  residx_atomaa_to_atomaa.long()

      # create the gather indices for mapping back
      lowerCamelCase__		:				Any    =  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__		:				Optional[int]    =  rc.restype_atoa[restype_letter]
            lowerCamelCase__		:				Dict    =  rc.residue_atoms[restype_name]
            for atom_name in atom_names:
                  lowerCamelCase__		:				Optional[Any]    =  rc.atom_order[atom_name]
                  lowerCamelCase__		:				Tuple    =  1

      lowerCamelCase__		:				Dict    =  restype_atomaa_mask[protein_aatype]
      lowerCamelCase__		:				Any    =  residx_atomaa_mask

      return protein


def       lowercase_		(		_A			:    Dict[str, torch.Tensor]						):

      """simple docstring"""




      lowerCamelCase__		:				Any    =  tree_map(lambda _A						: torch.tensor(__a    ,					device=batch["aatype"].device						)    ,					__a    ,					np.ndarray						)
      lowerCamelCase__		:				Dict    =  tensor_tree_map(lambda _A						: np.array(__a						)    ,					make_atomaa_masks(__a						)						)
      return out









def    __lowerCamelCase					(							__a   :str    )		->				list:






		"""simple docstring"""

		A__        =   [0] * len(__a    )

		for i in range(1    ,     len(__a    )    ):
				# use last results for better performance - dynamic programming
				A__        =   prefix_result[i - 1]
				while j > 0 and input_string[i] != input_string[j]:
						A__        =   prefix_result[j - 1]

				if input_string[i] == input_string[j]:
						j += 1
				A__        =   j

		return prefix_result







def    __lowerCamelCase					(							__a   :str    )		->				int:






		"""simple docstring"""

		return max(prefix_function(__a    )    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()



