# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Amber relaxation."""
from typing import Any, Dict, Sequence, Tuple
from openfold.np import protein
from openfold.np.relax import amber_minimize, utils
import numpy as np


class AmberRelaxation(object):
    """Amber relaxation."""
    def __init__(
        self,
        *,
        max_iterations: int,
        tolerance: float,
        stiffness: float,
        exclude_residues: Sequence[int],
        max_outer_iterations: int,
        use_gpu: bool,
    ):
        """Initialize Amber Relaxer.

        Args:
          max_iterations: Maximum number of L-BFGS iterations. 0 means no max.
          tolerance: kcal/mol, the energy tolerance of L-BFGS.
          stiffness: kcal/mol A**2, spring constant of heavy atom restraining
            potential.
          exclude_residues: Residues to exclude from per-atom restraining.
            Zero-indexed.
          max_outer_iterations: Maximum number of violation-informed relax
           iterations. A value of 1 will run the non-iterative procedure used in
           CASP14. Use 20 so that >95% of the bad cases are relaxed. Relax finishes
           as soon as there are no violations, hence in most cases this causes no
           slowdown. In the worst case we do 20 outer iterations.
          use_gpu: Whether to run on GPU
        """

        self._max_iterations = max_iterations
        self._tolerance = tolerance
        self._stiffness = stiffness
        self._exclude_residues = exclude_residues
        self._max_outer_iterations = max_outer_iterations
        self._use_gpu = use_gpu

    def process(
        self, *, prot: protein.Protein, cif_output: bool = False
    ) -> Tuple[str, Dict[str, Any], np.ndarray]:
        """Runs Amber relax on a prediction, adds hydrogens, returns PDB string."""
        out = amber_minimize.run_pipeline(
            prot=prot,
            max_iterations=self._max_iterations,
            tolerance=self._tolerance,
            stiffness=self._stiffness,
            exclude_residues=self._exclude_residues,
            max_outer_iterations=self._max_outer_iterations,
            use_gpu=self._use_gpu,
        )
        min_pos = out["pos"]
        start_pos = out["posinit"]
        rmsd = np.sqrt(np.sum((start_pos - min_pos) ** 2) / start_pos.shape[0])
        debug_data = {
            "initial_energy": out["einit"],
            "final_energy": out["efinal"],
            "attempts": out["min_attempts"],
            "rmsd": rmsd,
        }
        pdb_str = amber_minimize.clean_protein(prot)
        min_pdb = utils.overwrite_pdb_coordinates(pdb_str, min_pos)
        min_pdb = utils.overwrite_b_factors(min_pdb, prot.b_factors)
        utils.assert_equal_nonterminal_atom_types(
            protein.from_pdb_string(min_pdb).atom_mask, prot.atom_mask
        )
        violations = out["structural_violations"][
            "total_per_residue_violations_mask"
        ]

        min_pdb = protein.add_pdb_headers(prot, min_pdb)
        output_str = min_pdb
        if cif_output:
            # TODO the model cif will be missing some metadata like headers (PARENTs and
            #      REMARK with some details of the run, like num of recycles)
            final_prot = protein.from_pdb_string(min_pdb)
            output_str = protein.to_modelcif(final_prot)

        return output_str, debug_data, violations
