diff --git a/.gitignore b/.gitignore index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..dd8ef0fcdca6f691db7d9786ec30e528e8b381b1 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1,20 @@ +*.wandb +debug.log +debug-internal.log +debug-core.log +*.log + +# Weights & Biases +wandb/ +**/wandb/ +# Build artifacts +models/matris/build/ +*.so +*.o + +# Compiled/build artifacts +models/matris/build/ +models/matris/**/*.so +models/matris/**/*.o +*.so +*.o diff --git a/models/matris/README.md b/models/matris/README.md new file mode 100644 index 0000000000000000000000000000000000000000..48b2e1ba20604f30120150bdcee442cdfee17912 --- /dev/null +++ b/models/matris/README.md @@ -0,0 +1,5 @@ +# Introduction + +To train matris, run the run_matris.py script, there's also a ddp version called run_matris_ddp.py + +To eval matris, run the eval_matris.py \ No newline at end of file diff --git a/models/matris/__pycache__/matris_dataset.cpython-310.pyc b/models/matris/__pycache__/matris_dataset.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c8fb4a861781f9eb3baf2903671a6f9a873fa65c Binary files /dev/null and b/models/matris/__pycache__/matris_dataset.cpython-310.pyc differ diff --git a/models/matris/bench_matris_speed_single.py b/models/matris/bench_matris_speed_single.py new file mode 100644 index 0000000000000000000000000000000000000000..0927cf0dc88906a96c697a17342df6bfa447faa5 --- /dev/null +++ b/models/matris/bench_matris_speed_single.py @@ -0,0 +1,227 @@ +#!/usr/bin/env python3 +""" +Benchmark MatRIS speed (energy + forces) on a single structure ~96 atoms. + +- Selects a structure from an extxyz file (closest natoms to target, default 96) +- Loads a pretrained MatRIS model (matris_10m_oam or matris_10m_mp) +- Converts the structure to a MatRIS graph (optional to include conversion time) +- Times the forward pass that returns energy + forces (forces require autograd) + +Notes +- Do NOT wrap the model call in torch.no_grad(): MatRIS computes forces via autograd. +- For reliable repeated timing, we refresh the coordinate tensor each iteration to be a leaf + requiring grad (avoids "does not require grad" and avoids graph growth). +""" + +from __future__ import annotations +import argparse +import time +from pathlib import Path +from typing import Any + +import numpy as np +from ase.io import read +from ase.atoms import Atoms + +def pick_index_by_natoms(atoms_list: list[Atoms], target: int) -> int: + best_i = 0 + best_d = 10**9 + for i, a in enumerate(atoms_list): + d = abs(len(a) - target) + if d < best_d: + best_d = d + best_i = i + if best_d == 0: + break + return best_i + +def maybe_cuda_sync(device: str): + if device.startswith("cuda"): + try: + import torch + torch.cuda.synchronize() + except Exception: + pass + +def refresh_grad_coords(graph: Any): + """ + Ensure graph has a fresh leaf coordinate tensor requiring grad. + + MatRIS force computation needs gradients w.r.t. Cartesian coordinates. + Supports dict-like graphs and RadiusGraph-style objects. + """ + import torch + + candidate_keys = [ + "batch_cart_coords", + "cart_coords", + "cartesian_coords", + "pos", + "positions", + "atom_frac_coord", + ] + + # Case 1: dictionary-like graph + if isinstance(graph, dict): + for key in candidate_keys: + if key in graph: + coords = graph[key] + if not torch.is_tensor(coords): + raise TypeError(f"graph['{key}'] is not a torch.Tensor, got {type(coords)}") + graph[key] = coords.detach().clone().requires_grad_(True) + return + + raise KeyError(f"Could not find coordinate tensor in graph keys: {list(graph.keys())}") + + # Case 2: object with attributes, e.g. RadiusGraph + for key in candidate_keys: + if hasattr(graph, key): + coords = getattr(graph, key) + + if torch.is_tensor(coords): + setattr(graph, key, coords.detach().clone().requires_grad_(True)) + return + + # Helpful debug print + attrs = [a for a in dir(graph) if not a.startswith("_")] + raise TypeError( + f"Unsupported graph type for coord refresh: {type(graph)}\n" + f"Available public attributes include:\n{attrs[:80]}" + ) + +def main(): + ap = argparse.ArgumentParser(description="Benchmark MatRIS speed on a single ~96-atom structure.") + ap.add_argument("--xyz", required=True, help="Path to extxyz dataset (or single-structure xyz)") + ap.add_argument("--model-name", default="none", choices=["none", "matris_10m_oam", "matris_10m_mp"]) + ap.add_argument("--device", default=None, help='Device, e.g. "cuda" or "cpu" (default: auto)') + ap.add_argument("--target-natoms", type=int, default=96, help="Pick structure with natoms closest to this (default 96)") + ap.add_argument("--index", default=":", help='ASE selector for candidate pool (default ":" for all)') + ap.add_argument("--pick", choices=["closest", "first", "given"], default="closest", + help="How to pick the structure: closest to target natoms, first, or given index") + ap.add_argument("--given-index", type=int, default=0, help="Used when --pick given") + ap.add_argument("--warmup", type=int, default=50, help="Warmup iterations (default 50)") + ap.add_argument("--iters", type=int, default=300, help="Timed iterations (default 300)") + ap.add_argument("--include-graph", action="store_true", help="Include structure->graph conversion time in each iteration") + ap.add_argument("--disable-ref-energy", action="store_true", + help="Disable MatRIS composition reference energy (mpa AtomRef) during inference") + ap.add_argument( + "--is-conservative", + action=argparse.BooleanOptionalAction, + default=True, + help=( + "Use conservative forces F = -dE/dx. " + "Use --no-is-conservative for direct force prediction." + ), + ) + args = ap.parse_args() + + # device auto + if args.device is None: + try: + import torch + device = "cuda" if torch.cuda.is_available() else "cpu" + except Exception: + device = "cpu" + else: + device = args.device + + # Lazy imports for MatRIS + adaptor + try: + import torch + from pymatgen.io.ase import AseAtomsAdaptor + from matris.model.model import MatRIS + except Exception as e: + raise SystemExit(f"Failed imports. Ensure MatRIS + pymatgen + torch installed.\nOriginal error: {e}") + + atoms_list = read(args.xyz, index=args.index) + if isinstance(atoms_list, Atoms): + atoms_list = [atoms_list] + if len(atoms_list) == 0: + raise SystemExit("No structures read from --xyz/--index.") + + if args.pick == "first": + idx = 0 + elif args.pick == "given": + idx = int(args.given_index) + if idx < 0 or idx >= len(atoms_list): + raise SystemExit(f"--given-index {idx} out of range (0..{len(atoms_list)-1})") + else: + idx = pick_index_by_natoms(atoms_list, args.target_natoms) + + atoms = atoms_list[idx] + n_atoms = len(atoms) + + if args.model_name is None or args.model_name == "none": + print("Training from scratch") + model = MatRIS(is_conservation=args.is_conservative).to(args.device) + else: + print(f"Starting from foundation model: {args.model_name}") + model = MatRIS.load(args.model_name, + device=args.device, + cache_dir="foundation_models",) + # override loaded setting if needed + model.force_stress_head.is_conservation = args.is_conservative + + model.eval() + + if args.disable_ref_energy: + # The head uses model.reference_energy if present + model.reference_energy = None + + adaptor = AseAtomsAdaptor() + + def build_graph(): + structure = adaptor.get_structure(atoms) + g = model.graph_converter(structure).to(device) + return g + + # Prebuild graph for forward-only timing + graph0 = build_graph() + + def one_call(): + # optionally include graph conversion + g = build_graph() if args.include_graph else graph0 + + # ensure grad leaf coords each call + refresh_grad_coords(g) + + out = model([g], task="ef", is_training=False) + # touch outputs so work is not optimized away + e = out["e"][0] + f = out["f"][0] + # small reduction + _ = float(e.detach().cpu()) + float(f.detach().abs().mean().cpu()) + return + + # Warmup + for _ in range(args.warmup): + one_call() + maybe_cuda_sync(device) + + # Timed loop + t0 = time.time() + for _ in range(args.iters): + one_call() + maybe_cuda_sync(device) + t1 = time.time() + + dt = (t1 - t0) / float(args.iters) + ms = dt * 1e3 + us_per_atom = (dt / max(1, n_atoms)) * 1e6 + + print("MatRIS speed benchmark") + print(f" device: {device}") + print(f" model: {args.model_name}") + print(f" conservative: {args.is_conservative}") + print(f" dataset: {Path(args.xyz)}") + print(f" selected index: {idx}") + print(f" natoms: {n_atoms}") + print(f" include graph build: {args.include_graph}") + print(f" ref energy enabled: {not args.disable_ref_energy}") + print(f" warmup iters: {args.warmup}") + print(f" timed iters: {args.iters}") + print(f" avg time / call: {ms:.4f} ms") + print(f" avg time / atom: {us_per_atom:.4f} µs/atom") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/models/matris/eval_matris.py b/models/matris/eval_matris.py new file mode 100644 index 0000000000000000000000000000000000000000..110c010e5f1808a75827c8b1f5282eb6c4055b15 --- /dev/null +++ b/models/matris/eval_matris.py @@ -0,0 +1,334 @@ +from __future__ import annotations + +import argparse +import collections +from pathlib import Path + +import torch +from torch import nn +from torch.utils.data import DataLoader +from tqdm import tqdm + +from matris.model.model import MatRIS +from matris.model.reference_energy import AtomRef +from matris_dataset import ( + MatRISDataset, + matris_collate_fn, + move_matris_batch_to_device, +) + +REPO_ROOT = Path(__file__).resolve().parents[2] + +TEST_PATH = REPO_ROOT / "datasets/xyz/testdata.xyz" +SAVE_DIR = REPO_ROOT / "models/matris/checkpoints_matris" + + + +E0S_DEFAULT = { + 1: 2.467478445, # H + 6: 8.0273263225, # C + 8: 3.334308855, # O + 22: 4.879036065, # Ti +} + +def build_random_init_model( + device: str, + is_conservative: bool, + use_reference_energy: bool, + model_name: str | None = None, +) -> MatRIS: + print("Building RANDOM INIT model") + print(f"Requested model_name: {model_name}") + + if model_name is None or model_name == "none" or model_name == "": + print("Random scratch MatRIS architecture") + model = MatRIS(is_conservation=is_conservative) + else: + print(f"Random foundation architecture: {model_name}") + model = MatRIS.load( + model_name, + device="cpu", + cache_dir="foundation_models", + ) + model.force_stress_head.is_conservation = is_conservative + + if use_reference_energy: + model.reference_energy = build_reference_energy_module(model, E0S_DEFAULT) + else: + model.reference_energy = None + + model = model.to(device) + model.eval() + return model + + +def build_reference_energy_module(model: MatRIS, e0_dict: dict[int, float]) -> AtomRef: + atom_ref = AtomRef(reference_energy="mptrj", is_intensive=model.is_intensive) + + weight = torch.zeros(94, dtype=torch.float32) + for Z, e0 in e0_dict.items(): + weight[Z - 1] = e0 + + state_dict = collections.OrderedDict() + state_dict["weight"] = weight.view(1, 94) + atom_ref.fc.load_state_dict(state_dict) + + atom_ref.fitted = True + for p in atom_ref.parameters(): + p.requires_grad = False + + return atom_ref + + +class MatRISEvalLoss(nn.Module): + def __init__(self): + super().__init__() + self.l1_sum = nn.L1Loss(reduction="sum") + self.l2_sum = nn.MSELoss(reduction="sum") + + def forward(self, pred_forces, ref_forces, pred_energy, ref_energy): + loss_forces_l1 = self.l1_sum(pred_forces, ref_forces) + loss_energy_l1 = self.l1_sum(pred_energy, ref_energy) + + loss_forces_l2 = self.l2_sum(pred_forces, ref_forces) + loss_energy_l2 = self.l2_sum(pred_energy, ref_energy) + + return loss_forces_l1, loss_energy_l1, loss_forces_l2, loss_energy_l2 + + +def load_matris_from_checkpoint( + checkpoint_path: str | Path, + device: str, + is_conservative: bool, + use_reference_energy: bool, + model_name: str | None = None, +) -> MatRIS: + checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + + print(f"Requested model_name: {model_name}") + + if model_name is None or model_name == "none" or model_name == "": + print("Loading scratch MatRIS architecture") + model = MatRIS(is_conservation=is_conservative) + else: + print(f"Loading foundation model architecture: {model_name}") + model = MatRIS.load( + model_name, + device="cpu", + cache_dir="foundation_models", + ) + model.force_stress_head.is_conservation = is_conservative + + if use_reference_energy: + model.reference_energy = build_reference_energy_module(model, E0S_DEFAULT) + else: + model.reference_energy = None + + model.load_state_dict(checkpoint["model_state_dict"], strict=True) + + model = model.to(device) + model.eval() + return model + + +def evaluate(model, loader, loss_fn, use_grad, device, desc="Eval"): + model.eval() + + sum_abs_dF = torch.tensor(0.0, device=device) + sum_abs_dE = torch.tensor(0.0, device=device) + + sum_sq_dF = torch.tensor(0.0, device=device) + sum_sq_dE = torch.tensor(0.0, device=device) + + sum_abs_dE_per_atom = torch.tensor(0.0, device=device) + sum_sq_dE_per_atom = torch.tensor(0.0, device=device) + + total_force_components = torch.tensor(0.0, device=device) + total_systems = torch.tensor(0.0, device=device) + total_atoms = torch.tensor(0.0, device=device) + + # Important: no torch.no_grad(), because conservative forces need autograd. + for batch in tqdm(loader, desc=desc, dynamic_ncols=True): + # There's one sample in testset where this happens + if batch is None: + continue + batch = move_matris_batch_to_device(batch, device=device) + + with torch.set_grad_enabled(use_grad): + out = model(batch["graphs"], task="ef", is_training=False) + + n_atoms_per_graph = out["atoms_per_graph"].float() + natoms_batch = n_atoms_per_graph.sum() + n_systems = n_atoms_per_graph.numel() + + pred_energy = out["e"] + if model.is_intensive: + pred_energy = pred_energy * n_atoms_per_graph + + ref_energy = batch["energy"] + + pred_forces = torch.cat(out["f"], dim=0) + ref_forces = torch.cat(batch["forces"], dim=0) + + loss_forces_l1, loss_energy_l1, loss_forces_l2, loss_energy_l2 = loss_fn( + pred_forces=pred_forces, + ref_forces=ref_forces, + pred_energy=pred_energy, + ref_energy=ref_energy, + ) + + dE = pred_energy.view(-1) - ref_energy.view(-1) + abs_dE = dE.abs() + + total_atoms += natoms_batch.detach() + total_force_components += (3.0 * natoms_batch).detach() + total_systems += torch.tensor(float(n_systems), device=device) + + sum_abs_dF += loss_forces_l1.detach() + sum_abs_dE += loss_energy_l1.detach() + + sum_sq_dF += loss_forces_l2.detach() + sum_sq_dE += loss_energy_l2.detach() + + sum_abs_dE_per_atom += (abs_dE / n_atoms_per_graph).sum().detach() + sum_sq_dE_per_atom += ((dE / n_atoms_per_graph) ** 2).sum().detach() + mae_F = (sum_abs_dF / total_force_components).item() + mae_E_sys = (sum_abs_dE / total_systems).item() + mae_E_atom_weighted = (sum_abs_dE / total_atoms).item() + mae_E_atom_mace = (sum_abs_dE_per_atom / total_systems).item() + + rmse_F = torch.sqrt(sum_sq_dF / total_force_components).item() + rmse_E_sys = torch.sqrt(sum_sq_dE / total_systems).item() + rmse_E_atom_weighted = torch.sqrt(sum_sq_dE / total_atoms).item() + rmse_E_atom_mace = torch.sqrt(sum_sq_dE_per_atom / total_systems).item() + + print() + print("========== MatRIS Evaluation ==========") + print(f"MAE(F components) [eV/A]: {mae_F:.6f}") + print(f"MAE(E per system) [eV]: {mae_E_sys:.6f}") + print(f"MAE(E per atom, weighted) [eV]: {mae_E_atom_weighted:.6f}") + print(f"MAE(E per atom, MACE) [eV]: {mae_E_atom_mace:.6f}") + print(f"MAE(E per atom, MACE) [meV]: {1000.0 * mae_E_atom_mace:.3f}") + print() + print(f"RMSE(F components) [eV/A]: {rmse_F:.6f}") + print(f"RMSE(E per system) [eV]: {rmse_E_sys:.6f}") + print(f"RMSE(E per atom, weighted) [eV]: {rmse_E_atom_weighted:.6f}") + print(f"RMSE(E per atom, MACE) [eV]: {rmse_E_atom_mace:.6f}") + print(f"RMSE(E per atom, MACE) [meV]: {1000.0 * rmse_E_atom_mace:.3f}") + print("=======================================") + + return { + "mae_F": mae_F, + "mae_E_sys": mae_E_sys, + "mae_E_atom_weighted": mae_E_atom_weighted, + "mae_E_atom_mace": mae_E_atom_mace, + "rmse_F": rmse_F, + "rmse_E_sys": rmse_E_sys, + "rmse_E_atom_weighted": rmse_E_atom_weighted, + "rmse_E_atom_mace": rmse_E_atom_mace, + } + + +def parse_args(): + parser = argparse.ArgumentParser(description="Evaluate a MatRIS checkpoint.") + + parser.add_argument("--test_path", type=str, default=str(TEST_PATH)) + parser.add_argument("--checkpoint", type=str, default=None) + parser.add_argument( + "--random_init", + action="store_true", + help="Evaluate a randomly initialized model instead of loading checkpoint weights.", + ) + + parser.add_argument("--batch_size", type=int, default=1) + parser.add_argument("--num_workers", type=int, default=0) + parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu") + + parser.add_argument( + "--is_conservative", + action=argparse.BooleanOptionalAction, + default=True, + help="Use conservative forces.", + ) + + parser.add_argument( + "--use_reference_energy", + action=argparse.BooleanOptionalAction, + default=True, + help="Use the same custom AtomRef reference energies as training.", + ) + + parser.add_argument("--model_name", type=str, default="") + + + + + + return parser.parse_args() + + +def main(): + args = parse_args() + + print(f"Device: {args.device}") + print(f"Checkpoint: {args.checkpoint}") + print(f"Random init: {args.random_init}") + print(f"Test path: {args.test_path}") + print(f"Conservative forces: {args.is_conservative}") + print(f"Use reference energy: {args.use_reference_energy}") + + + if args.random_init: + print("Evaluing random init model") + model = build_random_init_model( + device=args.device, + is_conservative=args.is_conservative, + use_reference_energy=args.use_reference_energy, + model_name=args.model_name, + ) + else: + if args.checkpoint is None: + raise ValueError("--checkpoint is required unless --random_init is used.") + + model = load_matris_from_checkpoint( + checkpoint_path=args.checkpoint, + device=args.device, + is_conservative=args.is_conservative, + use_reference_energy=args.use_reference_energy, + model_name=args.model_name, + ) + + + use_grad = model.force_stress_head.is_conservation + + print(f'use_grad is: {use_grad}') + + # NOTE Try a random init model. + # print("Running random Matrsi model") + # model = MatRIS(is_conservation=args.is_conservative).to(args.device) + # model.reference_energy = build_reference_energy_module(model, E0S_DEFAULT).to(args.device) + + test_dataset = MatRISDataset(args.test_path, model=model) + + test_loader = DataLoader( + test_dataset, + batch_size=args.batch_size, + shuffle=False, + num_workers=args.num_workers, + collate_fn=matris_collate_fn, + ) + + loss_fn = MatRISEvalLoss() + + evaluate( + model=model, + loader=test_loader, + loss_fn=loss_fn, + use_grad = use_grad, + device=args.device, + desc="MatRIS Eval", + ) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/models/matris/example/cif_file/demo.cif b/models/matris/example/cif_file/demo.cif new file mode 100644 index 0000000000000000000000000000000000000000..6d0d500a627994dc35ad98fc155caad87d3f5618 --- /dev/null +++ b/models/matris/example/cif_file/demo.cif @@ -0,0 +1,36 @@ +# generated using pymatgen +data_Pu2Si3 +_symmetry_space_group_name_H-M 'P 1' +_cell_length_a 4.26022438 +_cell_length_b 7.16692875 +_cell_length_c 7.16692875 +_cell_angle_alpha 90.00000000 +_cell_angle_beta 90.00000000 +_cell_angle_gamma 90.00000000 +_symmetry_Int_Tables_number 1 +_chemical_formula_structural Pu2Si3 +_chemical_formula_sum 'Pu4 Si6' +_cell_volume 218.82586168 +_cell_formula_units_Z 2 +loop_ + _symmetry_equiv_pos_site_id + _symmetry_equiv_pos_as_xyz + 1 'x, y, z' +loop_ + _atom_site_type_symbol + _atom_site_label + _atom_site_symmetry_multiplicity + _atom_site_fract_x + _atom_site_fract_y + _atom_site_fract_z + _atom_site_occupancy + Pu Pu0 1 0.50000000 0.67597000 0.82403000 1.0 + Pu Pu1 1 0.50000000 0.32403000 0.17597000 1.0 + Pu Pu2 1 0.50000000 0.17597000 0.67597000 1.0 + Pu Pu3 1 0.50000000 0.82403000 0.32403000 1.0 + Si Si4 1 0.00000000 0.88257100 0.61742900 1.0 + Si Si5 1 0.00000000 0.11742900 0.38257100 1.0 + Si Si6 1 0.00000000 0.38257100 0.88257100 1.0 + Si Si7 1 0.00000000 0.61742900 0.11742900 1.0 + Si Si8 1 0.00000000 0.00000000 0.00000000 1.0 + Si Si9 1 0.00000000 0.50000000 0.50000000 1.0 diff --git a/models/matris/example/test_ralexation.py b/models/matris/example/test_ralexation.py new file mode 100644 index 0000000000000000000000000000000000000000..ce5d3b40c8371a966110917b48acba82f3b89fbe --- /dev/null +++ b/models/matris/example/test_ralexation.py @@ -0,0 +1,36 @@ +import ase +from ase.build import bulk +import torch +from pymatgen.core.structure import Structure +from matris.applications.relax import StructOptimizer + +model_name = "matris_10m_oam" +device = "cuda" if torch.cuda.is_available() else "cpu" + +matris_opt = StructOptimizer( + model=model_name, + task = "efsm", + optimizer = "FIRE", # FIRE, BFGS ... + device=device +) + +atom = Structure.from_file(f"example/cif_file/demo.cif") + +max_steps = 500 +fmax = 0.05 +opt_result = matris_opt.relax( + atoms=atom, # pymatgen.Structure or ase.Atoms + verbose=True, + steps=max_steps, + fmax=fmax, + relax_cell=max_steps > 0, + ase_filter="FrechetCellFilter", + ) + +trajectory = opt_result['trajectory'] +energy = trajectory.energies[-1] +force = trajectory.forces[-1] +stress = trajectory.stresses[-1] +magmom = trajectory.magmoms[-1] + +final_structure = opt_result['final_structure'] \ No newline at end of file diff --git a/models/matris/foundation_models/MatRIS_10M_MP.pth.tar b/models/matris/foundation_models/MatRIS_10M_MP.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..c23b482e6cd082fc89dbbceb98c700ce3dc14029 --- /dev/null +++ b/models/matris/foundation_models/MatRIS_10M_MP.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:333b6019ec901a4bce9735ef28136d7da637899272825ff7ef9c84587b22f2d9 +size 42184483 diff --git a/models/matris/foundation_models/MatRIS_10M_OAM.pth.tar b/models/matris/foundation_models/MatRIS_10M_OAM.pth.tar new file mode 100644 index 0000000000000000000000000000000000000000..019e49782c04569f20537fd3df637ad88362235f --- /dev/null +++ b/models/matris/foundation_models/MatRIS_10M_OAM.pth.tar @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c033abc53601a74f10d9b7fec0f658220c013c3b11d4d405f1d32136d4c2b067 +size 42273174 diff --git a/models/matris/matris/__init__.py b/models/matris/matris/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e7d12f73c978de744f9deb548772b8100a775044 --- /dev/null +++ b/models/matris/matris/__init__.py @@ -0,0 +1,8 @@ +from importlib.metadata import version + +from importlib.metadata import PackageNotFoundError, version + +try: + __version__ = version(__name__) # read from pyproject.toml +except PackageNotFoundError: + __version__ = "unknown" \ No newline at end of file diff --git a/models/matris/matris/__pycache__/__init__.cpython-310.pyc b/models/matris/matris/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7a8341f58bd678cc2accd2df09870442d3d745bf Binary files /dev/null and b/models/matris/matris/__pycache__/__init__.cpython-310.pyc differ diff --git a/models/matris/matris/applications/__init__.py b/models/matris/matris/applications/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..005db55754c11befa31ec3c1e8051c000aa56a37 --- /dev/null +++ b/models/matris/matris/applications/__init__.py @@ -0,0 +1,4 @@ + +from .base import MatRISCalculator +from .md import MolecularDynamics +from .relax import StructOptimizer diff --git a/models/matris/matris/applications/base.py b/models/matris/matris/applications/base.py new file mode 100644 index 0000000000000000000000000000000000000000..a3db489a01f26504605e9f76a2ff50e84696a874 --- /dev/null +++ b/models/matris/matris/applications/base.py @@ -0,0 +1,199 @@ +from ase import Atoms, units +from ase.calculators.calculator import Calculator, all_changes, all_properties +import numpy as np + +from ..model.model import MatRIS + +from pymatgen.io.ase import AseAtomsAdaptor +from ..graph import RadiusGraph + +from ase.optimize import ( + BFGS, BFGSLineSearch, + FIRE, LBFGS, + LBFGSLineSearch, MDMin +) + +names = [ + "BFGS", "BFGSLineSearch", + "FIRE", "LBFGS", + "LBFGSLineSearch", "MDMin" +] + +OPTIMIZERS = {name: globals()[name] for name in names} + +class MatRISCalculator(Calculator): + """MatRIS Calculator for ASE applications.""" + + implemented_properties = ("energy", "forces", "stress", "magmoms") # type: ignore + + def __init__( + self, + model: str = "matris_10m_oam", + task: str = "efs", + device: str = "cpu", + **kwargs, + ) -> None: + """ + Args: + model (MatRIS): Instance of a MatRIS model. If set to None, the default MatRIS is loaded. + task (str): The prediction task. Can be 'e', 'em', 'ef', 'efs', 'efsm'. + device (str): The device to be used for predictions, + stress_unit (float): the conversion factor to convert GPa(MatRIS default) to eV/A^3. + **kwargs: Passed to the Calculator parent class. + """ + super().__init__(**kwargs) + self.task=task + self.device = device + self.model = MatRIS.load(model_name=model, device=self.device) + + self.stress_unit = units.GPa + key = ["atoms_per_graph", "ref_energy"] + for t in task: + key.append(t) + self.key = set(key) + + def calculate( + self, + atoms: Atoms, + properties: list, + system_changes: list, + ) -> None: + + properties = properties or all_properties + system_changes = system_changes or all_changes + super().calculate( + atoms=atoms, + properties=properties, + system_changes=system_changes, + ) + + pbc = atoms.get_pbc() + if (not pbc[0]) or (not pbc[1]) or (not pbc[2]): + pos = atoms.get_positions() + cell = np.array(atoms.get_cell()) + + pbc_x = pbc[0] + pbc_y = pbc[1] + pbc_z = pbc[2] + identity = np.identity(3, dtype=float) + max_positions = np.max(np.absolute(pos)) + 1 + + cutoff = self.model.config["pairwise_cutoff"] + expand = max(5, self.model.config["num_layers"]) + + # Extend cell in non-periodic directions + if not pbc_x: + cell[0, :] = max_positions * expand * cutoff * identity[0, :] + if not pbc_y: + cell[1, :] = max_positions * expand * cutoff * identity[1, :] + if not pbc_z: + cell[2, :] = max_positions * expand * cutoff * identity[2, :] + + # update + atoms.set_cell(cell, scale_atoms=False) + + + structure = AseAtomsAdaptor.get_structure(atoms) + graph = self.model.graph_converter(structure).to(self.device) # convert to List + + graphs = [graph] if isinstance(graph, RadiusGraph) else graph + + model_prediction = self.model( + graphs, + task = self.task, + is_training = False, + ) + model_predictions = {} + + for key in self.key & set(model_prediction.keys()): + for idx, tensor in enumerate(model_prediction[key]): + model_predictions[key] = tensor.cpu().detach().numpy() + + # Convert Result + n_atoms = 1 if not self.model.is_intensive else structure.composition.num_atoms + + self.results.update( + ref_energy=model_predictions["ref_energy"] * n_atoms, + energy=model_predictions["e"] * n_atoms, # Total Energy + forces=model_predictions.get("f", None), + # Stress: GPa -> eV/A^3 + stress=model_predictions.get("s", None) * self.stress_unit if model_predictions.get("s", None) is not None else None, + magmoms=model_predictions.get("m", None), + ) + + +class TrajectoryObserver: + # ref: https://github.com/CederGroupHub/chgnet + + def __init__(self, atoms: Atoms) -> None: + + self.atoms = atoms + self.energies: list[float] = [] + self.forces: list[np.ndarray] = [] + self.stresses: list[np.ndarray] = [] + self.magmoms: list[np.ndarray] = [] + self.atom_positions: list[np.ndarray] = [] + self.cells: list[np.ndarray] = [] + + def __call__(self) -> None: + """The logic for saving the properties of an Atoms during the relaxation.""" + self.energies.append(self.compute_energy()) + self.forces.append(self.atoms.get_forces()) + self.stresses.append(self.atoms.get_stress()) + self.magmoms.append(self.atoms.get_magnetic_moments()) + self.atom_positions.append(self.atoms.get_positions()) + self.cells.append(self.atoms.get_cell()[:]) + + def __len__(self) -> int: + """The number of steps in the trajectory.""" + return len(self.energies) + + def compute_energy(self) -> float: + """Calculate the potential energy. + + Returns: + energy (float): the potential energy. + """ + return self.atoms.get_potential_energy() + + def save(self, filename: str) -> None: + """Save the trajectory to file. + + Args: + filename (str): filename to save the trajectory + """ + out_pkl = { + "energy": self.energies, + "forces": self.forces, + "stresses": self.stresses, + "magmoms": self.magmoms, + "atom_positions": self.atom_positions, + "cell": self.cells, + "atomic_number": self.atoms.get_atomic_numbers(), + } + with open(filename, "wb") as file: + pickle.dump(out_pkl, file) + + +class CrystalFeasObserver: + # ref: https://github.com/CederGroupHub/chgnet + + def __init__(self, atoms: Atoms) -> None: + """Create a CrystalFeasObserver from an Atoms object.""" + self.atoms = atoms + self.crystal_feature_vectors: list[np.ndarray] = [] + + def __call__(self) -> None: + """Record Atoms crystal feature vectors after an MD/relaxation step.""" + self.crystal_feature_vectors.append(self.atoms._calc.results["crystal_fea"]) + + def __len__(self) -> int: + """Number of recorded steps.""" + return len(self.crystal_feature_vectors) + + def save(self, filename: str) -> None: + """Save the crystal feature vectors to filename in pickle format.""" + out_pkl = {"crystal_feas": self.crystal_feature_vectors} + with open(filename, "wb") as file: + pickle.dump(out_pkl, file) + \ No newline at end of file diff --git a/models/matris/matris/applications/md.py b/models/matris/matris/applications/md.py new file mode 100644 index 0000000000000000000000000000000000000000..84283c66bc16be4b8eda7642d2e666c13a3afd19 --- /dev/null +++ b/models/matris/matris/applications/md.py @@ -0,0 +1,349 @@ +""" Modified from CHGNet: https://github.com/CederGroupHub/chgnet """ +from __future__ import annotations + +import inspect +import sys +import io +import contextlib +from ase import Atoms, units +from ase.io import Trajectory +from ase.optimize.optimize import Optimizer +import ase.filters as filter_classes +from ase.filters import Filter +from pymatgen.io.ase import AseAtomsAdaptor +from pymatgen.core.structure import Structure, Molecule + +from ase.md.npt import NPT +from ase.md.nptberendsen import Inhomogeneous_NPTBerendsen, NPTBerendsen +from ase.md.nvtberendsen import NVTBerendsen +from ase.md.velocitydistribution import MaxwellBoltzmannDistribution, Stationary +from ase.md.verlet import VelocityVerlet + +from ..model.model import MatRIS + +from .base import ( + OPTIMIZERS, + MatRISCalculator, + TrajectoryObserver, + CrystalFeasObserver +) + +class MolecularDynamics: + """This class is used for Molecular Dynamics.""" + + def __init__( + self, + atoms: Atoms | Structure, + model: str = "matris_10m_oam", + ensemble: str = "nvt", + thermostat: str = "Berendsen_inhomogeneous", + temperature: int = 300, + starting_temperature: int | None = None, + timestep: float = 2.0, + pressure: float = 1.01325e-4, + taut: float | None = None, + taup: float | None = None, + bulk_modulus: float | None = None, + trajectory: str | Trajectory | None = None, + logfile: str | None = None, + loginterval: int = 1, + crystal_feas_logfile: str | None = None, + append_trajectory: bool = False, + task: str = "efs", + device: str | None = None, + ) -> None: + """ + Args: + atoms (Atoms): atoms to run the MD + model (MatRIS): instance of a MatRIS model or MatRISCalculator. + If set to None, the pretrained MatRIS is loaded. + Default = None + ensemble (str): choose from 'nve', 'nvt', 'npt' + Default = "nvt" + thermostat (str): Thermostat to use + choose from "Nose-Hoover", "Berendsen", "Berendsen_inhomogeneous" + Default = "Berendsen_inhomogeneous" + temperature (float): temperature for MD simulation, in K + Default = 300 + starting_temperature (float): starting temperature of MD simulation, in K + if set as None, the MD starts with the momentum carried by ase.Atoms + if input is a pymatgen.core.Structure, the MD starts at 0K + Default = None + timestep (float): time step in fs + Default = 2 + pressure (float): pressure in GPa + Can be 3x3 or 6 np.array if thermostat is "Nose-Hoover" + Default = 1.01325e-4 GPa = 1 atm + taut (float): time constant for temperature coupling in fs. + The temperature will be raised to target temperature in approximate + 10 * taut time. + Default = 100 * timestep + taup (float): time constant for pressure coupling in fs + Default = 1000 * timestep + bulk_modulus (float): bulk modulus of the material in GPa. + trajectory (str or Trajectory): Attach trajectory object + Default = None + logfile (str): open this file for recording MD outputs + Default = None + loginterval (int): write to log file every interval steps + Default = 1 + crystal_feas_logfile (str): open this file for recording crystal features + during MD. Default = None + append_trajectory (bool): Whether to append to prev trajectory. + If false, previous trajectory gets overwritten + Default = False + task (str): The prediction task. Can be 'e', 'em', 'ef', 'efs', 'efsm'. + device (str): the device for the MD run + Default = None + """ + self.ensemble = ensemble + self.thermostat = thermostat + if isinstance(atoms, (Structure, Molecule)): + atoms = AseAtomsAdaptor().get_atoms(atoms) + + if starting_temperature is not None: + MaxwellBoltzmannDistribution( + atoms, temperature_K=starting_temperature, force_temp=True + ) + Stationary(atoms) + + self.atoms = atoms + + self.atoms.calc = MatRISCalculator( + model=model, + device=device, + task=task, + ) + + if taut is None: + taut = 100 * timestep + if taup is None: + taup = 1000 * timestep + + if ensemble.lower() == "nve": + """ + VelocityVerlet (constant N, V, E) molecular dynamics. + + Note: it's recommended to use smaller timestep for NVE compared to other + ensembles, since the VelocityVerlet algorithm assumes a strict conservative + force field. + """ + self.dyn = VelocityVerlet( + atoms=self.atoms, + timestep=timestep * units.fs, + trajectory=trajectory, + logfile=logfile, + loginterval=loginterval, + append_trajectory=append_trajectory, + ) + print("NVE-MD created") + + elif ensemble.lower() == "nvt": + """ + Constant volume/temperature molecular dynamics. + """ + if thermostat.lower() == "nose-hoover": + """ + Nose-hoover (constant N, V, T) molecular dynamics. + ASE implementation currently only supports upper triangular lattice + """ + self.upper_triangular_cell() + self.dyn = NPT( + atoms=self.atoms, + timestep=timestep * units.fs, + temperature_K=temperature, + externalstress=pressure + * units.GPa, # ase NPT does not like externalstress=None + ttime=taut * units.fs, + pfactor=None, + trajectory=trajectory, + logfile=logfile, + loginterval=loginterval, + append_trajectory=append_trajectory, + ) + print("NVT-Nose-Hoover MD created") + elif thermostat.lower().startswith("berendsen"): + """ + Berendsen (constant N, V, T) molecular dynamics. + """ + self.dyn = NVTBerendsen( + atoms=self.atoms, + timestep=timestep * units.fs, + temperature_K=temperature, + taut=taut * units.fs, + trajectory=trajectory, + logfile=logfile, + loginterval=loginterval, + append_trajectory=append_trajectory, + ) + print("NVT-Berendsen-MD created") + else: + raise ValueError( + "Thermostat not supported, choose in 'Nose-Hoover', 'Berendsen', " + "'Berendsen_inhomogeneous'" + ) + + elif ensemble.lower() == "npt": + """ + Constant pressure/temperature molecular dynamics. + """ + # Bulk modulus is needed for pressure damping time + if bulk_modulus is not None: + bulk_modulus_au = bulk_modulus * units.GPa # GPa to eV/A^3 + compressibility_au = 1 / bulk_modulus_au + else: + try: + # Fit bulk modulus by equation of state + eos = EquationOfState(model=self.atoms.calc) + eos.fit(atoms=atoms, steps=500, fmax=0.1, verbose=False) + bulk_modulus = eos.get_bulk_modulus(unit="GPa") + bulk_modulus_au = eos.get_bulk_modulus(unit="eV/A^3") + compressibility_au = eos.get_compressibility(unit="A^3/eV") + print( + f"Completed bulk modulus calculation: " + f"k = {bulk_modulus:.3}GPa, {bulk_modulus_au:.3}eV/A^3" + ) + except Exception: + bulk_modulus_au = 2 * units.GPa + compressibility_au = 1 / bulk_modulus_au + print( + "Warning!!! Equation of State fitting failed, setting bulk " + "modulus to 2 GPa. NPT simulation can proceed with incorrect " + "pressure relaxation time." + "User input for bulk modulus is recommended." + ) + self.bulk_modulus = bulk_modulus + + if thermostat.lower() == "nose-hoover": + """ + Combined Nose-Hoover and Parrinello-Rahman dynamics, creating an + NPT (or N,stress,T) ensemble. + see: https://gitlab.com/ase/ase/-/blob/master/ase/md/npt.py + ASE implementation currently only supports upper triangular lattice + """ + self.upper_triangular_cell() + ptime = taup * units.fs + self.dyn = NPT( + atoms=self.atoms, + timestep=timestep * units.fs, + temperature_K=temperature, + externalstress=pressure * units.GPa, + ttime=taut * units.fs, + pfactor=bulk_modulus * units.GPa * ptime * ptime, + trajectory=trajectory, + logfile=logfile, + loginterval=loginterval, + append_trajectory=append_trajectory, + ) + print("NPT-Nose-Hoover MD created") + + elif thermostat.lower() == "berendsen_inhomogeneous": + """ + Inhomogeneous_NPTBerendsen thermo/barostat + This is a more flexible scheme that fixes three angles of the unit + cell but allows three lattice parameter to change independently. + see: https://gitlab.com/ase/ase/-/blob/master/ase/md/nptberendsen.py + """ + + self.dyn = Inhomogeneous_NPTBerendsen( + atoms=self.atoms, + timestep=timestep * units.fs, + temperature_K=temperature, + pressure_au=pressure * units.GPa, + taut=taut * units.fs, + taup=taup * units.fs, + compressibility_au=compressibility_au, + trajectory=trajectory, + logfile=logfile, + loginterval=loginterval, + ) + print("NPT-Berendsen-inhomogeneous-MD created") + + elif thermostat.lower() == "npt_berendsen": + """ + This is a similar scheme to the Inhomogeneous_NPTBerendsen. + This is a less flexible scheme that fixes the shape of the + cell - three angles are fixed and the ratios between the three + lattice constants. + see: https://gitlab.com/ase/ase/-/blob/master/ase/md/nptberendsen.py + """ + + self.dyn = NPTBerendsen( + atoms=self.atoms, + timestep=timestep * units.fs, + temperature_K=temperature, + pressure_au=pressure * units.GPa, + taut=taut * units.fs, + taup=taup * units.fs, + compressibility_au=compressibility_au, + trajectory=trajectory, + logfile=logfile, + loginterval=loginterval, + append_trajectory=append_trajectory, + ) + print("NPT-Berendsen-MD created") + else: + raise ValueError( + "Thermostat not supported, choose in 'Nose-Hoover', 'Berendsen', " + "'Berendsen_inhomogeneous'" + ) + + self.trajectory = trajectory + self.logfile = logfile + self.loginterval = loginterval + self.timestep = timestep + self.crystal_feas_logfile = crystal_feas_logfile + + def run(self, steps: int) -> None: + """Thin wrapper of ase MD run. + + Args: + steps (int): number of MD steps + """ + if self.crystal_feas_logfile: + obs = CrystalFeasObserver(self.atoms) + self.dyn.attach(obs, interval=self.loginterval) + + self.dyn.run(steps) + + if self.crystal_feas_logfile: + obs.save(self.crystal_feas_logfile) + + def set_atoms(self, atoms: Atoms) -> None: + """Set new atoms to run MD. + + Args: + atoms (Atoms): new atoms for running MD + """ + calculator = self.atoms.calc + self.atoms = atoms + self.dyn.atoms = atoms + self.dyn.atoms.calc = calculator + + def upper_triangular_cell(self, verbose: bool | None = False) -> None: + """Transform to upper-triangular cell. + ASE Nose-Hoover implementation only supports upper-triangular cell + while ASE's canonical description is lower-triangular cell. + + Args: + verbose (bool): Whether to notify user about upper-triangular cell + transformation. Default = False + """ + if not NPT._isuppertriangular(self.atoms.get_cell()): + a, b, c, alpha, beta, gamma = self.atoms.cell.cellpar() + angles = np.radians((alpha, beta, gamma)) + sin_a, sin_b, _sin_g = np.sin(angles) + cos_a, cos_b, cos_g = np.cos(angles) + cos_p = (cos_g - cos_a * cos_b) / (sin_a * sin_b) + cos_p = np.clip(cos_p, -1, 1) + sin_p = (1 - cos_p**2) ** 0.5 + + new_basis = [ + (a * sin_b * sin_p, a * sin_b * cos_p, a * cos_b), + (0, b * sin_a, b * cos_a), + (0, 0, c), + ] + + self.atoms.set_cell(new_basis, scale_atoms=True) + if verbose: + print("Transformed to upper triangular unit cell.", flush=True) diff --git a/models/matris/matris/applications/relax.py b/models/matris/matris/applications/relax.py new file mode 100644 index 0000000000000000000000000000000000000000..811c5ad4e0e8e434c602f5bdb5c8070c887918a5 --- /dev/null +++ b/models/matris/matris/applications/relax.py @@ -0,0 +1,144 @@ +""" Modified from CHGNet: https://github.com/CederGroupHub/chgnet """ + +import inspect +import sys +import io +import contextlib +from ase import Atoms, units +from ase.optimize.optimize import Optimizer +import ase.filters as filter_classes +from ase.filters import Filter +from pymatgen.io.ase import AseAtomsAdaptor +from pymatgen.core.structure import Structure + +from ..model.model import MatRIS +from .base import ( + OPTIMIZERS, + MatRISCalculator, + TrajectoryObserver, + CrystalFeasObserver +) + +class StructOptimizer: + """This class is used for Sturcture Optimization.""" + + def __init__( + self, + model: str = "matris_10m_oam", + task: str = "efs", + optimizer: str = "FIRE", + device: str = "cpu", + ) -> None: + """ + Args: + model (MatRIS): Instance of a MatRIS model. If set to None, the default MatRIS is loaded. + task (str): The prediction task. Can be 'e', 'em', 'ef', 'efs', 'efsm'. + optimizer (Optimizer,str): choose optimizer from ASE. + device (str): The device to be used for predictions, + stress_unit (float): the conversion factor to convert GPa(MatRIS default) to eV/A^3. + **kwargs: Passed to the Calculator parent class. + """ + + if optimizer in OPTIMIZERS: + optimizer = OPTIMIZERS[optimizer] + else: + raise ValueError( + f"Optimizer {optimizer} not found. Select from {list(OPTIMIZERS)}" + ) + + self.optimizer: Optimizer = optimizer + + self.calculator = MatRISCalculator( + model=model, + task=task, + device=device, + ) + + def relax( + self, + atoms: Structure | Atoms, + fmax: float = 0.05, + steps: int = 500, + relax_cell: bool = True, + ase_filter: str = "FrechetCellFilter", + save_path: str = None, + loginterval: int = 1, + crystal_feas_save_path: str = None, + verbose: bool = True, + assign_magmoms: bool = True, + **kwargs, + ) -> dict[str, Structure | TrajectoryObserver]: + """ + Args: + atoms (Structure | Atoms): A Structure or Atoms object to relax. + fmax (float): The maximum force tolerance for relaxation. + steps (int): The maximum number of steps for relaxation. + relax_cell (bool): Whether to relax the cell as well. + ase_filter (str): The filter to apply to the atoms object for relaxation. + save_path (str): The path to save the trajectory. + loginterval (int): Interval for logging trajectory and crystal feas. + crystal_feas_save_path (str): Path to save crystal feature vectors + which are logged at a loginterval rage + verbose (bool): Whether to print the output of the ASE optimizer. + assign_magmoms (bool): Whether to assign magnetic moments to the final + structure. + **kwargs: Additional parameters for the optimizer. + """ + + valid_filter_names = [ + name + for name, cls in inspect.getmembers(filter_classes, inspect.isclass) + if issubclass(cls, Filter) + ] + + if isinstance(ase_filter, str): + if ase_filter in valid_filter_names: + ase_filter = getattr(filter_classes, ase_filter) + else: + raise ValueError( + f"Invalid {ase_filter=}, must be one of {valid_filter_names}. " + ) + + if isinstance(atoms, Structure): + atoms = AseAtomsAdaptor().get_atoms(atoms) + + atoms.calc = self.calculator + + stream = sys.stdout if verbose else io.StringIO() + with contextlib.redirect_stdout(stream): + obs = TrajectoryObserver(atoms) + + if crystal_feas_save_path: + cry_obs = CrystalFeasObserver(atoms) + + if relax_cell: + atoms = ase_filter(atoms) + optimizer: Optimizer = self.optimizer(atoms, **kwargs) + optimizer.attach(obs, interval=loginterval) + + if crystal_feas_save_path: + optimizer.attach(cry_obs, interval=loginterval) + + optimizer.run(fmax=fmax, steps=steps) + obs() + + if save_path is not None: + obs.save(save_path) + + if crystal_feas_save_path: + cry_obs.save(crystal_feas_save_path) + + if isinstance(atoms, Filter): + atoms = atoms.atoms + struct = AseAtomsAdaptor.get_structure(atoms) + + if assign_magmoms: + if atoms.get_magnetic_moments() is not None: + for key in struct.site_properties: + struct.remove_site_property(property_name=key) + struct.add_site_property( + "magmom", [float(magmom) for magmom in atoms.get_magnetic_moments()] + ) + + return {"final_structure": struct, "trajectory": obs} + diff --git a/models/matris/matris/graph/__init__.py b/models/matris/matris/graph/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e55e947ec41248e33b05f69dc8f7d7671536ad36 --- /dev/null +++ b/models/matris/matris/graph/__init__.py @@ -0,0 +1,3 @@ + +from .converter import GraphConverter +from .radiusgraph import RadiusGraph, datatype \ No newline at end of file diff --git a/models/matris/matris/graph/__pycache__/__init__.cpython-310.pyc b/models/matris/matris/graph/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..80437471587db0e4fa1b3ed579e8f26e7773bdaa Binary files /dev/null and b/models/matris/matris/graph/__pycache__/__init__.cpython-310.pyc differ diff --git a/models/matris/matris/graph/__pycache__/converter.cpython-310.pyc b/models/matris/matris/graph/__pycache__/converter.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2598f4a73127278ae453d42c902edf3a4890a48b Binary files /dev/null and b/models/matris/matris/graph/__pycache__/converter.cpython-310.pyc differ diff --git a/models/matris/matris/graph/__pycache__/radiusgraph.cpython-310.pyc b/models/matris/matris/graph/__pycache__/radiusgraph.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c650a9aa7d4af65234e0f5175b5a384c1be63145 Binary files /dev/null and b/models/matris/matris/graph/__pycache__/radiusgraph.cpython-310.pyc differ diff --git a/models/matris/matris/graph/converter.py b/models/matris/matris/graph/converter.py new file mode 100644 index 0000000000000000000000000000000000000000..2d3b2fa682e2c1199e2855ea5ae65e68ca0b2dc7 --- /dev/null +++ b/models/matris/matris/graph/converter.py @@ -0,0 +1,226 @@ +""" + This code is referenced from: https://github.com/CederGroupHub/chgnet/blob/main/chgnet/graph/converter.py + The original implementation can be found at the link above. +""" +from __future__ import annotations + +import numpy as np +import torch +from torch import nn +import gc + +from .radiusgraph import Graph, Node, RadiusGraph +from pymatgen.core import Structure + +try: + from .cygraph import make_graph +except (ImportError, AttributeError): + make_graph = None + print("Failed with make_graph, run some program before") + +datatype = torch.float32 + +class GraphConverter(nn.Module): + """Convert a pymatgen.core.Structure to a RadiusGraph""" + + def __init__( + self, + atom_graph_cutoff: float = 6, + line_graph_cutoff: float = 4, + verbose: bool = False, + ) -> None: + """Initialize the Graph Converter. + + Args: + atom_graph_cutoff (float): cutoff radius in atom graph. + line_graph_cutoff (float): bond length threshold in line graph. + verbose (bool): whether to print the GraphConverter. + """ + super().__init__() + self.atom_graph_cutoff = atom_graph_cutoff + self.line_graph_cutoff = ( + atom_graph_cutoff if line_graph_cutoff is None else line_graph_cutoff + ) + + if make_graph is not None: + self.create_graph = self._create_graph_fast + self.algorithm = 'fast' + else: + self.create_graph = self._create_graph_legacy + self.algorithm = 'legacy' + print("fast graph converter algorithm import error, using legacy") + + if verbose: + print(self) + + def __repr__(self) -> str: + """String representation of the GraphConverter.""" + atom_graph_cutoff = self.atom_graph_cutoff + line_graph_cutoff = self.line_graph_cutoff + algorithm = self.algorithm + cls_name = type(self).__name__ + return f"{cls_name}({algorithm=}, {atom_graph_cutoff=}, {line_graph_cutoff=})" + + def forward( + self, + structure: Structure, + graph_id=None, + mp_id=None, + ) -> RadiusGraph: + """Convert a structure, return a RadiusGraph. + + Args: + structure (pymatgen.core.Structure): structure to convert + graph_id (str): an id to keep track of this crystal graph + Default = None + mp_id (str): Materials Project id of this structure + Default = None + + """ + n_atoms = len(structure) + atomic_number = torch.tensor( [site.specie.Z for site in structure], dtype=torch.int32 ) + + atom_frac_coord = torch.tensor( structure.frac_coords, dtype=datatype ) + lattice = torch.tensor( structure.lattice.matrix, dtype=datatype ) + + center_index, neighbor_index, image, distance = structure.get_neighbor_list( + r=self.atom_graph_cutoff, sites=structure.sites, numerical_tol=1e-8 + ) + # Ceate atom graph + graph = self.create_graph( + n_atoms, center_index, neighbor_index, image, distance + ) + atom_graph, directed2undirected = graph.adjacency_list() + atom_graph = torch.tensor(atom_graph, dtype=torch.int32) + directed2undirected = torch.tensor(directed2undirected, dtype=torch.int32) + undirected2directed = graph.undirected2directed() + undirected2directed = torch.tensor(undirected2directed, dtype=torch.int32) + + line_graph = [] + try: + line_graph = graph.line_graph_adjacency_list( + cutoff=self.line_graph_cutoff + ) + except Exception as exc: + structure.to(filename="error_graph.cif") + + line_graph = torch.tensor(line_graph, dtype=torch.int32) + + # For isolated atom, we stop this calculation + n_isolated_atoms = len({*range(n_atoms)} - {*center_index}) + if n_isolated_atoms: + atom_graph_cutoff = self.atom_graph_cutoff + error = f"Error: Detected {n_isolated_atoms} isolated atom. Calculation stopped" + raise ValueError(error) # or print(error) + + return RadiusGraph( + atomic_number=atomic_number, + atom_frac_coord=atom_frac_coord, + atom_graph=atom_graph, + neighbor_image=torch.tensor(image, dtype=datatype), + directed2undirected=directed2undirected, + undirected2directed=undirected2directed, + line_graph=line_graph, + lattice=lattice, + graph_id=graph_id, + mp_id=mp_id, + composition=structure.composition.formula, + atom_graph_cutoff=self.atom_graph_cutoff, + line_graph_cutoff=self.line_graph_cutoff, + ) + + @staticmethod + def _create_graph_legacy( + n_atoms: int, + center_index: np.ndarray, + neighbor_index: np.ndarray, + image: np.ndarray, + distance: np.ndarray, + ) -> Graph: + """Given structure information, create a Graph structure to be used to + create Crystal_Graph using pure python implementation. + + Args: + n_atoms (int): the number of atoms in the structure + center_index (np.ndarray): np array of indices of center atoms. + [num_undirected_bonds] + neighbor_index (np.ndarray): np array of indices of neighbor atoms. + [num_undirected_bonds] + image (np.ndarray): np array of images for each edge. + [num_undirected_bonds, 3] + distance (np.ndarray): np array of distances. + [num_undirected_bonds] + + Return: + Graph data structure used to create Crystal_Graph object + """ + + graph = Graph([Node(index=idx) for idx in range(n_atoms)]) + for ii, jj, img, dist in zip(center_index, neighbor_index, image, distance): + graph.add_edge(center_index=ii, neighbor_index=jj, image=img, distance=dist) + + return graph + + @staticmethod + def _create_graph_fast( + n_atoms: int, + center_index: np.ndarray, + neighbor_index: np.ndarray, + image: np.ndarray, + distance: np.ndarray, + ) -> Graph: + """Given structure information, create a Graph structure to be used to + create Crystal_Graph using C implementation. + + NOTE: this is the fast version of _create_graph_legacy optimized + in c (~3x speedup). + + Args: + n_atoms (int): the number of atoms in the structure + center_index (np.ndarray): np array of indices of center atoms. + [num_undirected_bonds] + neighbor_index (np.ndarray): np array of indices of neighbor atoms. + [num_undirected_bonds] + image (np.ndarray): np array of images for each edge. + [num_undirected_bonds, 3] + distance (np.ndarray): np array of distances. + [num_undirected_bonds] + + Return: + Graph data structure used to create Crystal_Graph object + """ + center_index = np.ascontiguousarray(center_index) + neighbor_index = np.ascontiguousarray(neighbor_index) + image = np.ascontiguousarray(image, dtype=np.int_) + distance = np.ascontiguousarray(distance) + gc_saved = gc.get_threshold() + gc.set_threshold(0) + ( + nodes, + directed_edges_list, + undirected_edges_list, + undirected_edges, + ) = make_graph( + center_index, len(center_index), neighbor_index, image, distance, n_atoms + ) + + graph = Graph(nodes=nodes) + graph.directed_edges_list = directed_edges_list + graph.undirected_edges_list = undirected_edges_list + graph.undirected_edges = undirected_edges + gc.set_threshold(gc_saved[0]) + + return graph + + def as_dict(self) -> dict[str, float]: + """Save the args of the graph converter.""" + return { + "atom_graph_cutoff": self.atom_graph_cutoff, + "line_graph_cutoff": self.line_graph_cutoff, + "algorithm": self.algorithm, + } + + @classmethod + def from_dict(cls, dict) -> GraphConverter: + """Create converter from dictionary.""" + return GraphConverter(**dict) diff --git a/models/matris/matris/graph/cygraph.c b/models/matris/matris/graph/cygraph.c new file mode 100644 index 0000000000000000000000000000000000000000..75c351e7b2e61f6a763f04228d4b85bac287e181 --- /dev/null +++ b/models/matris/matris/graph/cygraph.c @@ -0,0 +1,28506 @@ +/* Generated by Cython 3.1.6 */ + +/* BEGIN: Cython Metadata +{ + "distutils": { + "depends": [ + "matris/graph/fast_converter_libraries/create_graph.c" + ], + "include_dirs": [ + "matris/graph" + ], + "name": "matris.graph.cygraph", + "sources": [ + "matris/graph/cygraph.pyx" + ] + }, + "module_name": "matris.graph.cygraph" +} +END: Cython Metadata */ + +#ifndef PY_SSIZE_T_CLEAN +#define PY_SSIZE_T_CLEAN +#endif /* PY_SSIZE_T_CLEAN */ +/* InitLimitedAPI */ +#if defined(Py_LIMITED_API) + #if !defined(CYTHON_LIMITED_API) + #define CYTHON_LIMITED_API 1 + #endif +#elif defined(CYTHON_LIMITED_API) + #ifdef _MSC_VER + #pragma message ("Limited API usage is enabled with 'CYTHON_LIMITED_API' but 'Py_LIMITED_API' does not define a Python target version. Consider setting 'Py_LIMITED_API' instead.") + #else + #warning Limited API usage is enabled with 'CYTHON_LIMITED_API' but 'Py_LIMITED_API' does not define a Python target version. Consider setting 'Py_LIMITED_API' instead. + #endif +#endif + +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x03080000 + #error Cython requires Python 3.8+. +#else +#define __PYX_ABI_VERSION "3_1_6" +#define CYTHON_HEX_VERSION 0x030106F0 +#define CYTHON_FUTURE_DIVISION 1 +/* CModulePreamble */ +#include +#ifndef offsetof + #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(_WIN32) && !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#define __PYX_COMMA , +#ifndef HAVE_LONG_LONG + #define HAVE_LONG_LONG +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#define __PYX_LIMITED_VERSION_HEX PY_VERSION_HEX +#if defined(GRAALVM_PYTHON) + /* For very preliminary testing purposes. Most variables are set the same as PyPy. + The existence of this section does not imply that anything works or is even tested */ + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_LIMITED_API 0 + #define CYTHON_COMPILING_IN_GRAAL 1 + #define CYTHON_COMPILING_IN_CPYTHON_FREETHREADING 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 0 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_AVOID_THREAD_UNSAFE_BORROWED_REFS + #define CYTHON_AVOID_THREAD_UNSAFE_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_ASSUME_SAFE_SIZE + #define CYTHON_ASSUME_SAFE_SIZE 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL 0 + #undef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS 1 + #endif + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #undef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 0 + #undef CYTHON_USE_SYS_MONITORING + #define CYTHON_USE_SYS_MONITORING 0 + #undef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #undef CYTHON_USE_AM_SEND + #define CYTHON_USE_AM_SEND 0 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 1 + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 + #endif + #undef CYTHON_USE_FREELISTS + #define CYTHON_USE_FREELISTS 0 +#elif defined(PYPY_VERSION) + #define CYTHON_COMPILING_IN_PYPY 1 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_LIMITED_API 0 + #define CYTHON_COMPILING_IN_GRAAL 0 + #define CYTHON_COMPILING_IN_CPYTHON_FREETHREADING 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #ifndef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 0 + #endif + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_AVOID_THREAD_UNSAFE_BORROWED_REFS + #define CYTHON_AVOID_THREAD_UNSAFE_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #ifndef CYTHON_ASSUME_SAFE_SIZE + #define CYTHON_ASSUME_SAFE_SIZE 1 + #endif + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL 0 + #undef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS 1 + #endif + #if PY_VERSION_HEX < 0x03090000 + #undef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 0 + #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT) + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #endif + #undef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 0 + #undef CYTHON_USE_SYS_MONITORING + #define CYTHON_USE_SYS_MONITORING 0 + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE (PYPY_VERSION_NUM >= 0x07030C00) + #endif + #undef CYTHON_USE_AM_SEND + #define CYTHON_USE_AM_SEND 0 + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC (PYPY_VERSION_NUM >= 0x07031100) + #endif + #undef CYTHON_USE_FREELISTS + #define CYTHON_USE_FREELISTS 0 +#elif defined(CYTHON_LIMITED_API) + #ifdef Py_LIMITED_API + #undef __PYX_LIMITED_VERSION_HEX + #define __PYX_LIMITED_VERSION_HEX Py_LIMITED_API + #endif + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_LIMITED_API 1 + #define CYTHON_COMPILING_IN_GRAAL 0 + #define CYTHON_COMPILING_IN_CPYTHON_FREETHREADING 0 + #undef CYTHON_CLINE_IN_TRACEBACK + #define CYTHON_CLINE_IN_TRACEBACK 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 1 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #endif + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_AVOID_THREAD_UNSAFE_BORROWED_REFS + #define CYTHON_AVOID_THREAD_UNSAFE_BORROWED_REFS 0 + #endif + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_ASSUME_SAFE_SIZE + #define CYTHON_ASSUME_SAFE_SIZE 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL 0 + #undef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL (__PYX_LIMITED_VERSION_HEX >= 0x030C0000) + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS 1 + #endif + #ifndef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #endif + #ifndef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 0 + #endif + #undef CYTHON_USE_SYS_MONITORING + #define CYTHON_USE_SYS_MONITORING 0 + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 0 + #endif + #ifndef CYTHON_USE_AM_SEND + #define CYTHON_USE_AM_SEND (__PYX_LIMITED_VERSION_HEX >= 0x030A0000) + #endif + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #undef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 0 + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 + #endif + #undef CYTHON_USE_FREELISTS + #define CYTHON_USE_FREELISTS 0 +#else + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_CPYTHON 1 + #define CYTHON_COMPILING_IN_LIMITED_API 0 + #define CYTHON_COMPILING_IN_GRAAL 0 + #ifdef Py_GIL_DISABLED + #define CYTHON_COMPILING_IN_CPYTHON_FREETHREADING 1 + #else + #define CYTHON_COMPILING_IN_CPYTHON_FREETHREADING 0 + #endif + #if PY_VERSION_HEX < 0x030A0000 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #elif !defined(CYTHON_USE_TYPE_SLOTS) + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #ifndef CYTHON_USE_TYPE_SPECS + #define CYTHON_USE_TYPE_SPECS 0 + #endif + #ifndef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 1 + #endif + #ifndef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 1 + #endif + #if CYTHON_COMPILING_IN_CPYTHON_FREETHREADING + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #elif !defined(CYTHON_USE_PYLIST_INTERNALS) + #define CYTHON_USE_PYLIST_INTERNALS 1 + #endif + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #if CYTHON_COMPILING_IN_CPYTHON_FREETHREADING || PY_VERSION_HEX >= 0x030B00A2 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #elif !defined(CYTHON_USE_UNICODE_WRITER) + #define CYTHON_USE_UNICODE_WRITER 1 + #endif + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #if CYTHON_COMPILING_IN_CPYTHON_FREETHREADING + #undef CYTHON_AVOID_THREAD_UNSAFE_BORROWED_REFS + #define CYTHON_AVOID_THREAD_UNSAFE_BORROWED_REFS 1 + #elif !defined(CYTHON_AVOID_THREAD_UNSAFE_BORROWED_REFS) + #define CYTHON_AVOID_THREAD_UNSAFE_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_ASSUME_SAFE_SIZE + #define CYTHON_ASSUME_SAFE_SIZE 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #ifndef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 1 + #endif + #if CYTHON_COMPILING_IN_CPYTHON_FREETHREADING + #undef CYTHON_FAST_GIL + #define CYTHON_FAST_GIL 0 + #elif !defined(CYTHON_FAST_GIL) + #define CYTHON_FAST_GIL (PY_VERSION_HEX < 0x030C00A6) + #endif + #ifndef CYTHON_METH_FASTCALL + #define CYTHON_METH_FASTCALL 1 + #endif + #ifndef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 1 + #endif + #ifndef CYTHON_PEP487_INIT_SUBCLASS + #define CYTHON_PEP487_INIT_SUBCLASS 1 + #endif + #ifndef CYTHON_PEP489_MULTI_PHASE_INIT + #define CYTHON_PEP489_MULTI_PHASE_INIT 1 + #endif + #ifndef CYTHON_USE_MODULE_STATE + #define CYTHON_USE_MODULE_STATE 0 + #endif + #ifndef CYTHON_USE_SYS_MONITORING + #define CYTHON_USE_SYS_MONITORING (PY_VERSION_HEX >= 0x030d00B1) + #endif + #ifndef CYTHON_USE_TP_FINALIZE + #define CYTHON_USE_TP_FINALIZE 1 + #endif + #ifndef CYTHON_USE_AM_SEND + #define CYTHON_USE_AM_SEND 1 + #endif + #if CYTHON_COMPILING_IN_CPYTHON_FREETHREADING + #undef CYTHON_USE_DICT_VERSIONS + #define CYTHON_USE_DICT_VERSIONS 0 + #elif !defined(CYTHON_USE_DICT_VERSIONS) + #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX < 0x030C00A5 && !CYTHON_USE_MODULE_STATE) + #endif + #ifndef CYTHON_USE_EXC_INFO_STACK + #define CYTHON_USE_EXC_INFO_STACK 1 + #endif + #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC + #define CYTHON_UPDATE_DESCRIPTOR_DOC 1 + #endif + #ifndef CYTHON_USE_FREELISTS + #define CYTHON_USE_FREELISTS (!CYTHON_COMPILING_IN_CPYTHON_FREETHREADING) + #endif +#endif +#ifndef CYTHON_FAST_PYCCALL +#define CYTHON_FAST_PYCCALL CYTHON_FAST_PYCALL +#endif +#ifndef CYTHON_VECTORCALL +#if CYTHON_COMPILING_IN_LIMITED_API +#define CYTHON_VECTORCALL (__PYX_LIMITED_VERSION_HEX >= 0x030C0000) +#else +#define CYTHON_VECTORCALL (CYTHON_FAST_PYCCALL && PY_VERSION_HEX >= 0x030800B1) +#endif +#endif +#define CYTHON_BACKPORT_VECTORCALL (CYTHON_METH_FASTCALL && PY_VERSION_HEX < 0x030800B1) +#if CYTHON_USE_PYLONG_INTERNALS + #undef SHIFT + #undef BASE + #undef MASK + #ifdef SIZEOF_VOID_P + enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) }; + #endif +#endif +#ifndef CYTHON_LOCK_AND_GIL_DEADLOCK_AVOIDANCE_TIME + #define CYTHON_LOCK_AND_GIL_DEADLOCK_AVOIDANCE_TIME 100 +#endif +#ifndef __has_attribute + #define __has_attribute(x) 0 +#endif +#ifndef __has_cpp_attribute + #define __has_cpp_attribute(x) 0 +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#ifndef CYTHON_UNUSED + #if defined(__cplusplus) + /* for clang __has_cpp_attribute(maybe_unused) is true even before C++17 + * but leads to warnings with -pedantic, since it is a C++17 feature */ + #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L) + #if __has_cpp_attribute(maybe_unused) + #define CYTHON_UNUSED [[maybe_unused]] + #endif + #endif + #endif +#endif +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_UNUSED_VAR +# if defined(__cplusplus) + template void CYTHON_UNUSED_VAR( const T& ) { } +# else +# define CYTHON_UNUSED_VAR(x) (void)(x) +# endif +#endif +#ifndef CYTHON_MAYBE_UNUSED_VAR + #define CYTHON_MAYBE_UNUSED_VAR(x) CYTHON_UNUSED_VAR(x) +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON && !CYTHON_COMPILING_IN_CPYTHON_FREETHREADING +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_USE_CPP_STD_MOVE + #if defined(__cplusplus) && (\ + __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1600)) + #define CYTHON_USE_CPP_STD_MOVE 1 + #else + #define CYTHON_USE_CPP_STD_MOVE 0 + #endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) +#ifdef _MSC_VER + #ifndef _MSC_STDINT_H_ + #if _MSC_VER < 1300 + typedef unsigned char uint8_t; + typedef unsigned short uint16_t; + typedef unsigned int uint32_t; + #else + typedef unsigned __int8 uint8_t; + typedef unsigned __int16 uint16_t; + typedef unsigned __int32 uint32_t; + #endif + #endif + #if _MSC_VER < 1300 + #ifdef _WIN64 + typedef unsigned long long __pyx_uintptr_t; + #else + typedef unsigned int __pyx_uintptr_t; + #endif + #else + #ifdef _WIN64 + typedef unsigned __int64 __pyx_uintptr_t; + #else + typedef unsigned __int32 __pyx_uintptr_t; + #endif + #endif +#else + #include + typedef uintptr_t __pyx_uintptr_t; +#endif +#ifndef CYTHON_FALLTHROUGH + #if defined(__cplusplus) + /* for clang __has_cpp_attribute(fallthrough) is true even before C++17 + * but leads to warnings with -pedantic, since it is a C++17 feature */ + #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L) + #if __has_cpp_attribute(fallthrough) + #define CYTHON_FALLTHROUGH [[fallthrough]] + #endif + #endif + #ifndef CYTHON_FALLTHROUGH + #if __has_cpp_attribute(clang::fallthrough) + #define CYTHON_FALLTHROUGH [[clang::fallthrough]] + #elif __has_cpp_attribute(gnu::fallthrough) + #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] + #endif + #endif + #endif + #ifndef CYTHON_FALLTHROUGH + #if __has_attribute(fallthrough) + #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) + #else + #define CYTHON_FALLTHROUGH + #endif + #endif + #if defined(__clang__) && defined(__apple_build_version__) + #if __apple_build_version__ < 7000000 + #undef CYTHON_FALLTHROUGH + #define CYTHON_FALLTHROUGH + #endif + #endif +#endif +#ifndef Py_UNREACHABLE + #define Py_UNREACHABLE() assert(0); abort() +#endif +#ifdef __cplusplus + template + struct __PYX_IS_UNSIGNED_IMPL {static const bool value = T(0) < T(-1);}; + #define __PYX_IS_UNSIGNED(type) (__PYX_IS_UNSIGNED_IMPL::value) +#else + #define __PYX_IS_UNSIGNED(type) (((type)-1) > 0) +#endif +#if CYTHON_COMPILING_IN_PYPY == 1 + #define __PYX_NEED_TP_PRINT_SLOT (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x030A0000) +#else + #define __PYX_NEED_TP_PRINT_SLOT (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000) +#endif +#define __PYX_REINTERPRET_FUNCION(func_pointer, other_pointer) ((func_pointer)(void(*)(void))(other_pointer)) + +/* CInitCode */ +#ifndef CYTHON_INLINE + #if defined(__clang__) + #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) + #elif defined(__GNUC__) + #define CYTHON_INLINE __inline__ + #elif defined(_MSC_VER) + #define CYTHON_INLINE __inline + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_INLINE inline + #else + #define CYTHON_INLINE + #endif +#endif + +/* PythonCompatibility */ +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#define __Pyx_BUILTIN_MODULE_NAME "builtins" +#define __Pyx_DefaultClassType PyType_Type +#if CYTHON_COMPILING_IN_LIMITED_API + #ifndef CO_OPTIMIZED + static int CO_OPTIMIZED; + #endif + #ifndef CO_NEWLOCALS + static int CO_NEWLOCALS; + #endif + #ifndef CO_VARARGS + static int CO_VARARGS; + #endif + #ifndef CO_VARKEYWORDS + static int CO_VARKEYWORDS; + #endif + #ifndef CO_ASYNC_GENERATOR + static int CO_ASYNC_GENERATOR; + #endif + #ifndef CO_GENERATOR + static int CO_GENERATOR; + #endif + #ifndef CO_COROUTINE + static int CO_COROUTINE; + #endif +#else + #ifndef CO_COROUTINE + #define CO_COROUTINE 0x80 + #endif + #ifndef CO_ASYNC_GENERATOR + #define CO_ASYNC_GENERATOR 0x200 + #endif +#endif +static int __Pyx_init_co_variables(void); +#if PY_VERSION_HEX >= 0x030900A4 || defined(Py_IS_TYPE) + #define __Pyx_IS_TYPE(ob, type) Py_IS_TYPE(ob, type) +#else + #define __Pyx_IS_TYPE(ob, type) (((const PyObject*)ob)->ob_type == (type)) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_Is) + #define __Pyx_Py_Is(x, y) Py_Is(x, y) +#else + #define __Pyx_Py_Is(x, y) ((x) == (y)) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsNone) + #define __Pyx_Py_IsNone(ob) Py_IsNone(ob) +#else + #define __Pyx_Py_IsNone(ob) __Pyx_Py_Is((ob), Py_None) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsTrue) + #define __Pyx_Py_IsTrue(ob) Py_IsTrue(ob) +#else + #define __Pyx_Py_IsTrue(ob) __Pyx_Py_Is((ob), Py_True) +#endif +#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsFalse) + #define __Pyx_Py_IsFalse(ob) Py_IsFalse(ob) +#else + #define __Pyx_Py_IsFalse(ob) __Pyx_Py_Is((ob), Py_False) +#endif +#define __Pyx_NoneAsNull(obj) (__Pyx_Py_IsNone(obj) ? NULL : (obj)) +#if PY_VERSION_HEX >= 0x030900F0 && !CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyObject_GC_IsFinalized(o) PyObject_GC_IsFinalized(o) +#else + #define __Pyx_PyObject_GC_IsFinalized(o) _PyGC_FINALIZED(o) +#endif +#ifndef Py_TPFLAGS_CHECKTYPES + #define Py_TPFLAGS_CHECKTYPES 0 +#endif +#ifndef Py_TPFLAGS_HAVE_INDEX + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#ifndef Py_TPFLAGS_SEQUENCE + #define Py_TPFLAGS_SEQUENCE 0 +#endif +#ifndef Py_TPFLAGS_MAPPING + #define Py_TPFLAGS_MAPPING 0 +#endif +#ifndef METH_STACKLESS + #define METH_STACKLESS 0 +#endif +#ifndef METH_FASTCALL + #ifndef METH_FASTCALL + #define METH_FASTCALL 0x80 + #endif + typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); + typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, + Py_ssize_t nargs, PyObject *kwnames); +#else + #if PY_VERSION_HEX >= 0x030d00A4 + # define __Pyx_PyCFunctionFast PyCFunctionFast + # define __Pyx_PyCFunctionFastWithKeywords PyCFunctionFastWithKeywords + #else + # define __Pyx_PyCFunctionFast _PyCFunctionFast + # define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords + #endif +#endif +#if CYTHON_METH_FASTCALL + #define __Pyx_METH_FASTCALL METH_FASTCALL + #define __Pyx_PyCFunction_FastCall __Pyx_PyCFunctionFast + #define __Pyx_PyCFunction_FastCallWithKeywords __Pyx_PyCFunctionFastWithKeywords +#else + #define __Pyx_METH_FASTCALL METH_VARARGS + #define __Pyx_PyCFunction_FastCall PyCFunction + #define __Pyx_PyCFunction_FastCallWithKeywords PyCFunctionWithKeywords +#endif +#if CYTHON_VECTORCALL + #define __pyx_vectorcallfunc vectorcallfunc + #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET PY_VECTORCALL_ARGUMENTS_OFFSET + #define __Pyx_PyVectorcall_NARGS(n) PyVectorcall_NARGS((size_t)(n)) +#elif CYTHON_BACKPORT_VECTORCALL + typedef PyObject *(*__pyx_vectorcallfunc)(PyObject *callable, PyObject *const *args, + size_t nargsf, PyObject *kwnames); + #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET ((size_t)1 << (8 * sizeof(size_t) - 1)) + #define __Pyx_PyVectorcall_NARGS(n) ((Py_ssize_t)(((size_t)(n)) & ~__Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET)) +#else + #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET 0 + #define __Pyx_PyVectorcall_NARGS(n) ((Py_ssize_t)(n)) +#endif +#if PY_VERSION_HEX >= 0x030900B1 +#define __Pyx_PyCFunction_CheckExact(func) PyCFunction_CheckExact(func) +#else +#define __Pyx_PyCFunction_CheckExact(func) PyCFunction_Check(func) +#endif +#define __Pyx_CyOrPyCFunction_Check(func) PyCFunction_Check(func) +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_CyOrPyCFunction_GET_FUNCTION(func) (((PyCFunctionObject*)(func))->m_ml->ml_meth) +#elif !CYTHON_COMPILING_IN_LIMITED_API +#define __Pyx_CyOrPyCFunction_GET_FUNCTION(func) PyCFunction_GET_FUNCTION(func) +#endif +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_CyOrPyCFunction_GET_FLAGS(func) (((PyCFunctionObject*)(func))->m_ml->ml_flags) +static CYTHON_INLINE PyObject* __Pyx_CyOrPyCFunction_GET_SELF(PyObject *func) { + return (__Pyx_CyOrPyCFunction_GET_FLAGS(func) & METH_STATIC) ? NULL : ((PyCFunctionObject*)func)->m_self; +} +#endif +static CYTHON_INLINE int __Pyx__IsSameCFunction(PyObject *func, void (*cfunc)(void)) { +#if CYTHON_COMPILING_IN_LIMITED_API + return PyCFunction_Check(func) && PyCFunction_GetFunction(func) == (PyCFunction) cfunc; +#else + return PyCFunction_Check(func) && PyCFunction_GET_FUNCTION(func) == (PyCFunction) cfunc; +#endif +} +#define __Pyx_IsSameCFunction(func, cfunc) __Pyx__IsSameCFunction(func, cfunc) +#if __PYX_LIMITED_VERSION_HEX < 0x03090000 + #define __Pyx_PyType_FromModuleAndSpec(m, s, b) ((void)m, PyType_FromSpecWithBases(s, b)) + typedef PyObject *(*__Pyx_PyCMethod)(PyObject *, PyTypeObject *, PyObject *const *, size_t, PyObject *); +#else + #define __Pyx_PyType_FromModuleAndSpec(m, s, b) PyType_FromModuleAndSpec(m, s, b) + #define __Pyx_PyCMethod PyCMethod +#endif +#ifndef METH_METHOD + #define METH_METHOD 0x200 +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) + #define PyObject_Malloc(s) PyMem_Malloc(s) + #define PyObject_Free(p) PyMem_Free(p) + #define PyObject_Realloc(p) PyMem_Realloc(p) +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) +#elif CYTHON_COMPILING_IN_GRAAL + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) _PyFrame_SetLineNumber((frame), (lineno)) +#else + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_PyThreadState_Current PyThreadState_Get() +#elif !CYTHON_FAST_THREAD_STATE + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#elif PY_VERSION_HEX >= 0x030d00A1 + #define __Pyx_PyThreadState_Current PyThreadState_GetUnchecked() +#else + #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() +#endif +#if CYTHON_USE_MODULE_STATE +static CYTHON_INLINE void *__Pyx__PyModule_GetState(PyObject *op) +{ + void *result; + result = PyModule_GetState(op); + if (!result) + Py_FatalError("Couldn't find the module state"); + return result; +} +#define __Pyx_PyModule_GetState(o) (__pyx_mstatetype *)__Pyx__PyModule_GetState(o) +#else +#define __Pyx_PyModule_GetState(op) ((void)op,__pyx_mstate_global) +#endif +#define __Pyx_PyObject_GetSlot(obj, name, func_ctype) __Pyx_PyType_GetSlot(Py_TYPE((PyObject *) obj), name, func_ctype) +#define __Pyx_PyObject_TryGetSlot(obj, name, func_ctype) __Pyx_PyType_TryGetSlot(Py_TYPE(obj), name, func_ctype) +#define __Pyx_PyObject_GetSubSlot(obj, sub, name, func_ctype) __Pyx_PyType_GetSubSlot(Py_TYPE(obj), sub, name, func_ctype) +#define __Pyx_PyObject_TryGetSubSlot(obj, sub, name, func_ctype) __Pyx_PyType_TryGetSubSlot(Py_TYPE(obj), sub, name, func_ctype) +#if CYTHON_USE_TYPE_SLOTS + #define __Pyx_PyType_GetSlot(type, name, func_ctype) ((type)->name) + #define __Pyx_PyType_TryGetSlot(type, name, func_ctype) __Pyx_PyType_GetSlot(type, name, func_ctype) + #define __Pyx_PyType_GetSubSlot(type, sub, name, func_ctype) (((type)->sub) ? ((type)->sub->name) : NULL) + #define __Pyx_PyType_TryGetSubSlot(type, sub, name, func_ctype) __Pyx_PyType_GetSubSlot(type, sub, name, func_ctype) +#else + #define __Pyx_PyType_GetSlot(type, name, func_ctype) ((func_ctype) PyType_GetSlot((type), Py_##name)) + #define __Pyx_PyType_TryGetSlot(type, name, func_ctype)\ + ((__PYX_LIMITED_VERSION_HEX >= 0x030A0000 ||\ + (PyType_GetFlags(type) & Py_TPFLAGS_HEAPTYPE) || __Pyx_get_runtime_version() >= 0x030A0000) ?\ + __Pyx_PyType_GetSlot(type, name, func_ctype) : NULL) + #define __Pyx_PyType_GetSubSlot(obj, sub, name, func_ctype) __Pyx_PyType_GetSlot(obj, name, func_ctype) + #define __Pyx_PyType_TryGetSubSlot(obj, sub, name, func_ctype) __Pyx_PyType_TryGetSlot(obj, name, func_ctype) +#endif +#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) +#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) +#else +#define __Pyx_PyDict_NewPresized(n) PyDict_New() +#endif +#define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) +#define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_UNICODE_INTERNALS +#define __Pyx_PyDict_GetItemStrWithError(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) +static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStr(PyObject *dict, PyObject *name) { + PyObject *res = __Pyx_PyDict_GetItemStrWithError(dict, name); + if (res == NULL) PyErr_Clear(); + return res; +} +#elif !CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07020000 +#define __Pyx_PyDict_GetItemStrWithError PyDict_GetItemWithError +#define __Pyx_PyDict_GetItemStr PyDict_GetItem +#else +static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStrWithError(PyObject *dict, PyObject *name) { +#if CYTHON_COMPILING_IN_PYPY + return PyDict_GetItem(dict, name); +#else + PyDictEntry *ep; + PyDictObject *mp = (PyDictObject*) dict; + long hash = ((PyStringObject *) name)->ob_shash; + assert(hash != -1); + ep = (mp->ma_lookup)(mp, name, hash); + if (ep == NULL) { + return NULL; + } + return ep->me_value; +#endif +} +#define __Pyx_PyDict_GetItemStr PyDict_GetItem +#endif +#if CYTHON_USE_TYPE_SLOTS + #define __Pyx_PyType_GetFlags(tp) (((PyTypeObject *)tp)->tp_flags) + #define __Pyx_PyType_HasFeature(type, feature) ((__Pyx_PyType_GetFlags(type) & (feature)) != 0) +#else + #define __Pyx_PyType_GetFlags(tp) (PyType_GetFlags((PyTypeObject *)tp)) + #define __Pyx_PyType_HasFeature(type, feature) PyType_HasFeature(type, feature) +#endif +#define __Pyx_PyObject_GetIterNextFunc(iterator) __Pyx_PyObject_GetSlot(iterator, tp_iternext, iternextfunc) +#if CYTHON_USE_TYPE_SPECS && PY_VERSION_HEX >= 0x03080000 +#define __Pyx_PyHeapTypeObject_GC_Del(obj) {\ + PyTypeObject *type = Py_TYPE((PyObject*)obj);\ + assert(__Pyx_PyType_HasFeature(type, Py_TPFLAGS_HEAPTYPE));\ + PyObject_GC_Del(obj);\ + Py_DECREF(type);\ +} +#else +#define __Pyx_PyHeapTypeObject_GC_Del(obj) PyObject_GC_Del(obj) +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_ReadChar(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((void)u, 1114111U) + #define __Pyx_PyUnicode_KIND(u) ((void)u, (0)) + #define __Pyx_PyUnicode_DATA(u) ((void*)u) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)k, PyUnicode_ReadChar((PyObject*)(d), i)) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GetLength(u)) +#else + #if PY_VERSION_HEX >= 0x030C0000 + #define __Pyx_PyUnicode_READY(op) (0) + #else + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #endif + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) + #define __Pyx_PyUnicode_KIND(u) ((int)PyUnicode_KIND(u)) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, (Py_UCS4) ch) + #if PY_VERSION_HEX >= 0x030C0000 + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_LENGTH(u)) + #else + #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03090000 + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : ((PyCompactUnicodeObject *)(u))->wstr_length)) + #else + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) + #endif + #endif +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #if !defined(PyUnicode_DecodeUnicodeEscape) + #define PyUnicode_DecodeUnicodeEscape(s, size, errors) PyUnicode_Decode(s, size, "unicode_escape", errors) + #endif + #if !defined(PyUnicode_Contains) + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) + #endif + #if !defined(PyByteArray_Check) + #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) + #endif + #if !defined(PyObject_Format) + #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) + #endif +#endif +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if CYTHON_COMPILING_IN_CPYTHON + #define __Pyx_PySequence_ListKeepNew(obj)\ + (likely(PyList_CheckExact(obj) && Py_REFCNT(obj) == 1) ? __Pyx_NewRef(obj) : PySequence_List(obj)) +#else + #define __Pyx_PySequence_ListKeepNew(obj) PySequence_List(obj) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) __Pyx_IS_TYPE(obj, &PySet_Type) +#endif +#if PY_VERSION_HEX >= 0x030900A4 + #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt) + #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size) +#else + #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt) + #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size) +#endif +#if CYTHON_AVOID_BORROWED_REFS || CYTHON_AVOID_THREAD_UNSAFE_BORROWED_REFS + #if __PYX_LIMITED_VERSION_HEX >= 0x030d0000 + #define __Pyx_PyList_GetItemRef(o, i) PyList_GetItemRef(o, i) + #elif CYTHON_COMPILING_IN_LIMITED_API || !CYTHON_ASSUME_SAFE_MACROS + #define __Pyx_PyList_GetItemRef(o, i) (likely((i) >= 0) ? PySequence_GetItem(o, i) : (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) + #else + #define __Pyx_PyList_GetItemRef(o, i) PySequence_ITEM(o, i) + #endif +#elif CYTHON_COMPILING_IN_LIMITED_API || !CYTHON_ASSUME_SAFE_MACROS + #if __PYX_LIMITED_VERSION_HEX >= 0x030d0000 + #define __Pyx_PyList_GetItemRef(o, i) PyList_GetItemRef(o, i) + #else + #define __Pyx_PyList_GetItemRef(o, i) __Pyx_XNewRef(PyList_GetItem(o, i)) + #endif +#else + #define __Pyx_PyList_GetItemRef(o, i) __Pyx_NewRef(PyList_GET_ITEM(o, i)) +#endif +#if __PYX_LIMITED_VERSION_HEX >= 0x030d0000 +#define __Pyx_PyDict_GetItemRef(dict, key, result) PyDict_GetItemRef(dict, key, result) +#elif CYTHON_AVOID_BORROWED_REFS || CYTHON_AVOID_THREAD_UNSAFE_BORROWED_REFS +static CYTHON_INLINE int __Pyx_PyDict_GetItemRef(PyObject *dict, PyObject *key, PyObject **result) { + *result = PyObject_GetItem(dict, key); + if (*result == NULL) { + if (PyErr_ExceptionMatches(PyExc_KeyError)) { + PyErr_Clear(); + return 0; + } + return -1; + } + return 1; +} +#else +static CYTHON_INLINE int __Pyx_PyDict_GetItemRef(PyObject *dict, PyObject *key, PyObject **result) { + *result = PyDict_GetItemWithError(dict, key); + if (*result == NULL) { + return PyErr_Occurred() ? -1 : 0; + } + Py_INCREF(*result); + return 1; +} +#endif +#if defined(CYTHON_DEBUG_VISIT_CONST) && CYTHON_DEBUG_VISIT_CONST + #define __Pyx_VISIT_CONST(obj) Py_VISIT(obj) +#else + #define __Pyx_VISIT_CONST(obj) +#endif +#if CYTHON_ASSUME_SAFE_MACROS + #define __Pyx_PySequence_ITEM(o, i) PySequence_ITEM(o, i) + #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) + #define __Pyx_PyTuple_SET_ITEM(o, i, v) (PyTuple_SET_ITEM(o, i, v), (0)) + #define __Pyx_PyTuple_GET_ITEM(o, i) PyTuple_GET_ITEM(o, i) + #define __Pyx_PyList_SET_ITEM(o, i, v) (PyList_SET_ITEM(o, i, v), (0)) + #define __Pyx_PyList_GET_ITEM(o, i) PyList_GET_ITEM(o, i) +#else + #define __Pyx_PySequence_ITEM(o, i) PySequence_GetItem(o, i) + #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) + #define __Pyx_PyTuple_SET_ITEM(o, i, v) PyTuple_SetItem(o, i, v) + #define __Pyx_PyTuple_GET_ITEM(o, i) PyTuple_GetItem(o, i) + #define __Pyx_PyList_SET_ITEM(o, i, v) PyList_SetItem(o, i, v) + #define __Pyx_PyList_GET_ITEM(o, i) PyList_GetItem(o, i) +#endif +#if CYTHON_ASSUME_SAFE_SIZE + #define __Pyx_PyTuple_GET_SIZE(o) PyTuple_GET_SIZE(o) + #define __Pyx_PyList_GET_SIZE(o) PyList_GET_SIZE(o) + #define __Pyx_PySet_GET_SIZE(o) PySet_GET_SIZE(o) + #define __Pyx_PyBytes_GET_SIZE(o) PyBytes_GET_SIZE(o) + #define __Pyx_PyByteArray_GET_SIZE(o) PyByteArray_GET_SIZE(o) + #define __Pyx_PyUnicode_GET_LENGTH(o) PyUnicode_GET_LENGTH(o) +#else + #define __Pyx_PyTuple_GET_SIZE(o) PyTuple_Size(o) + #define __Pyx_PyList_GET_SIZE(o) PyList_Size(o) + #define __Pyx_PySet_GET_SIZE(o) PySet_Size(o) + #define __Pyx_PyBytes_GET_SIZE(o) PyBytes_Size(o) + #define __Pyx_PyByteArray_GET_SIZE(o) PyByteArray_Size(o) + #define __Pyx_PyUnicode_GET_LENGTH(o) PyUnicode_GetLength(o) +#endif +#if __PYX_LIMITED_VERSION_HEX >= 0x030d0000 + #define __Pyx_PyImport_AddModuleRef(name) PyImport_AddModuleRef(name) +#else + static CYTHON_INLINE PyObject *__Pyx_PyImport_AddModuleRef(const char *name) { + PyObject *module = PyImport_AddModule(name); + Py_XINCREF(module); + return module; + } +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_InternFromString) + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) +#endif +#define __Pyx_PyLong_FromHash_t PyLong_FromSsize_t +#define __Pyx_PyLong_AsHash_t __Pyx_PyIndex_AsSsize_t +#if __PYX_LIMITED_VERSION_HEX >= 0x030A0000 + #define __Pyx_PySendResult PySendResult +#else + typedef enum { + PYGEN_RETURN = 0, + PYGEN_ERROR = -1, + PYGEN_NEXT = 1, + } __Pyx_PySendResult; +#endif +#if CYTHON_COMPILING_IN_LIMITED_API || PY_VERSION_HEX < 0x030A00A3 + typedef __Pyx_PySendResult (*__Pyx_pyiter_sendfunc)(PyObject *iter, PyObject *value, PyObject **result); +#else + #define __Pyx_pyiter_sendfunc sendfunc +#endif +#if !CYTHON_USE_AM_SEND +#define __PYX_HAS_PY_AM_SEND 0 +#elif __PYX_LIMITED_VERSION_HEX >= 0x030A0000 +#define __PYX_HAS_PY_AM_SEND 1 +#else +#define __PYX_HAS_PY_AM_SEND 2 // our own backported implementation +#endif +#if __PYX_HAS_PY_AM_SEND < 2 + #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods +#else + typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; + __Pyx_pyiter_sendfunc am_send; + } __Pyx_PyAsyncMethodsStruct; + #define __Pyx_SlotTpAsAsync(s) ((PyAsyncMethods*)(s)) +#endif +#if CYTHON_USE_AM_SEND && PY_VERSION_HEX < 0x030A00F0 + #define __Pyx_TPFLAGS_HAVE_AM_SEND (1UL << 21) +#else + #define __Pyx_TPFLAGS_HAVE_AM_SEND (0) +#endif +#if PY_VERSION_HEX >= 0x03090000 +#define __Pyx_PyInterpreterState_Get() PyInterpreterState_Get() +#else +#define __Pyx_PyInterpreterState_Get() PyThreadState_Get()->interp +#endif +#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030A0000 +#ifdef __cplusplus +extern "C" +#endif +PyAPI_FUNC(void *) PyMem_Calloc(size_t nelem, size_t elsize); +#endif +#if CYTHON_COMPILING_IN_LIMITED_API +static int __Pyx_init_co_variable(PyObject *inspect, const char* name, int *write_to) { + int value; + PyObject *py_value = PyObject_GetAttrString(inspect, name); + if (!py_value) return 0; + value = (int) PyLong_AsLong(py_value); + Py_DECREF(py_value); + *write_to = value; + return value != -1 || !PyErr_Occurred(); +} +static int __Pyx_init_co_variables(void) { + PyObject *inspect; + int result; + inspect = PyImport_ImportModule("inspect"); + result = +#if !defined(CO_OPTIMIZED) + __Pyx_init_co_variable(inspect, "CO_OPTIMIZED", &CO_OPTIMIZED) && +#endif +#if !defined(CO_NEWLOCALS) + __Pyx_init_co_variable(inspect, "CO_NEWLOCALS", &CO_NEWLOCALS) && +#endif +#if !defined(CO_VARARGS) + __Pyx_init_co_variable(inspect, "CO_VARARGS", &CO_VARARGS) && +#endif +#if !defined(CO_VARKEYWORDS) + __Pyx_init_co_variable(inspect, "CO_VARKEYWORDS", &CO_VARKEYWORDS) && +#endif +#if !defined(CO_ASYNC_GENERATOR) + __Pyx_init_co_variable(inspect, "CO_ASYNC_GENERATOR", &CO_ASYNC_GENERATOR) && +#endif +#if !defined(CO_GENERATOR) + __Pyx_init_co_variable(inspect, "CO_GENERATOR", &CO_GENERATOR) && +#endif +#if !defined(CO_COROUTINE) + __Pyx_init_co_variable(inspect, "CO_COROUTINE", &CO_COROUTINE) && +#endif + 1; + Py_DECREF(inspect); + return result ? 0 : -1; +} +#else +static int __Pyx_init_co_variables(void) { + return 0; // It's a limited API-only feature +} +#endif + +/* MathInitCode */ +#if defined(_WIN32) || defined(WIN32) || defined(MS_WINDOWS) + #ifndef _USE_MATH_DEFINES + #define _USE_MATH_DEFINES + #endif +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif +#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) +#define __Pyx_truncl trunc +#else +#define __Pyx_truncl truncl +#endif + +#ifndef CYTHON_CLINE_IN_TRACEBACK_RUNTIME +#define CYTHON_CLINE_IN_TRACEBACK_RUNTIME 0 +#endif +#ifndef CYTHON_CLINE_IN_TRACEBACK +#define CYTHON_CLINE_IN_TRACEBACK CYTHON_CLINE_IN_TRACEBACK_RUNTIME +#endif +#if CYTHON_CLINE_IN_TRACEBACK +#define __PYX_MARK_ERR_POS(f_index, lineno) { __pyx_filename = __pyx_f[f_index]; (void) __pyx_filename; __pyx_lineno = lineno; (void) __pyx_lineno; __pyx_clineno = __LINE__; (void) __pyx_clineno; } +#else +#define __PYX_MARK_ERR_POS(f_index, lineno) { __pyx_filename = __pyx_f[f_index]; (void) __pyx_filename; __pyx_lineno = lineno; (void) __pyx_lineno; (void) __pyx_clineno; } +#endif +#define __PYX_ERR(f_index, lineno, Ln_error) \ + { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; } + +#ifdef CYTHON_EXTERN_C + #undef __PYX_EXTERN_C + #define __PYX_EXTERN_C CYTHON_EXTERN_C +#elif defined(__PYX_EXTERN_C) + #ifdef _MSC_VER + #pragma message ("Please do not define the '__PYX_EXTERN_C' macro externally. Use 'CYTHON_EXTERN_C' instead.") + #else + #warning Please do not define the '__PYX_EXTERN_C' macro externally. Use 'CYTHON_EXTERN_C' instead. + #endif +#else + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#define __PYX_HAVE__matris__graph__cygraph +#define __PYX_HAVE_API__matris__graph__cygraph +/* Early includes */ +#include +#include +#include "fast_converter_libraries/create_graph.c" +#include "pythread.h" +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0 +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) { + return (size_t) i < (size_t) limit; +} +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) + #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s); +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +static CYTHON_INLINE PyObject* __Pyx_PyByteArray_FromString(const char*); +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if CYTHON_ASSUME_SAFE_MACROS + #define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) + #define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) + #define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) + #define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) + #define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) + #define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) + #define __Pyx_PyByteArray_AsString(s) PyByteArray_AS_STRING(s) +#else + #define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AsString(s)) + #define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AsString(s)) + #define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AsString(s)) + #define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AsString(s)) + #define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AsString(s)) + #define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AsString(s)) + #define __Pyx_PyByteArray_AsString(s) PyByteArray_AsString(s) +#endif +#define __Pyx_PyObject_AsWritableString(s) ((char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableSString(s) ((signed char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +#define __Pyx_PyUnicode_FromOrdinal(o) PyUnicode_FromOrdinal((int)o) +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +static CYTHON_INLINE PyObject *__Pyx_NewRef(PyObject *obj) { +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030a0000 || defined(Py_NewRef) + return Py_NewRef(obj); +#else + Py_INCREF(obj); + return obj; +#endif +} +static CYTHON_INLINE PyObject *__Pyx_XNewRef(PyObject *obj) { +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030a0000 || defined(Py_XNewRef) + return Py_XNewRef(obj); +#else + Py_XINCREF(obj); + return obj; +#endif +} +static CYTHON_INLINE PyObject *__Pyx_Owned_Py_None(int b); +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Long(PyObject* x); +#define __Pyx_PySequence_Tuple(obj)\ + (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyLong_FromSize_t(size_t); +static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*); +#if CYTHON_ASSUME_SAFE_MACROS +#define __Pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#define __Pyx_PyFloat_AS_DOUBLE(x) PyFloat_AS_DOUBLE(x) +#else +#define __Pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#define __Pyx_PyFloat_AS_DOUBLE(x) PyFloat_AsDouble(x) +#endif +#define __Pyx_PyFloat_AsFloat(x) ((float) __Pyx_PyFloat_AsDouble(x)) +#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) +#if CYTHON_USE_PYLONG_INTERNALS + #if PY_VERSION_HEX >= 0x030C00A7 + #ifndef _PyLong_SIGN_MASK + #define _PyLong_SIGN_MASK 3 + #endif + #ifndef _PyLong_NON_SIZE_BITS + #define _PyLong_NON_SIZE_BITS 3 + #endif + #define __Pyx_PyLong_Sign(x) (((PyLongObject*)x)->long_value.lv_tag & _PyLong_SIGN_MASK) + #define __Pyx_PyLong_IsNeg(x) ((__Pyx_PyLong_Sign(x) & 2) != 0) + #define __Pyx_PyLong_IsNonNeg(x) (!__Pyx_PyLong_IsNeg(x)) + #define __Pyx_PyLong_IsZero(x) (__Pyx_PyLong_Sign(x) & 1) + #define __Pyx_PyLong_IsPos(x) (__Pyx_PyLong_Sign(x) == 0) + #define __Pyx_PyLong_CompactValueUnsigned(x) (__Pyx_PyLong_Digits(x)[0]) + #define __Pyx_PyLong_DigitCount(x) ((Py_ssize_t) (((PyLongObject*)x)->long_value.lv_tag >> _PyLong_NON_SIZE_BITS)) + #define __Pyx_PyLong_SignedDigitCount(x)\ + ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * __Pyx_PyLong_DigitCount(x)) + #if defined(PyUnstable_Long_IsCompact) && defined(PyUnstable_Long_CompactValue) + #define __Pyx_PyLong_IsCompact(x) PyUnstable_Long_IsCompact((PyLongObject*) x) + #define __Pyx_PyLong_CompactValue(x) PyUnstable_Long_CompactValue((PyLongObject*) x) + #else + #define __Pyx_PyLong_IsCompact(x) (((PyLongObject*)x)->long_value.lv_tag < (2 << _PyLong_NON_SIZE_BITS)) + #define __Pyx_PyLong_CompactValue(x) ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * (Py_ssize_t) __Pyx_PyLong_Digits(x)[0]) + #endif + typedef Py_ssize_t __Pyx_compact_pylong; + typedef size_t __Pyx_compact_upylong; + #else + #define __Pyx_PyLong_IsNeg(x) (Py_SIZE(x) < 0) + #define __Pyx_PyLong_IsNonNeg(x) (Py_SIZE(x) >= 0) + #define __Pyx_PyLong_IsZero(x) (Py_SIZE(x) == 0) + #define __Pyx_PyLong_IsPos(x) (Py_SIZE(x) > 0) + #define __Pyx_PyLong_CompactValueUnsigned(x) ((Py_SIZE(x) == 0) ? 0 : __Pyx_PyLong_Digits(x)[0]) + #define __Pyx_PyLong_DigitCount(x) __Pyx_sst_abs(Py_SIZE(x)) + #define __Pyx_PyLong_SignedDigitCount(x) Py_SIZE(x) + #define __Pyx_PyLong_IsCompact(x) (Py_SIZE(x) == 0 || Py_SIZE(x) == 1 || Py_SIZE(x) == -1) + #define __Pyx_PyLong_CompactValue(x)\ + ((Py_SIZE(x) == 0) ? (sdigit) 0 : ((Py_SIZE(x) < 0) ? -(sdigit)__Pyx_PyLong_Digits(x)[0] : (sdigit)__Pyx_PyLong_Digits(x)[0])) + typedef sdigit __Pyx_compact_pylong; + typedef digit __Pyx_compact_upylong; + #endif + static CYTHON_INLINE int __Pyx_PyLong_CompactAsLong(PyObject *x, long *return_value); + #if PY_VERSION_HEX >= 0x030C00A5 + #define __Pyx_PyLong_Digits(x) (((PyLongObject*)x)->long_value.ob_digit) + #else + #define __Pyx_PyLong_Digits(x) (((PyLongObject*)x)->ob_digit) + #endif +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 + #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#elif __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeASCII(c_str, size, NULL) +#else + #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ +/* PretendToInitialize */ +#ifdef __cplusplus +#if __cplusplus > 201103L +#include +#endif +template +static void __Pyx_pretend_to_initialize(T* ptr) { +#if __cplusplus > 201103L + if ((std::is_trivially_default_constructible::value)) +#endif + *ptr = T(); + (void)ptr; +} +#else +static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } +#endif + + +#if !CYTHON_USE_MODULE_STATE +static PyObject *__pyx_m = NULL; +#endif +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * const __pyx_cfilenm = __FILE__; +static const char *__pyx_filename; + +/* #### Code section: filename_table ### */ + +static const char* const __pyx_f[] = { + "matris/graph/cygraph.pyx", + "", +}; +/* #### Code section: utility_code_proto_before_types ### */ +/* Atomics.proto */ +#include +#ifndef CYTHON_ATOMICS + #define CYTHON_ATOMICS 1 +#endif +#define __PYX_CYTHON_ATOMICS_ENABLED() CYTHON_ATOMICS +#define __PYX_GET_CYTHON_COMPILING_IN_CPYTHON_FREETHREADING() CYTHON_COMPILING_IN_CPYTHON_FREETHREADING +#define __pyx_atomic_int_type int +#define __pyx_nonatomic_int_type int +#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\ + (__STDC_VERSION__ >= 201112L) &&\ + !defined(__STDC_NO_ATOMICS__)) + #include +#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\ + (__cplusplus >= 201103L) ||\ + (defined(_MSC_VER) && _MSC_VER >= 1700))) + #include +#endif +#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\ + (__STDC_VERSION__ >= 201112L) &&\ + !defined(__STDC_NO_ATOMICS__) &&\ + ATOMIC_INT_LOCK_FREE == 2) + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type atomic_int + #define __pyx_atomic_ptr_type atomic_uintptr_t + #define __pyx_nonatomic_ptr_type uintptr_t + #define __pyx_atomic_incr_relaxed(value) atomic_fetch_add_explicit(value, 1, memory_order_relaxed) + #define __pyx_atomic_incr_acq_rel(value) atomic_fetch_add_explicit(value, 1, memory_order_acq_rel) + #define __pyx_atomic_decr_acq_rel(value) atomic_fetch_sub_explicit(value, 1, memory_order_acq_rel) + #define __pyx_atomic_sub(value, arg) atomic_fetch_sub(value, arg) + #define __pyx_atomic_int_cmp_exchange(value, expected, desired) atomic_compare_exchange_strong(value, expected, desired) + #define __pyx_atomic_load(value) atomic_load(value) + #define __pyx_atomic_store(value, new_value) atomic_store(value, new_value) + #define __pyx_atomic_pointer_load_relaxed(value) atomic_load_explicit(value, memory_order_relaxed) + #define __pyx_atomic_pointer_load_acquire(value) atomic_load_explicit(value, memory_order_acquire) + #define __pyx_atomic_pointer_exchange(value, new_value) atomic_exchange(value, (__pyx_nonatomic_ptr_type)new_value) + #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER) + #pragma message ("Using standard C atomics") + #elif defined(__PYX_DEBUG_ATOMICS) + #warning "Using standard C atomics" + #endif +#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\ + (__cplusplus >= 201103L) ||\ +\ + (defined(_MSC_VER) && _MSC_VER >= 1700)) &&\ + ATOMIC_INT_LOCK_FREE == 2) + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type std::atomic_int + #define __pyx_atomic_ptr_type std::atomic_uintptr_t + #define __pyx_nonatomic_ptr_type uintptr_t + #define __pyx_atomic_incr_relaxed(value) std::atomic_fetch_add_explicit(value, 1, std::memory_order_relaxed) + #define __pyx_atomic_incr_acq_rel(value) std::atomic_fetch_add_explicit(value, 1, std::memory_order_acq_rel) + #define __pyx_atomic_decr_acq_rel(value) std::atomic_fetch_sub_explicit(value, 1, std::memory_order_acq_rel) + #define __pyx_atomic_sub(value, arg) std::atomic_fetch_sub(value, arg) + #define __pyx_atomic_int_cmp_exchange(value, expected, desired) std::atomic_compare_exchange_strong(value, expected, desired) + #define __pyx_atomic_load(value) std::atomic_load(value) + #define __pyx_atomic_store(value, new_value) std::atomic_store(value, new_value) + #define __pyx_atomic_pointer_load_relaxed(value) std::atomic_load_explicit(value, std::memory_order_relaxed) + #define __pyx_atomic_pointer_load_acquire(value) std::atomic_load_explicit(value, std::memory_order_acquire) + #define __pyx_atomic_pointer_exchange(value, new_value) std::atomic_exchange(value, (__pyx_nonatomic_ptr_type)new_value) + #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER) + #pragma message ("Using standard C++ atomics") + #elif defined(__PYX_DEBUG_ATOMICS) + #warning "Using standard C++ atomics" + #endif +#elif CYTHON_ATOMICS && (__GNUC__ >= 5 || (__GNUC__ == 4 &&\ + (__GNUC_MINOR__ > 1 ||\ + (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL__ >= 2)))) + #define __pyx_atomic_ptr_type void* + #define __pyx_atomic_incr_relaxed(value) __sync_fetch_and_add(value, 1) + #define __pyx_atomic_incr_acq_rel(value) __sync_fetch_and_add(value, 1) + #define __pyx_atomic_decr_acq_rel(value) __sync_fetch_and_sub(value, 1) + #define __pyx_atomic_sub(value, arg) __sync_fetch_and_sub(value, arg) + static CYTHON_INLINE int __pyx_atomic_int_cmp_exchange(__pyx_atomic_int_type* value, __pyx_nonatomic_int_type* expected, __pyx_nonatomic_int_type desired) { + __pyx_nonatomic_int_type old = __sync_val_compare_and_swap(value, *expected, desired); + int result = old == *expected; + *expected = old; + return result; + } + #define __pyx_atomic_load(value) __sync_fetch_and_add(value, 0) + #define __pyx_atomic_store(value, new_value) __sync_lock_test_and_set(value, new_value) + #define __pyx_atomic_pointer_load_relaxed(value) __sync_fetch_and_add(value, 0) + #define __pyx_atomic_pointer_load_acquire(value) __sync_fetch_and_add(value, 0) + #define __pyx_atomic_pointer_exchange(value, new_value) __sync_lock_test_and_set(value, (__pyx_atomic_ptr_type)new_value) + #ifdef __PYX_DEBUG_ATOMICS + #warning "Using GNU atomics" + #endif +#elif CYTHON_ATOMICS && defined(_MSC_VER) + #include + #undef __pyx_atomic_int_type + #define __pyx_atomic_int_type long + #define __pyx_atomic_ptr_type void* + #undef __pyx_nonatomic_int_type + #define __pyx_nonatomic_int_type long + #pragma intrinsic (_InterlockedExchangeAdd, _InterlockedExchange, _InterlockedCompareExchange, _InterlockedCompareExchangePointer, _InterlockedExchangePointer) + #define __pyx_atomic_incr_relaxed(value) _InterlockedExchangeAdd(value, 1) + #define __pyx_atomic_incr_acq_rel(value) _InterlockedExchangeAdd(value, 1) + #define __pyx_atomic_decr_acq_rel(value) _InterlockedExchangeAdd(value, -1) + #define __pyx_atomic_sub(value, arg) _InterlockedExchangeAdd(value, -arg) + static CYTHON_INLINE int __pyx_atomic_int_cmp_exchange(__pyx_atomic_int_type* value, __pyx_nonatomic_int_type* expected, __pyx_nonatomic_int_type desired) { + __pyx_nonatomic_int_type old = _InterlockedCompareExchange(value, desired, *expected); + int result = old == *expected; + *expected = old; + return result; + } + #define __pyx_atomic_load(value) _InterlockedExchangeAdd(value, 0) + #define __pyx_atomic_store(value, new_value) _InterlockedExchange(value, new_value) + #define __pyx_atomic_pointer_load_relaxed(value) *(void * volatile *)value + #define __pyx_atomic_pointer_load_acquire(value) _InterlockedCompareExchangePointer(value, 0, 0) + #define __pyx_atomic_pointer_exchange(value, new_value) _InterlockedExchangePointer(value, (__pyx_atomic_ptr_type)new_value) + #ifdef __PYX_DEBUG_ATOMICS + #pragma message ("Using MSVC atomics") + #endif +#else + #undef CYTHON_ATOMICS + #define CYTHON_ATOMICS 0 + #ifdef __PYX_DEBUG_ATOMICS + #warning "Not using atomics" + #endif +#endif +#if CYTHON_ATOMICS + #define __pyx_add_acquisition_count(memview)\ + __pyx_atomic_incr_relaxed(__pyx_get_slice_count_pointer(memview)) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_atomic_decr_acq_rel(__pyx_get_slice_count_pointer(memview)) +#else + #define __pyx_add_acquisition_count(memview)\ + __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) + #define __pyx_sub_acquisition_count(memview)\ + __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock) +#endif + +/* ForceInitThreads.proto */ +#ifndef __PYX_FORCE_INIT_THREADS + #define __PYX_FORCE_INIT_THREADS 0 +#endif + +/* NoFastGil.proto */ +#define __Pyx_PyGILState_Ensure PyGILState_Ensure +#define __Pyx_PyGILState_Release PyGILState_Release +#define __Pyx_FastGIL_Remember() +#define __Pyx_FastGIL_Forget() +#define __Pyx_FastGilFuncInit() + +/* IncludeStructmemberH.proto */ +#include + +/* CriticalSections.proto */ +#if !CYTHON_COMPILING_IN_CPYTHON_FREETHREADING +#define __Pyx_PyCriticalSection void* +#define __Pyx_PyCriticalSection2 void* +#define __Pyx_PyCriticalSection_Begin1(cs, arg) (void)cs +#define __Pyx_PyCriticalSection_Begin2(cs, arg1, arg2) (void)cs +#define __Pyx_PyCriticalSection_End1(cs) +#define __Pyx_PyCriticalSection_End2(cs) +#else +#define __Pyx_PyCriticalSection PyCriticalSection +#define __Pyx_PyCriticalSection2 PyCriticalSection2 +#define __Pyx_PyCriticalSection_Begin1 PyCriticalSection_Begin +#define __Pyx_PyCriticalSection_Begin2 PyCriticalSection2_Begin +#define __Pyx_PyCriticalSection_End1 PyCriticalSection_End +#define __Pyx_PyCriticalSection_End2 PyCriticalSection2_End +#endif +#if PY_VERSION_HEX < 0x030d0000 || CYTHON_COMPILING_IN_LIMITED_API +#define __Pyx_BEGIN_CRITICAL_SECTION(o) { +#define __Pyx_END_CRITICAL_SECTION() } +#else +#define __Pyx_BEGIN_CRITICAL_SECTION Py_BEGIN_CRITICAL_SECTION +#define __Pyx_END_CRITICAL_SECTION Py_END_CRITICAL_SECTION +#endif + +/* BufferFormatStructs.proto */ +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; + const struct __Pyx_StructField_* fields; + size_t size; + size_t arraysize[8]; + int ndim; + char typegroup; + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + const __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + const __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + +/* MemviewSliceStruct.proto */ +struct __pyx_memoryview_obj; +typedef struct { + struct __pyx_memoryview_obj *memview; + char *data; + Py_ssize_t shape[8]; + Py_ssize_t strides[8]; + Py_ssize_t suboffsets[8]; +} __Pyx_memviewslice; +#define __Pyx_MemoryView_Len(m) (m.shape[0]) + +/* #### Code section: numeric_typedefs ### */ +/* #### Code section: complex_type_declarations ### */ +/* #### Code section: type_declarations ### */ + +/*--- Type declarations ---*/ +struct __pyx_array_obj; +struct __pyx_MemviewEnum_obj; +struct __pyx_memoryview_obj; +struct __pyx_memoryviewslice_obj; + +/* "View.MemoryView":110 + * + * + * @cython.collection_type("sequence") # <<<<<<<<<<<<<< + * @cname("__pyx_array") + * cdef class array: +*/ +struct __pyx_array_obj { + PyObject_HEAD + struct __pyx_vtabstruct_array *__pyx_vtab; + char *data; + Py_ssize_t len; + char *format; + int ndim; + Py_ssize_t *_shape; + Py_ssize_t *_strides; + Py_ssize_t itemsize; + PyObject *mode; + PyObject *_format; + void (*callback_free_data)(void *); + int free_data; + int dtype_is_object; +}; + + +/* "View.MemoryView":299 + * + * + * @cname('__pyx_MemviewEnum') # <<<<<<<<<<<<<< + * cdef class Enum(object): + * cdef object name +*/ +struct __pyx_MemviewEnum_obj { + PyObject_HEAD + PyObject *name; +}; + + +/* "View.MemoryView":334 + * + * + * @cname('__pyx_memoryview') # <<<<<<<<<<<<<< + * cdef class memoryview: + * +*/ +struct __pyx_memoryview_obj { + PyObject_HEAD + struct __pyx_vtabstruct_memoryview *__pyx_vtab; + PyObject *obj; + PyObject *_size; + PyObject *_array_interface; + PyThread_type_lock lock; + __pyx_atomic_int_type acquisition_count; + Py_buffer view; + int flags; + int dtype_is_object; + __Pyx_TypeInfo const *typeinfo; +}; + + +/* "View.MemoryView":950 + * + * + * @cython.collection_type("sequence") # <<<<<<<<<<<<<< + * @cname('__pyx_memoryviewslice') + * cdef class _memoryviewslice(memoryview): +*/ +struct __pyx_memoryviewslice_obj { + struct __pyx_memoryview_obj __pyx_base; + __Pyx_memviewslice from_slice; + PyObject *from_object; + PyObject *(*to_object_func)(char *); + int (*to_dtype_func)(char *, PyObject *); +}; + + + +/* "View.MemoryView":110 + * + * + * @cython.collection_type("sequence") # <<<<<<<<<<<<<< + * @cname("__pyx_array") + * cdef class array: +*/ + +struct __pyx_vtabstruct_array { + PyObject *(*get_memview)(struct __pyx_array_obj *); +}; +static struct __pyx_vtabstruct_array *__pyx_vtabptr_array; + + +/* "View.MemoryView":334 + * + * + * @cname('__pyx_memoryview') # <<<<<<<<<<<<<< + * cdef class memoryview: + * +*/ + +struct __pyx_vtabstruct_memoryview { + char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); + PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *); + PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *); + PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *); + PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *); + PyObject *(*_get_base)(struct __pyx_memoryview_obj *); +}; +static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview; + + +/* "View.MemoryView":950 + * + * + * @cython.collection_type("sequence") # <<<<<<<<<<<<<< + * @cname('__pyx_memoryviewslice') + * cdef class _memoryviewslice(memoryview): +*/ + +struct __pyx_vtabstruct__memoryviewslice { + struct __pyx_vtabstruct_memoryview __pyx_base; +}; +static struct __pyx_vtabstruct__memoryviewslice *__pyx_vtabptr__memoryviewslice; +/* #### Code section: utility_code_proto ### */ + +/* --- Runtime support code (head) --- */ +/* Refnanny.proto */ +#ifndef CYTHON_REFNANNY + #define CYTHON_REFNANNY 0 +#endif +#if CYTHON_REFNANNY + typedef struct { + void (*INCREF)(void*, PyObject*, Py_ssize_t); + void (*DECREF)(void*, PyObject*, Py_ssize_t); + void (*GOTREF)(void*, PyObject*, Py_ssize_t); + void (*GIVEREF)(void*, PyObject*, Py_ssize_t); + void* (*SetupContext)(const char*, Py_ssize_t, const char*); + void (*FinishContext)(void**); + } __Pyx_RefNannyAPIStruct; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); + #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL; + #define __Pyx_RefNannySetupContext(name, acquire_gil)\ + if (acquire_gil) {\ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), (__LINE__), (__FILE__));\ + PyGILState_Release(__pyx_gilstate_save);\ + } else {\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), (__LINE__), (__FILE__));\ + } + #define __Pyx_RefNannyFinishContextNogil() {\ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ + __Pyx_RefNannyFinishContext();\ + PyGILState_Release(__pyx_gilstate_save);\ + } + #define __Pyx_RefNannyFinishContextNogil() {\ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ + __Pyx_RefNannyFinishContext();\ + PyGILState_Release(__pyx_gilstate_save);\ + } + #define __Pyx_RefNannyFinishContext()\ + __Pyx_RefNanny->FinishContext(&__pyx_refnanny) + #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), (__LINE__)) + #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), (__LINE__)) + #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), (__LINE__)) + #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), (__LINE__)) + #define __Pyx_XINCREF(r) do { if((r) == NULL); else {__Pyx_INCREF(r); }} while(0) + #define __Pyx_XDECREF(r) do { if((r) == NULL); else {__Pyx_DECREF(r); }} while(0) + #define __Pyx_XGOTREF(r) do { if((r) == NULL); else {__Pyx_GOTREF(r); }} while(0) + #define __Pyx_XGIVEREF(r) do { if((r) == NULL); else {__Pyx_GIVEREF(r);}} while(0) +#else + #define __Pyx_RefNannyDeclarations + #define __Pyx_RefNannySetupContext(name, acquire_gil) + #define __Pyx_RefNannyFinishContextNogil() + #define __Pyx_RefNannyFinishContext() + #define __Pyx_INCREF(r) Py_INCREF(r) + #define __Pyx_DECREF(r) Py_DECREF(r) + #define __Pyx_GOTREF(r) + #define __Pyx_GIVEREF(r) + #define __Pyx_XINCREF(r) Py_XINCREF(r) + #define __Pyx_XDECREF(r) Py_XDECREF(r) + #define __Pyx_XGOTREF(r) + #define __Pyx_XGIVEREF(r) +#endif +#define __Pyx_Py_XDECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; Py_XDECREF(tmp);\ + } while (0) +#define __Pyx_XDECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_XDECREF(tmp);\ + } while (0) +#define __Pyx_DECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_DECREF(tmp);\ + } while (0) +#define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) +#define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) + +/* PyErrExceptionMatches.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); +#else +#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) +#endif + +/* PyThreadStateGet.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; +#define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current; +#if PY_VERSION_HEX >= 0x030C00A6 +#define __Pyx_PyErr_Occurred() (__pyx_tstate->current_exception != NULL) +#define __Pyx_PyErr_CurrentExceptionType() (__pyx_tstate->current_exception ? (PyObject*) Py_TYPE(__pyx_tstate->current_exception) : (PyObject*) NULL) +#else +#define __Pyx_PyErr_Occurred() (__pyx_tstate->curexc_type != NULL) +#define __Pyx_PyErr_CurrentExceptionType() (__pyx_tstate->curexc_type) +#endif +#else +#define __Pyx_PyThreadState_declare +#define __Pyx_PyThreadState_assign +#define __Pyx_PyErr_Occurred() (PyErr_Occurred() != NULL) +#define __Pyx_PyErr_CurrentExceptionType() PyErr_Occurred() +#endif + +/* PyErrFetchRestore.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL) +#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A6 +#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) +#else +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#endif +#else +#define __Pyx_PyErr_Clear() PyErr_Clear() +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) +#endif + +/* PyObjectGetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +/* PyObjectGetAttrStrNoError.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name); + +/* GetBuiltinName.proto */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +/* TupleAndListFromArray.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyList_FromArray(PyObject *const *src, Py_ssize_t n); +#endif +#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_METH_FASTCALL +static CYTHON_INLINE PyObject* __Pyx_PyTuple_FromArray(PyObject *const *src, Py_ssize_t n); +#endif + +/* IncludeStringH.proto */ +#include + +/* BytesEquals.proto */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); + +/* UnicodeEquals.proto */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); + +/* fastcall.proto */ +#if CYTHON_AVOID_BORROWED_REFS + #define __Pyx_ArgRef_VARARGS(args, i) __Pyx_PySequence_ITEM(args, i) +#elif CYTHON_ASSUME_SAFE_MACROS + #define __Pyx_ArgRef_VARARGS(args, i) __Pyx_NewRef(__Pyx_PyTuple_GET_ITEM(args, i)) +#else + #define __Pyx_ArgRef_VARARGS(args, i) __Pyx_XNewRef(PyTuple_GetItem(args, i)) +#endif +#define __Pyx_NumKwargs_VARARGS(kwds) PyDict_Size(kwds) +#define __Pyx_KwValues_VARARGS(args, nargs) NULL +#define __Pyx_GetKwValue_VARARGS(kw, kwvalues, s) __Pyx_PyDict_GetItemStrWithError(kw, s) +#define __Pyx_KwargsAsDict_VARARGS(kw, kwvalues) PyDict_Copy(kw) +#if CYTHON_METH_FASTCALL + #define __Pyx_ArgRef_FASTCALL(args, i) __Pyx_NewRef(args[i]) + #define __Pyx_NumKwargs_FASTCALL(kwds) __Pyx_PyTuple_GET_SIZE(kwds) + #define __Pyx_KwValues_FASTCALL(args, nargs) ((args) + (nargs)) + static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s); + #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030d0000 || CYTHON_COMPILING_IN_LIMITED_API + CYTHON_UNUSED static PyObject *__Pyx_KwargsAsDict_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues); + #else + #define __Pyx_KwargsAsDict_FASTCALL(kw, kwvalues) _PyStack_AsDict(kwvalues, kw) + #endif +#else + #define __Pyx_ArgRef_FASTCALL __Pyx_ArgRef_VARARGS + #define __Pyx_NumKwargs_FASTCALL __Pyx_NumKwargs_VARARGS + #define __Pyx_KwValues_FASTCALL __Pyx_KwValues_VARARGS + #define __Pyx_GetKwValue_FASTCALL __Pyx_GetKwValue_VARARGS + #define __Pyx_KwargsAsDict_FASTCALL __Pyx_KwargsAsDict_VARARGS +#endif +#define __Pyx_ArgsSlice_VARARGS(args, start, stop) PyTuple_GetSlice(args, start, stop) +#if CYTHON_METH_FASTCALL || (CYTHON_COMPILING_IN_CPYTHON && CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) +#define __Pyx_ArgsSlice_FASTCALL(args, start, stop) __Pyx_PyTuple_FromArray(args + start, stop - start) +#else +#define __Pyx_ArgsSlice_FASTCALL(args, start, stop) PyTuple_GetSlice(args, start, stop) +#endif + +/* RaiseDoubleKeywords.proto */ +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +/* ParseKeywords.proto */ +static CYTHON_INLINE int __Pyx_ParseKeywords( + PyObject *kwds, PyObject *const *kwvalues, PyObject ** const argnames[], + PyObject *kwds2, PyObject *values[], + Py_ssize_t num_pos_args, Py_ssize_t num_kwargs, + const char* function_name, + int ignore_unknown_kwargs +); + +/* CallCFunction.proto */ +#define __Pyx_CallCFunction(cfunc, self, args)\ + ((PyCFunction)(void(*)(void))(cfunc)->func)(self, args) +#define __Pyx_CallCFunctionWithKeywords(cfunc, self, args, kwargs)\ + ((PyCFunctionWithKeywords)(void(*)(void))(cfunc)->func)(self, args, kwargs) +#define __Pyx_CallCFunctionFast(cfunc, self, args, nargs)\ + ((__Pyx_PyCFunctionFast)(void(*)(void))(PyCFunction)(cfunc)->func)(self, args, nargs) +#define __Pyx_CallCFunctionFastWithKeywords(cfunc, self, args, nargs, kwnames)\ + ((__Pyx_PyCFunctionFastWithKeywords)(void(*)(void))(PyCFunction)(cfunc)->func)(self, args, nargs, kwnames) + +/* PyFunctionFastCall.proto */ +#if CYTHON_FAST_PYCALL +#if !CYTHON_VECTORCALL +#define __Pyx_PyFunction_FastCall(func, args, nargs)\ + __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject *const *args, Py_ssize_t nargs, PyObject *kwargs); +#endif +#define __Pyx_BUILD_ASSERT_EXPR(cond)\ + (sizeof(char [1 - 2*!(cond)]) - 1) +#ifndef Py_MEMBER_SIZE +#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) +#endif +#if !CYTHON_VECTORCALL +#if PY_VERSION_HEX >= 0x03080000 + #include "frameobject.h" + #define __Pxy_PyFrame_Initialize_Offsets() + #define __Pyx_PyFrame_GetLocalsplus(frame) ((frame)->f_localsplus) +#else + static size_t __pyx_pyframe_localsplus_offset = 0; + #include "frameobject.h" + #define __Pxy_PyFrame_Initialize_Offsets()\ + ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ + (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) + #define __Pyx_PyFrame_GetLocalsplus(frame)\ + (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) +#endif +#endif +#endif + +/* PyObjectCall.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +/* PyObjectCallMethO.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); +#endif + +/* PyObjectFastCall.proto */ +#define __Pyx_PyObject_FastCall(func, args, nargs) __Pyx_PyObject_FastCallDict(func, args, (size_t)(nargs), NULL) +static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject * const*args, size_t nargs, PyObject *kwargs); + +/* UnpackUnboundCMethod.proto */ +typedef struct { + PyObject *type; + PyObject **method_name; +#if CYTHON_COMPILING_IN_CPYTHON_FREETHREADING && CYTHON_ATOMICS + __pyx_atomic_int_type initialized; +#endif + PyCFunction func; + PyObject *method; + int flag; +} __Pyx_CachedCFunction; +#if CYTHON_COMPILING_IN_CPYTHON_FREETHREADING +static CYTHON_INLINE int __Pyx_CachedCFunction_GetAndSetInitializing(__Pyx_CachedCFunction *cfunc) { +#if !CYTHON_ATOMICS + return 1; +#else + __pyx_nonatomic_int_type expected = 0; + if (__pyx_atomic_int_cmp_exchange(&cfunc->initialized, &expected, 1)) { + return 0; + } + return expected; +#endif +} +static CYTHON_INLINE void __Pyx_CachedCFunction_SetFinishedInitializing(__Pyx_CachedCFunction *cfunc) { +#if CYTHON_ATOMICS + __pyx_atomic_store(&cfunc->initialized, 2); +#endif +} +#else +#define __Pyx_CachedCFunction_GetAndSetInitializing(cfunc) 2 +#define __Pyx_CachedCFunction_SetFinishedInitializing(cfunc) +#endif + +/* CallUnboundCMethod2.proto */ +CYTHON_UNUSED +static PyObject* __Pyx__CallUnboundCMethod2(__Pyx_CachedCFunction* cfunc, PyObject* self, PyObject* arg1, PyObject* arg2); +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject *__Pyx_CallUnboundCMethod2(__Pyx_CachedCFunction *cfunc, PyObject *self, PyObject *arg1, PyObject *arg2); +#else +#define __Pyx_CallUnboundCMethod2(cfunc, self, arg1, arg2) __Pyx__CallUnboundCMethod2(cfunc, self, arg1, arg2) +#endif + +/* RaiseArgTupleInvalid.proto */ +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +/* ArgTypeTest.proto */ +#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ + ((likely(__Pyx_IS_TYPE(obj, type) | (none_allowed && (obj == Py_None)))) ? 1 :\ + __Pyx__ArgTypeTest(obj, type, name, exact)) +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); + +/* RaiseException.proto */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +/* PyObjectFastCallMethod.proto */ +#if CYTHON_VECTORCALL && PY_VERSION_HEX >= 0x03090000 +#define __Pyx_PyObject_FastCallMethod(name, args, nargsf) PyObject_VectorcallMethod(name, args, nargsf, NULL) +#else +static PyObject *__Pyx_PyObject_FastCallMethod(PyObject *name, PyObject *const *args, size_t nargsf); +#endif + +/* RaiseUnexpectedTypeError.proto */ +static int __Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj); + +/* BuildPyUnicode.proto */ +static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, const char* chars, int clength, + int prepend_sign, char padding_char); + +/* COrdinalToPyUnicode.proto */ +static CYTHON_INLINE int __Pyx_CheckUnicodeValue(int value); +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromOrdinal_Padded(int value, Py_ssize_t width, char padding_char); + +/* GCCDiagnostics.proto */ +#if !defined(__INTEL_COMPILER) && defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)) +#define __Pyx_HAS_GCC_DIAGNOSTIC +#endif + +/* IncludeStdlibH.proto */ +#include + +/* CIntToPyUnicode.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char); + +/* CIntToPyUnicode.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char); + +/* JoinPyUnicode.proto */ +static PyObject* __Pyx_PyUnicode_Join(PyObject** values, Py_ssize_t value_count, Py_ssize_t result_ulength, + Py_UCS4 max_char); + +/* PyObjectFormatSimple.proto */ +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + PyObject_Format(s, f)) +#elif CYTHON_USE_TYPE_SLOTS + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + likely(PyLong_CheckExact(s)) ? PyLong_Type.tp_repr(s) :\ + likely(PyFloat_CheckExact(s)) ? PyFloat_Type.tp_repr(s) :\ + PyObject_Format(s, f)) +#else + #define __Pyx_PyObject_FormatSimple(s, f) (\ + likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ + PyObject_Format(s, f)) +#endif + +CYTHON_UNUSED static int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/ +/* GetAttr.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); + +/* GetItemInt.proto */ +#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck, has_gil)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ + __Pyx_GetItemInt_Generic(o, to_py_func(i)))) +#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck, has_gil)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck, has_gil)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ + (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + int wraparound, int boundscheck); +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, + int is_list, int wraparound, int boundscheck); + +/* PyObjectCallOneArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); + +/* ObjectGetItem.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key); +#else +#define __Pyx_PyObject_GetItem(obj, key) PyObject_GetItem(obj, key) +#endif + +/* RejectKeywords.proto */ +static void __Pyx_RejectKeywords(const char* function_name, PyObject *kwds); + +/* DivInt[Py_ssize_t].proto */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t, int b_is_constant); + +/* UnaryNegOverflows.proto */ +#define __Pyx_UNARY_NEG_WOULD_OVERFLOW(x)\ + (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) + +/* GetAttr3.proto */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); + +/* PyDictVersioning.proto */ +#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS +#define __PYX_DICT_VERSION_INIT ((PY_UINT64_T) -1) +#define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\ + (version_var) = __PYX_GET_DICT_VERSION(dict);\ + (cache_var) = (value); +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ + (VAR) = __pyx_dict_cached_value;\ + } else {\ + (VAR) = __pyx_dict_cached_value = (LOOKUP);\ + __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ + }\ +} +static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj); +static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj); +static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version); +#else +#define __PYX_GET_DICT_VERSION(dict) (0) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var) +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP); +#endif + +/* GetModuleGlobalName.proto */ +#if CYTHON_USE_DICT_VERSIONS +#define __Pyx_GetModuleGlobalName(var, name) do {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_mstate_global->__pyx_d))) ?\ + (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ + __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} while(0) +#define __Pyx_GetModuleGlobalNameUncached(var, name) do {\ + PY_UINT64_T __pyx_dict_version;\ + PyObject *__pyx_dict_cached_value;\ + (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} while(0) +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); +#else +#define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) +#define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name) +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name); +#endif + +/* AssertionsEnabled.proto */ +#if CYTHON_COMPILING_IN_LIMITED_API || (CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030C0000) + static int __pyx_assertions_enabled_flag; + #define __pyx_assertions_enabled() (__pyx_assertions_enabled_flag) + static int __Pyx_init_assertions_enabled(void) { + PyObject *builtins, *debug, *debug_str; + int flag; + builtins = PyEval_GetBuiltins(); + if (!builtins) goto bad; + debug_str = PyUnicode_FromStringAndSize("__debug__", 9); + if (!debug_str) goto bad; + debug = PyObject_GetItem(builtins, debug_str); + Py_DECREF(debug_str); + if (!debug) goto bad; + flag = PyObject_IsTrue(debug); + Py_DECREF(debug); + if (flag == -1) goto bad; + __pyx_assertions_enabled_flag = flag; + return 0; + bad: + __pyx_assertions_enabled_flag = 1; + return -1; + } +#else + #define __Pyx_init_assertions_enabled() (0) + #define __pyx_assertions_enabled() (!Py_OptimizeFlag) +#endif + +/* RaiseTooManyValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +/* RaiseNeedMoreValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +/* RaiseNoneIterError.proto */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +/* ExtTypeTest.proto */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); + +/* GetTopmostException.proto */ +#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE +static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); +#endif + +/* SaveResetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +#else +#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) +#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) +#endif + +/* GetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* SwapException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* Import.proto */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +/* ImportDottedModule.proto */ +static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple); +static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple); + +/* FastTypeChecks.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) +#define __Pyx_TypeCheck2(obj, type1, type2) __Pyx_IsAnySubtype2(Py_TYPE(obj), (PyTypeObject *)type1, (PyTypeObject *)type2) +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); +static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); +#else +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#define __Pyx_TypeCheck2(obj, type1, type2) (PyObject_TypeCheck(obj, (PyTypeObject *)type1) || PyObject_TypeCheck(obj, (PyTypeObject *)type2)) +#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2) { + return PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2); +} +#endif +#define __Pyx_PyErr_ExceptionMatches2(err1, err2) __Pyx_PyErr_GivenExceptionMatches2(__Pyx_PyErr_CurrentExceptionType(), err1, err2) +#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) +#ifdef PyExceptionInstance_Check + #define __Pyx_PyBaseException_Check(obj) PyExceptionInstance_Check(obj) +#else + #define __Pyx_PyBaseException_Check(obj) __Pyx_TypeCheck(obj, PyExc_BaseException) +#endif + +CYTHON_UNUSED static int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ +/* ListCompAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len)) { + Py_INCREF(x); + #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030d0000 + L->ob_item[len] = x; + #else + PyList_SET_ITEM(list, len, x); + #endif + __Pyx_SET_SIZE(list, len + 1); + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) +#endif + +/* PySequenceMultiply.proto */ +#define __Pyx_PySequence_Multiply_Left(mul, seq) __Pyx_PySequence_Multiply(seq, mul) +static CYTHON_INLINE PyObject* __Pyx_PySequence_Multiply(PyObject *seq, Py_ssize_t mul); + +/* PyObjectFormatAndDecref.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_FormatSimpleAndDecref(PyObject* s, PyObject* f); +static CYTHON_INLINE PyObject* __Pyx_PyObject_FormatAndDecref(PyObject* s, PyObject* f); + +/* PyObjectFormat.proto */ +#if CYTHON_USE_UNICODE_WRITER +static PyObject* __Pyx_PyObject_Format(PyObject* s, PyObject* f); +#else +#define __Pyx_PyObject_Format(s, f) PyObject_Format(s, f) +#endif + +/* SetItemInt.proto */ +#define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck, has_gil)\ + (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ + __Pyx_SetItemInt_Fast(o, (Py_ssize_t)i, v, is_list, wraparound, boundscheck) :\ + (is_list ? (PyErr_SetString(PyExc_IndexError, "list assignment index out of range"), -1) :\ + __Pyx_SetItemInt_Generic(o, to_py_func(i), v))) +static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v); +static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, + int is_list, int wraparound, int boundscheck); + +/* RaiseUnboundLocalError.proto */ +static void __Pyx_RaiseUnboundLocalError(const char *varname); + +/* DivInt[long].proto */ +static CYTHON_INLINE long __Pyx_div_long(long, long, int b_is_constant); + +/* PySequenceContains.proto */ +static CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) { + int result = PySequence_Contains(seq, item); + return unlikely(result < 0) ? result : (result == (eq == Py_EQ)); +} + +/* ImportFrom.proto */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); + +/* HasAttr.proto */ +#if __PYX_LIMITED_VERSION_HEX >= 0x030d0000 +#define __Pyx_HasAttr(o, n) PyObject_HasAttrWithError(o, n) +#else +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *); +#endif + +/* BufferIndexError.proto */ +static void __Pyx_RaiseBufferIndexError(int axis); + +/* PyObjectVectorCallKwBuilder.proto */ +CYTHON_UNUSED static int __Pyx_VectorcallBuilder_AddArg_Check(PyObject *key, PyObject *value, PyObject *builder, PyObject **args, int n); +#if CYTHON_VECTORCALL +#if PY_VERSION_HEX >= 0x03090000 +#define __Pyx_Object_Vectorcall_CallFromBuilder PyObject_Vectorcall +#else +#define __Pyx_Object_Vectorcall_CallFromBuilder _PyObject_Vectorcall +#endif +#define __Pyx_MakeVectorcallBuilderKwds(n) PyTuple_New(n) +static int __Pyx_VectorcallBuilder_AddArg(PyObject *key, PyObject *value, PyObject *builder, PyObject **args, int n); +static int __Pyx_VectorcallBuilder_AddArgStr(const char *key, PyObject *value, PyObject *builder, PyObject **args, int n); +#else +#define __Pyx_Object_Vectorcall_CallFromBuilder __Pyx_PyObject_FastCallDict +#define __Pyx_MakeVectorcallBuilderKwds(n) __Pyx_PyDict_NewPresized(n) +#define __Pyx_VectorcallBuilder_AddArg(key, value, builder, args, n) PyDict_SetItem(builder, key, value) +#define __Pyx_VectorcallBuilder_AddArgStr(key, value, builder, args, n) PyDict_SetItemString(builder, key, value) +#endif + +/* ListAppend.proto */ +#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS +static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) { + PyListObject* L = (PyListObject*) list; + Py_ssize_t len = Py_SIZE(list); + if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) { + Py_INCREF(x); + #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030d0000 + L->ob_item[len] = x; + #else + PyList_SET_ITEM(list, len, x); + #endif + __Pyx_SET_SIZE(list, len + 1); + return 0; + } + return PyList_Append(list, x); +} +#else +#define __Pyx_PyList_Append(L,x) PyList_Append(L,x) +#endif + +/* PyObjectCall2Args.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2); + +/* PyObjectGetMethod.proto */ +static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method); + +/* PyObjectCallMethod1.proto */ +static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg); + +/* append.proto */ +static CYTHON_INLINE int __Pyx_PyObject_Append(PyObject* L, PyObject* x); + +/* DictGetItem.proto */ +#if !CYTHON_COMPILING_IN_PYPY +static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key); +#define __Pyx_PyObject_Dict_GetItem(obj, name)\ + (likely(PyDict_CheckExact(obj)) ?\ + __Pyx_PyDict_GetItem(obj, name) : PyObject_GetItem(obj, name)) +#else +#define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key) +#define __Pyx_PyObject_Dict_GetItem(obj, name) PyObject_GetItem(obj, name) +#endif + +/* PyObjectCallNoArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func); + +/* pyfrozenset_new.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyFrozenSet_New(PyObject* it); + +/* PyDictContains.proto */ +static CYTHON_INLINE int __Pyx_PyDict_ContainsTF(PyObject* item, PyObject* dict, int eq) { + int result = PyDict_Contains(dict, item); + return unlikely(result < 0) ? result : (result == (eq == Py_EQ)); +} + +/* CallTypeTraverse.proto */ +#if !CYTHON_USE_TYPE_SPECS || (!CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x03090000) +#define __Pyx_call_type_traverse(o, always_call, visit, arg) 0 +#else +static int __Pyx_call_type_traverse(PyObject *o, int always_call, visitproc visit, void *arg); +#endif + +/* LimitedApiGetTypeDict.proto */ +#if CYTHON_COMPILING_IN_LIMITED_API +static PyObject *__Pyx_GetTypeDict(PyTypeObject *tp); +#endif + +/* SetItemOnTypeDict.proto */ +static int __Pyx__SetItemOnTypeDict(PyTypeObject *tp, PyObject *k, PyObject *v); +#define __Pyx_SetItemOnTypeDict(tp, k, v) __Pyx__SetItemOnTypeDict((PyTypeObject*)tp, k, v) + +/* FixUpExtensionType.proto */ +static CYTHON_INLINE int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type); + +/* PyObjectCallMethod0.proto */ +static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name); + +/* ValidateBasesTuple.proto */ +#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS +static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases); +#endif + +/* PyType_Ready.proto */ +CYTHON_UNUSED static int __Pyx_PyType_Ready(PyTypeObject *t); + +/* SetVTable.proto */ +static int __Pyx_SetVtable(PyTypeObject* typeptr , void* vtable); + +/* GetVTable.proto */ +static void* __Pyx_GetVtable(PyTypeObject *type); + +/* MergeVTables.proto */ +static int __Pyx_MergeVtables(PyTypeObject *type); + +/* DelItemOnTypeDict.proto */ +static int __Pyx__DelItemOnTypeDict(PyTypeObject *tp, PyObject *k); +#define __Pyx_DelItemOnTypeDict(tp, k) __Pyx__DelItemOnTypeDict((PyTypeObject*)tp, k) + +/* SetupReduce.proto */ +static int __Pyx_setup_reduce(PyObject* type_obj); + +/* ListPack.proto */ +static PyObject *__Pyx_PyList_Pack(Py_ssize_t n, ...); + +/* FetchSharedCythonModule.proto */ +static PyObject *__Pyx_FetchSharedCythonABIModule(void); + +/* dict_setdefault.proto */ +static CYTHON_INLINE PyObject *__Pyx_PyDict_SetDefault(PyObject *d, PyObject *key, PyObject *default_value, int is_safe_type); + +/* FetchCommonType.proto */ +static PyTypeObject* __Pyx_FetchCommonTypeFromSpec(PyTypeObject *metaclass, PyObject *module, PyType_Spec *spec, PyObject *bases); + +/* CommonTypesMetaclass.proto */ +static int __pyx_CommonTypesMetaclass_init(PyObject *module); +#define __Pyx_CommonTypesMetaclass_USED + +/* PyMethodNew.proto */ +static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, PyObject *typ); + +/* PyVectorcallFastCallDict.proto */ +#if CYTHON_METH_FASTCALL && (CYTHON_VECTORCALL || CYTHON_BACKPORT_VECTORCALL) +static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw); +#endif + +/* CythonFunctionShared.proto */ +#define __Pyx_CyFunction_USED +#define __Pyx_CYFUNCTION_STATICMETHOD 0x01 +#define __Pyx_CYFUNCTION_CLASSMETHOD 0x02 +#define __Pyx_CYFUNCTION_CCLASS 0x04 +#define __Pyx_CYFUNCTION_COROUTINE 0x08 +#define __Pyx_CyFunction_GetClosure(f)\ + (((__pyx_CyFunctionObject *) (f))->func_closure) +#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + #define __Pyx_CyFunction_GetClassObj(f)\ + (((__pyx_CyFunctionObject *) (f))->func_classobj) +#else + #define __Pyx_CyFunction_GetClassObj(f)\ + ((PyObject*) ((PyCMethodObject *) (f))->mm_class) +#endif +#define __Pyx_CyFunction_SetClassObj(f, classobj)\ + __Pyx__CyFunction_SetClassObj((__pyx_CyFunctionObject *) (f), (classobj)) +#define __Pyx_CyFunction_Defaults(type, f)\ + ((type *)(((__pyx_CyFunctionObject *) (f))->defaults)) +#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\ + ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g) +typedef struct { +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject_HEAD + PyObject *func; +#elif PY_VERSION_HEX < 0x030900B1 + PyCFunctionObject func; +#else + PyCMethodObject func; +#endif +#if CYTHON_BACKPORT_VECTORCALL ||\ + (CYTHON_COMPILING_IN_LIMITED_API && CYTHON_METH_FASTCALL) + __pyx_vectorcallfunc func_vectorcall; +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject *func_weakreflist; +#endif + PyObject *func_dict; + PyObject *func_name; + PyObject *func_qualname; + PyObject *func_doc; + PyObject *func_globals; + PyObject *func_code; + PyObject *func_closure; +#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + PyObject *func_classobj; +#endif + PyObject *defaults; + int flags; + PyObject *defaults_tuple; + PyObject *defaults_kwdict; + PyObject *(*defaults_getter)(PyObject *); + PyObject *func_annotations; + PyObject *func_is_coroutine; +} __pyx_CyFunctionObject; +#undef __Pyx_CyOrPyCFunction_Check +#define __Pyx_CyFunction_Check(obj) __Pyx_TypeCheck(obj, __pyx_mstate_global->__pyx_CyFunctionType) +#define __Pyx_CyOrPyCFunction_Check(obj) __Pyx_TypeCheck2(obj, __pyx_mstate_global->__pyx_CyFunctionType, &PyCFunction_Type) +#define __Pyx_CyFunction_CheckExact(obj) __Pyx_IS_TYPE(obj, __pyx_mstate_global->__pyx_CyFunctionType) +static CYTHON_INLINE int __Pyx__IsSameCyOrCFunction(PyObject *func, void (*cfunc)(void)); +#undef __Pyx_IsSameCFunction +#define __Pyx_IsSameCFunction(func, cfunc) __Pyx__IsSameCyOrCFunction(func, cfunc) +static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject* op, PyMethodDef *ml, + int flags, PyObject* qualname, + PyObject *closure, + PyObject *module, PyObject *globals, + PyObject* code); +static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj); +static CYTHON_INLINE PyObject *__Pyx_CyFunction_InitDefaults(PyObject *func, + PyTypeObject *defaults_type); +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m, + PyObject *tuple); +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m, + PyObject *dict); +static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m, + PyObject *dict); +static int __pyx_CyFunction_init(PyObject *module); +#if CYTHON_METH_FASTCALL +static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames); +#if CYTHON_BACKPORT_VECTORCALL || CYTHON_COMPILING_IN_LIMITED_API +#define __Pyx_CyFunction_func_vectorcall(f) (((__pyx_CyFunctionObject*)f)->func_vectorcall) +#else +#define __Pyx_CyFunction_func_vectorcall(f) (((PyCFunctionObject*)f)->vectorcall) +#endif +#endif + +/* CythonFunction.proto */ +static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, + int flags, PyObject* qualname, + PyObject *closure, + PyObject *module, PyObject *globals, + PyObject* code); + +/* CLineInTraceback.proto */ +#if CYTHON_CLINE_IN_TRACEBACK && CYTHON_CLINE_IN_TRACEBACK_RUNTIME +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); +#else +#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) +#endif + +/* CodeObjectCache.proto */ +#if CYTHON_COMPILING_IN_LIMITED_API +typedef PyObject __Pyx_CachedCodeObjectType; +#else +typedef PyCodeObject __Pyx_CachedCodeObjectType; +#endif +typedef struct { + __Pyx_CachedCodeObjectType* code_object; + int code_line; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; + #if CYTHON_COMPILING_IN_CPYTHON_FREETHREADING + __pyx_atomic_int_type accessor_count; + #endif +}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static __Pyx_CachedCodeObjectType *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, __Pyx_CachedCodeObjectType* code_object); + +/* AddTraceback.proto */ +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +/* BufferStructDeclare.proto */ +typedef struct { + Py_ssize_t shape, strides, suboffsets; +} __Pyx_Buf_DimInfo; +typedef struct { + size_t refcount; + Py_buffer pybuffer; +} __Pyx_Buffer; +typedef struct { + __Pyx_Buffer *rcbuffer; + char *data; + __Pyx_Buf_DimInfo diminfo[8]; +} __Pyx_LocalBuf_ND; + +/* MemviewSliceIsContig.proto */ +static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim); + +/* OverlappingSlices.proto */ +static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize); + +/* IsLittleEndian.proto */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void); + +/* BufferFormatCheck.proto */ +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + const __Pyx_TypeInfo* type); + +/* TypeInfoCompare.proto */ +static int __pyx_typeinfo_cmp(const __Pyx_TypeInfo *a, const __Pyx_TypeInfo *b); + +/* MemviewSliceValidateAndInit.proto */ +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + const __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_long__const__(PyObject *, int writable_flag); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_dc_long__const__(PyObject *, int writable_flag); + +/* ObjectToMemviewSlice.proto */ +static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_double__const__(PyObject *, int writable_flag); + +/* MemviewDtypeToObject.proto */ +static CYTHON_INLINE PyObject *__pyx_memview_get_long__const__(const char *itemp); + +/* MemviewSliceCopyTemplate.proto */ +static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object); + +/* MemviewSliceInit.proto */ +#include +#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d +#define __Pyx_MEMVIEW_DIRECT 1 +#define __Pyx_MEMVIEW_PTR 2 +#define __Pyx_MEMVIEW_FULL 4 +#define __Pyx_MEMVIEW_CONTIG 8 +#define __Pyx_MEMVIEW_STRIDED 16 +#define __Pyx_MEMVIEW_FOLLOW 32 +#define __Pyx_IS_C_CONTIG 1 +#define __Pyx_IS_F_CONTIG 2 +static int __Pyx_init_memviewslice( + struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference); +static CYTHON_INLINE int __pyx_add_acquisition_count_locked( + __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock); +static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( + __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock); +#define __pyx_get_slice_count_pointer(memview) (&memview->acquisition_count) +#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__) +#define __PYX_XCLEAR_MEMVIEW(slice, have_gil) __Pyx_XCLEAR_MEMVIEW(slice, have_gil, __LINE__) +static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int); +static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *, int, int); + +/* CIntFromPy.proto */ +static CYTHON_INLINE long __Pyx_PyLong_As_long(PyObject *); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyLong_From_long(long value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE int __Pyx_PyLong_As_int(PyObject *); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyLong_From_int(int value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE char __Pyx_PyLong_As_char(PyObject *); + +/* FormatTypeName.proto */ +#if CYTHON_COMPILING_IN_LIMITED_API +typedef PyObject *__Pyx_TypeName; +#define __Pyx_FMT_TYPENAME "%U" +#define __Pyx_DECREF_TypeName(obj) Py_XDECREF(obj) +#if __PYX_LIMITED_VERSION_HEX >= 0x030d0000 +#define __Pyx_PyType_GetFullyQualifiedName PyType_GetFullyQualifiedName +#else +static __Pyx_TypeName __Pyx_PyType_GetFullyQualifiedName(PyTypeObject* tp); +#endif +#else // !LIMITED_API +typedef const char *__Pyx_TypeName; +#define __Pyx_FMT_TYPENAME "%.200s" +#define __Pyx_PyType_GetFullyQualifiedName(tp) ((tp)->tp_name) +#define __Pyx_DECREF_TypeName(obj) +#endif + +/* GetRuntimeVersion.proto */ +static unsigned long __Pyx_get_runtime_version(void); + +/* CheckBinaryVersion.proto */ +static int __Pyx_check_binary_version(unsigned long ct_version, unsigned long rt_version, int allow_newer); + +/* MultiPhaseInitModuleState.proto */ +#if CYTHON_PEP489_MULTI_PHASE_INIT && CYTHON_USE_MODULE_STATE +static PyObject *__Pyx_State_FindModule(void*); +static int __Pyx_State_AddModule(PyObject* module, void*); +static int __Pyx_State_RemoveModule(void*); +#elif CYTHON_USE_MODULE_STATE +#define __Pyx_State_FindModule PyState_FindModule +#define __Pyx_State_AddModule PyState_AddModule +#define __Pyx_State_RemoveModule PyState_RemoveModule +#endif + +/* #### Code section: module_declarations ### */ +/* CythonABIVersion.proto */ +#if CYTHON_COMPILING_IN_LIMITED_API + #if CYTHON_METH_FASTCALL + #define __PYX_FASTCALL_ABI_SUFFIX "_fastcall" + #else + #define __PYX_FASTCALL_ABI_SUFFIX + #endif + #define __PYX_LIMITED_ABI_SUFFIX "limited" __PYX_FASTCALL_ABI_SUFFIX __PYX_AM_SEND_ABI_SUFFIX +#else + #define __PYX_LIMITED_ABI_SUFFIX +#endif +#if __PYX_HAS_PY_AM_SEND == 1 + #define __PYX_AM_SEND_ABI_SUFFIX +#elif __PYX_HAS_PY_AM_SEND == 2 + #define __PYX_AM_SEND_ABI_SUFFIX "amsendbackport" +#else + #define __PYX_AM_SEND_ABI_SUFFIX "noamsend" +#endif +#ifndef __PYX_MONITORING_ABI_SUFFIX + #define __PYX_MONITORING_ABI_SUFFIX +#endif +#if CYTHON_USE_TP_FINALIZE + #define __PYX_TP_FINALIZE_ABI_SUFFIX +#else + #define __PYX_TP_FINALIZE_ABI_SUFFIX "nofinalize" +#endif +#if CYTHON_USE_FREELISTS || !defined(__Pyx_AsyncGen_USED) + #define __PYX_FREELISTS_ABI_SUFFIX +#else + #define __PYX_FREELISTS_ABI_SUFFIX "nofreelists" +#endif +#define CYTHON_ABI __PYX_ABI_VERSION __PYX_LIMITED_ABI_SUFFIX __PYX_MONITORING_ABI_SUFFIX __PYX_TP_FINALIZE_ABI_SUFFIX __PYX_FREELISTS_ABI_SUFFIX __PYX_AM_SEND_ABI_SUFFIX +#define __PYX_ABI_MODULE_NAME "_cython_" CYTHON_ABI +#define __PYX_TYPE_MODULE_PREFIX __PYX_ABI_MODULE_NAME "." + +static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ +static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ +static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ +static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryview__get_base(struct __pyx_memoryview_obj *__pyx_v_self); /* proto*/ +static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ +static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ +static PyObject *__pyx_memoryviewslice__get_base(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto*/ + +/* Module declarations from "libc.string" */ + +/* Module declarations from "libc.stdlib" */ + +/* Module declarations from "matris.graph.cygraph" */ +static PyObject *__pyx_collections_abc_Sequence = 0; +static PyObject *generic = 0; +static PyObject *strided = 0; +static PyObject *indirect = 0; +static PyObject *contiguous = 0; +static PyObject *indirect_contiguous = 0; +static int __pyx_memoryview_thread_locks_used; +static PyThread_type_lock __pyx_memoryview_thread_locks[8]; +static int __pyx_array_allocate_buffer(struct __pyx_array_obj *); /*proto*/ +static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char const *, char *); /*proto*/ +static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo const *); /*proto*/ +static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ +static PyObject *_unellipsify(PyObject *, int); /*proto*/ +static int assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ +static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ +static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ +static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ +static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ +static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ +static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ +static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ +static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ +static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ +static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ +static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ +static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ +static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ +static int __pyx_memoryview_err_dim(PyObject *, PyObject *, int); /*proto*/ +static int __pyx_memoryview_err(PyObject *, PyObject *); /*proto*/ +static int __pyx_memoryview_err_no_memory(void); /*proto*/ +static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ +static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ +static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ +static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ +static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ +/* #### Code section: typeinfo ### */ +static const __Pyx_TypeInfo __Pyx_TypeInfo_long__const__ = { "const long", NULL, sizeof(long const ), { 0 }, 0, __PYX_IS_UNSIGNED(long const ) ? 'U' : 'I', __PYX_IS_UNSIGNED(long const ), 0 }; +static const __Pyx_TypeInfo __Pyx_TypeInfo_double__const__ = { "const double", NULL, sizeof(double const ), { 0 }, 0, 'R', 0, 0 }; +/* #### Code section: before_global_var ### */ +#define __Pyx_MODULE_NAME "matris.graph.cygraph" +extern int __pyx_module_is_main_matris__graph__cygraph; +int __pyx_module_is_main_matris__graph__cygraph = 0; + +/* Implementation of "matris.graph.cygraph" */ +/* #### Code section: global_var ### */ +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin___import__; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_MemoryError; +static PyObject *__pyx_builtin_enumerate; +static PyObject *__pyx_builtin_TypeError; +static PyObject *__pyx_builtin_AssertionError; +static PyObject *__pyx_builtin_Ellipsis; +static PyObject *__pyx_builtin_id; +static PyObject *__pyx_builtin_IndexError; +/* #### Code section: string_decls ### */ +static const char __pyx_k_[] = ": "; +static const char __pyx_k_O[] = "O"; +static const char __pyx_k_c[] = "c"; +static const char __pyx_k_j[] = "j"; +static const char __pyx_k_k[] = "k"; +static const char __pyx_k_x[] = "x"; +static const char __pyx_k__2[] = "."; +static const char __pyx_k__3[] = ">"; +static const char __pyx_k__4[] = "'"; +static const char __pyx_k__5[] = ")"; +static const char __pyx_k__6[] = ""; +static const char __pyx_k__7[] = "?"; +static const char __pyx_k_gc[] = "gc"; +static const char __pyx_k_id[] = "id"; +static const char __pyx_k_np[] = "np"; +static const char __pyx_k_UDE[] = "UDE"; +static const char __pyx_k_abc[] = "abc"; +static const char __pyx_k_and[] = " and "; +static const char __pyx_k_got[] = " (got "; +static const char __pyx_k_idx[] = "idx"; +static const char __pyx_k_n_e[] = "n_e"; +static const char __pyx_k_new[] = "__new__"; +static const char __pyx_k_obj[] = "obj"; +static const char __pyx_k_pop[] = "pop"; +static const char __pyx_k_Node[] = "Node"; +static const char __pyx_k_base[] = "base"; +static const char __pyx_k_dict[] = "__dict__"; +static const char __pyx_k_func[] = "__func__"; +static const char __pyx_k_info[] = "info"; +static const char __pyx_k_main[] = "__main__"; +static const char __pyx_k_mode[] = "mode"; +static const char __pyx_k_name[] = "name"; +static const char __pyx_k_ndim[] = "ndim"; +static const char __pyx_k_pack[] = "pack"; +static const char __pyx_k_size[] = "size"; +static const char __pyx_k_spec[] = "__spec__"; +static const char __pyx_k_step[] = "step"; +static const char __pyx_k_stop[] = "stop"; +static const char __pyx_k_test[] = "__test__"; +static const char __pyx_k_ASCII[] = "ASCII"; +static const char __pyx_k_at_0x[] = " at 0x"; +static const char __pyx_k_class[] = "__class__"; +static const char __pyx_k_count[] = "count"; +static const char __pyx_k_error[] = "error"; +static const char __pyx_k_flags[] = "flags"; +static const char __pyx_k_image[] = "image"; +static const char __pyx_k_index[] = "index"; +static const char __pyx_k_nodes[] = "nodes"; +static const char __pyx_k_numpy[] = "numpy"; +static const char __pyx_k_py_DE[] = "py_DE"; +static const char __pyx_k_range[] = "range"; +static const char __pyx_k_shape[] = "shape"; +static const char __pyx_k_start[] = "start"; +static const char __pyx_k_append[] = "append"; +static const char __pyx_k_enable[] = "enable"; +static const char __pyx_k_encode[] = "encode"; +static const char __pyx_k_format[] = "format"; +static const char __pyx_k_import[] = "__import__"; +static const char __pyx_k_module[] = "__module__"; +static const char __pyx_k_name_2[] = "__name__"; +static const char __pyx_k_object[] = " object>"; +static const char __pyx_k_pickle[] = "pickle"; +static const char __pyx_k_reduce[] = "__reduce__"; +static const char __pyx_k_struct[] = "struct"; +static const char __pyx_k_unpack[] = "unpack"; +static const char __pyx_k_update[] = "update"; +static const char __pyx_k_asarray[] = "asarray"; +static const char __pyx_k_disable[] = "disable"; +static const char __pyx_k_fortran[] = "fortran"; +static const char __pyx_k_memview[] = "memview"; +static const char __pyx_k_this_DE[] = "this_DE"; +static const char __pyx_k_Ellipsis[] = "Ellipsis"; +static const char __pyx_k_Sequence[] = "Sequence"; +static const char __pyx_k_add_note[] = "add_note"; +static const char __pyx_k_chg_Node[] = "chg_Node"; +static const char __pyx_k_distance[] = "distance"; +static const char __pyx_k_getstate[] = "__getstate__"; +static const char __pyx_k_image_np[] = "image_np"; +static const char __pyx_k_itemsize[] = "itemsize"; +static const char __pyx_k_py_nodes[] = "py_nodes"; +static const char __pyx_k_pyx_type[] = "__pyx_type"; +static const char __pyx_k_qualname[] = "__qualname__"; +static const char __pyx_k_register[] = "register"; +static const char __pyx_k_returned[] = "returned"; +static const char __pyx_k_set_name[] = "__set_name__"; +static const char __pyx_k_setstate[] = "__setstate__"; +static const char __pyx_k_this_set[] = "this_set"; +static const char __pyx_k_TypeError[] = "TypeError"; +static const char __pyx_k_enumerate[] = "enumerate"; +static const char __pyx_k_isenabled[] = "isenabled"; +static const char __pyx_k_neighbors[] = "neighbors"; +static const char __pyx_k_num_atoms[] = "num_atoms"; +static const char __pyx_k_pyx_state[] = "__pyx_state"; +static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; +static const char __pyx_k_this_node[] = "this_node"; +static const char __pyx_k_IndexError[] = "IndexError"; +static const char __pyx_k_ValueError[] = "ValueError"; +static const char __pyx_k_edge_index[] = "edge_index"; +static const char __pyx_k_make_graph[] = "make_graph"; +static const char __pyx_k_node_index[] = "node_index"; +static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; +static const char __pyx_k_this_entry[] = "this_entry"; +static const char __pyx_k_MemoryError[] = "MemoryError"; +static const char __pyx_k_PickleError[] = "PickleError"; +static const char __pyx_k_radiusgraph[] = "radiusgraph"; +static const char __pyx_k_replacement[] = "replacement"; +static const char __pyx_k_DirectedEdge[] = "DirectedEdge"; +static const char __pyx_k_center_index[] = "center_index"; +static const char __pyx_k_initializing[] = "_initializing"; +static const char __pyx_k_is_coroutine[] = "_is_coroutine"; +static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; +static const char __pyx_k_this_py_node[] = "this_py_node"; +static const char __pyx_k_MemoryView_of[] = " 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 3)) + #define CYTHON_SMALL_CODE __attribute__((cold)) +#else + #define CYTHON_SMALL_CODE +#endif +#endif + +typedef struct { + PyObject *__pyx_d; + PyObject *__pyx_b; + PyObject *__pyx_cython_runtime; + PyObject *__pyx_empty_tuple; + PyObject *__pyx_empty_bytes; + PyObject *__pyx_empty_unicode; + #ifdef __Pyx_CyFunction_USED + PyTypeObject *__pyx_CyFunctionType; + #endif + #ifdef __Pyx_FusedFunction_USED + PyTypeObject *__pyx_FusedFunctionType; + #endif + #ifdef __Pyx_Generator_USED + PyTypeObject *__pyx_GeneratorType; + #endif + #ifdef __Pyx_IterableCoroutine_USED + PyTypeObject *__pyx_IterableCoroutineType; + #endif + #ifdef __Pyx_Coroutine_USED + PyTypeObject *__pyx_CoroutineAwaitType; + #endif + #ifdef __Pyx_Coroutine_USED + PyTypeObject *__pyx_CoroutineType; + #endif + PyObject *__pyx_type___pyx_array; + PyObject *__pyx_type___pyx_MemviewEnum; + PyObject *__pyx_type___pyx_memoryview; + PyObject *__pyx_type___pyx_memoryviewslice; + PyTypeObject *__pyx_array_type; + PyTypeObject *__pyx_MemviewEnum_type; + PyTypeObject *__pyx_memoryview_type; + PyTypeObject *__pyx_memoryviewslice_type; + __Pyx_CachedCFunction __pyx_umethod_PyDict_Type_pop; + PyObject *__pyx_slice[1]; + PyObject *__pyx_tuple[2]; + PyObject *__pyx_codeobj_tab[1]; + PyObject *__pyx_string_tab[169]; + PyObject *__pyx_int_0; + PyObject *__pyx_int_1; + PyObject *__pyx_int_112105877; + PyObject *__pyx_int_136983863; + PyObject *__pyx_int_184977713; + PyObject *__pyx_int_neg_1; +/* #### Code section: module_state_contents ### */ +/* CommonTypesMetaclass.module_state_decls */ +PyTypeObject *__pyx_CommonTypesMetaclassType; + +/* CachedMethodType.module_state_decls */ +#if CYTHON_COMPILING_IN_LIMITED_API +PyObject *__Pyx_CachedMethodType; +#endif + +/* CodeObjectCache.module_state_decls */ +struct __Pyx_CodeObjectCache __pyx_code_cache; + +/* #### Code section: module_state_end ### */ +} __pyx_mstatetype; + +#if CYTHON_USE_MODULE_STATE +#ifdef __cplusplus +namespace { +extern struct PyModuleDef __pyx_moduledef; +} /* anonymous namespace */ +#else +static struct PyModuleDef __pyx_moduledef; +#endif + +#define __pyx_mstate_global (__Pyx_PyModule_GetState(__Pyx_State_FindModule(&__pyx_moduledef))) + +#define __pyx_m (__Pyx_State_FindModule(&__pyx_moduledef)) +#else +static __pyx_mstatetype __pyx_mstate_global_static = +#ifdef __cplusplus + {}; +#else + {0}; +#endif +static __pyx_mstatetype * const __pyx_mstate_global = &__pyx_mstate_global_static; +#endif +/* #### Code section: constant_name_defines ### */ +#define __pyx_kp_u_ __pyx_string_tab[0] +#define __pyx_n_u_ASCII __pyx_string_tab[1] +#define __pyx_kp_u_All_dimensions_preceding_dimensi __pyx_string_tab[2] +#define __pyx_n_u_AssertionError __pyx_string_tab[3] +#define __pyx_kp_u_Buffer_view_does_not_expose_stri __pyx_string_tab[4] +#define __pyx_kp_u_Can_only_create_a_buffer_that_is __pyx_string_tab[5] +#define __pyx_kp_u_Cannot_assign_to_read_only_memor __pyx_string_tab[6] +#define __pyx_kp_u_Cannot_create_writable_memory_vi __pyx_string_tab[7] +#define __pyx_kp_u_Cannot_index_with_type __pyx_string_tab[8] +#define __pyx_kp_u_Cannot_transpose_memoryview_with __pyx_string_tab[9] +#define __pyx_kp_u_Dimension_d_is_not_direct __pyx_string_tab[10] +#define __pyx_n_u_DirectedEdge __pyx_string_tab[11] +#define __pyx_n_u_Ellipsis __pyx_string_tab[12] +#define __pyx_kp_u_Empty_shape_tuple_for_cython_arr __pyx_string_tab[13] +#define __pyx_kp_u_Incompatible_checksums_0x_x_vs_0 __pyx_string_tab[14] +#define __pyx_n_u_IndexError __pyx_string_tab[15] +#define __pyx_kp_u_Index_out_of_bounds_axis_d __pyx_string_tab[16] +#define __pyx_kp_u_Indirect_dimensions_not_supporte __pyx_string_tab[17] +#define __pyx_kp_u_Invalid_mode_expected_c_or_fortr __pyx_string_tab[18] +#define __pyx_kp_u_Invalid_shape_in_axis __pyx_string_tab[19] +#define __pyx_n_u_MemoryError __pyx_string_tab[20] +#define __pyx_kp_u_MemoryView_of __pyx_string_tab[21] +#define __pyx_n_u_Node __pyx_string_tab[22] +#define __pyx_kp_u_Note_that_Cython_is_deliberately __pyx_string_tab[23] +#define __pyx_n_b_O __pyx_string_tab[24] +#define __pyx_kp_u_Out_of_bounds_on_buffer_access_a __pyx_string_tab[25] +#define __pyx_n_u_PickleError __pyx_string_tab[26] +#define __pyx_n_u_Sequence __pyx_string_tab[27] +#define __pyx_kp_u_Step_may_not_be_zero_axis_d __pyx_string_tab[28] +#define __pyx_n_u_TypeError __pyx_string_tab[29] +#define __pyx_n_u_UDE __pyx_string_tab[30] +#define __pyx_kp_u_Unable_to_convert_item_to_object __pyx_string_tab[31] +#define __pyx_n_u_UndirectedEdge __pyx_string_tab[32] +#define __pyx_n_u_ValueError __pyx_string_tab[33] +#define __pyx_n_u_View_MemoryView __pyx_string_tab[34] +#define __pyx_kp_u__2 __pyx_string_tab[35] +#define __pyx_kp_u__3 __pyx_string_tab[36] +#define __pyx_kp_u__4 __pyx_string_tab[37] +#define __pyx_kp_u__5 __pyx_string_tab[38] +#define __pyx_n_u__6 __pyx_string_tab[39] +#define __pyx_kp_u__7 __pyx_string_tab[40] +#define __pyx_n_u_abc __pyx_string_tab[41] +#define __pyx_kp_u_add_note __pyx_string_tab[42] +#define __pyx_n_u_allocate_buffer __pyx_string_tab[43] +#define __pyx_kp_u_and __pyx_string_tab[44] +#define __pyx_n_u_append __pyx_string_tab[45] +#define __pyx_n_u_asarray __pyx_string_tab[46] +#define __pyx_n_u_asyncio_coroutines __pyx_string_tab[47] +#define __pyx_kp_u_at_0x __pyx_string_tab[48] +#define __pyx_n_u_base __pyx_string_tab[49] +#define __pyx_n_u_c __pyx_string_tab[50] +#define __pyx_n_u_center_index __pyx_string_tab[51] +#define __pyx_n_u_chg_DirectedEdge __pyx_string_tab[52] +#define __pyx_n_u_chg_Node __pyx_string_tab[53] +#define __pyx_n_u_chg_UndirectedEdge __pyx_string_tab[54] +#define __pyx_n_u_class __pyx_string_tab[55] +#define __pyx_n_u_class_getitem __pyx_string_tab[56] +#define __pyx_n_u_cline_in_traceback __pyx_string_tab[57] +#define __pyx_kp_u_collections_abc __pyx_string_tab[58] +#define __pyx_kp_u_contiguous_and_direct __pyx_string_tab[59] +#define __pyx_kp_u_contiguous_and_indirect __pyx_string_tab[60] +#define __pyx_n_u_count __pyx_string_tab[61] +#define __pyx_n_u_dict __pyx_string_tab[62] +#define __pyx_n_u_directed_edge_index __pyx_string_tab[63] +#define __pyx_n_u_directed_edges __pyx_string_tab[64] +#define __pyx_kp_u_disable __pyx_string_tab[65] +#define __pyx_n_u_distance __pyx_string_tab[66] +#define __pyx_n_u_dtype_is_object __pyx_string_tab[67] +#define __pyx_n_u_edge_index __pyx_string_tab[68] +#define __pyx_kp_u_enable __pyx_string_tab[69] +#define __pyx_n_u_encode __pyx_string_tab[70] +#define __pyx_n_u_enumerate __pyx_string_tab[71] +#define __pyx_n_u_error __pyx_string_tab[72] +#define __pyx_n_u_flags __pyx_string_tab[73] +#define __pyx_n_u_format __pyx_string_tab[74] +#define __pyx_n_u_fortran __pyx_string_tab[75] +#define __pyx_n_u_func __pyx_string_tab[76] +#define __pyx_kp_u_gc __pyx_string_tab[77] +#define __pyx_n_u_getstate __pyx_string_tab[78] +#define __pyx_kp_u_got __pyx_string_tab[79] +#define __pyx_kp_u_got_differing_extents_in_dimensi __pyx_string_tab[80] +#define __pyx_n_u_id __pyx_string_tab[81] +#define __pyx_n_u_idx __pyx_string_tab[82] +#define __pyx_n_u_image __pyx_string_tab[83] +#define __pyx_n_u_image_np __pyx_string_tab[84] +#define __pyx_n_u_import __pyx_string_tab[85] +#define __pyx_n_u_index __pyx_string_tab[86] +#define __pyx_n_u_info __pyx_string_tab[87] +#define __pyx_n_u_initializing __pyx_string_tab[88] +#define __pyx_n_u_is_coroutine __pyx_string_tab[89] +#define __pyx_kp_u_isenabled __pyx_string_tab[90] +#define __pyx_n_u_itemsize __pyx_string_tab[91] +#define __pyx_kp_u_itemsize_0_for_cython_array __pyx_string_tab[92] +#define __pyx_n_u_j __pyx_string_tab[93] +#define __pyx_n_u_k __pyx_string_tab[94] +#define __pyx_n_u_main __pyx_string_tab[95] +#define __pyx_n_u_make_graph __pyx_string_tab[96] +#define __pyx_n_u_matris_graph_cygraph __pyx_string_tab[97] +#define __pyx_kp_u_matris_graph_cygraph_pyx __pyx_string_tab[98] +#define __pyx_n_u_memview __pyx_string_tab[99] +#define __pyx_n_u_mode __pyx_string_tab[100] +#define __pyx_n_u_module __pyx_string_tab[101] +#define __pyx_n_u_n_e __pyx_string_tab[102] +#define __pyx_n_u_name __pyx_string_tab[103] +#define __pyx_n_u_name_2 __pyx_string_tab[104] +#define __pyx_n_u_ndim __pyx_string_tab[105] +#define __pyx_n_u_neighbor_index __pyx_string_tab[106] +#define __pyx_n_u_neighbors __pyx_string_tab[107] +#define __pyx_n_u_new __pyx_string_tab[108] +#define __pyx_kp_u_no_default___reduce___due_to_non __pyx_string_tab[109] +#define __pyx_n_u_node_index __pyx_string_tab[110] +#define __pyx_n_u_node_neighbors __pyx_string_tab[111] +#define __pyx_n_u_nodes __pyx_string_tab[112] +#define __pyx_n_u_np __pyx_string_tab[113] +#define __pyx_n_u_num_atoms __pyx_string_tab[114] +#define __pyx_n_u_numpy __pyx_string_tab[115] +#define __pyx_n_u_obj __pyx_string_tab[116] +#define __pyx_kp_u_object __pyx_string_tab[117] +#define __pyx_n_u_pack __pyx_string_tab[118] +#define __pyx_n_u_pickle __pyx_string_tab[119] +#define __pyx_n_u_pop __pyx_string_tab[120] +#define __pyx_n_u_py_DE __pyx_string_tab[121] +#define __pyx_n_u_py_directed_edges_list __pyx_string_tab[122] +#define __pyx_n_u_py_nodes __pyx_string_tab[123] +#define __pyx_n_u_py_undirected_edge __pyx_string_tab[124] +#define __pyx_n_u_py_undirected_edges __pyx_string_tab[125] +#define __pyx_n_u_py_undirected_edges_list __pyx_string_tab[126] +#define __pyx_n_u_pyx_checksum __pyx_string_tab[127] +#define __pyx_n_u_pyx_state __pyx_string_tab[128] +#define __pyx_n_u_pyx_type __pyx_string_tab[129] +#define __pyx_n_u_pyx_unpickle_Enum __pyx_string_tab[130] +#define __pyx_n_u_pyx_vtable __pyx_string_tab[131] +#define __pyx_n_u_qualname __pyx_string_tab[132] +#define __pyx_n_u_radiusgraph __pyx_string_tab[133] +#define __pyx_n_u_range __pyx_string_tab[134] +#define __pyx_n_u_reduce __pyx_string_tab[135] +#define __pyx_n_u_reduce_cython __pyx_string_tab[136] +#define __pyx_n_u_reduce_ex __pyx_string_tab[137] +#define __pyx_n_u_register __pyx_string_tab[138] +#define __pyx_n_u_replacement __pyx_string_tab[139] +#define __pyx_n_u_returned __pyx_string_tab[140] +#define __pyx_n_u_set_name __pyx_string_tab[141] +#define __pyx_n_u_setstate __pyx_string_tab[142] +#define __pyx_n_u_setstate_cython __pyx_string_tab[143] +#define __pyx_n_u_shape __pyx_string_tab[144] +#define __pyx_n_u_size __pyx_string_tab[145] +#define __pyx_n_u_spec __pyx_string_tab[146] +#define __pyx_n_u_start __pyx_string_tab[147] +#define __pyx_n_u_step __pyx_string_tab[148] +#define __pyx_n_u_stop __pyx_string_tab[149] +#define __pyx_kp_u_strided_and_direct __pyx_string_tab[150] +#define __pyx_kp_u_strided_and_direct_or_indirect __pyx_string_tab[151] +#define __pyx_kp_u_strided_and_indirect __pyx_string_tab[152] +#define __pyx_n_u_struct __pyx_string_tab[153] +#define __pyx_n_u_test __pyx_string_tab[154] +#define __pyx_n_u_this_DE __pyx_string_tab[155] +#define __pyx_n_u_this_entry __pyx_string_tab[156] +#define __pyx_n_u_this_neighbor_index __pyx_string_tab[157] +#define __pyx_n_u_this_neighbors __pyx_string_tab[158] +#define __pyx_n_u_this_node __pyx_string_tab[159] +#define __pyx_n_u_this_py_node __pyx_string_tab[160] +#define __pyx_n_u_this_set __pyx_string_tab[161] +#define __pyx_kp_u_unable_to_allocate_array_data __pyx_string_tab[162] +#define __pyx_kp_u_unable_to_allocate_shape_and_str __pyx_string_tab[163] +#define __pyx_n_u_undirected_edge __pyx_string_tab[164] +#define __pyx_n_u_undirected_edge_index __pyx_string_tab[165] +#define __pyx_n_u_unpack __pyx_string_tab[166] +#define __pyx_n_u_update __pyx_string_tab[167] +#define __pyx_n_u_x __pyx_string_tab[168] +/* #### Code section: module_state_clear ### */ +#if CYTHON_USE_MODULE_STATE +static CYTHON_SMALL_CODE int __pyx_m_clear(PyObject *m) { + __pyx_mstatetype *clear_module_state = __Pyx_PyModule_GetState(m); + if (!clear_module_state) return 0; + Py_CLEAR(clear_module_state->__pyx_d); + Py_CLEAR(clear_module_state->__pyx_b); + Py_CLEAR(clear_module_state->__pyx_cython_runtime); + Py_CLEAR(clear_module_state->__pyx_empty_tuple); + Py_CLEAR(clear_module_state->__pyx_empty_bytes); + Py_CLEAR(clear_module_state->__pyx_empty_unicode); + #ifdef __Pyx_CyFunction_USED + Py_CLEAR(clear_module_state->__pyx_CyFunctionType); + #endif + #ifdef __Pyx_FusedFunction_USED + Py_CLEAR(clear_module_state->__pyx_FusedFunctionType); + #endif + #if CYTHON_PEP489_MULTI_PHASE_INIT + __Pyx_State_RemoveModule(NULL); + #endif + Py_CLEAR(clear_module_state->__pyx_array_type); + Py_CLEAR(clear_module_state->__pyx_type___pyx_array); + Py_CLEAR(clear_module_state->__pyx_MemviewEnum_type); + Py_CLEAR(clear_module_state->__pyx_type___pyx_MemviewEnum); + Py_CLEAR(clear_module_state->__pyx_memoryview_type); + Py_CLEAR(clear_module_state->__pyx_type___pyx_memoryview); + Py_CLEAR(clear_module_state->__pyx_memoryviewslice_type); + Py_CLEAR(clear_module_state->__pyx_type___pyx_memoryviewslice); + for (int i=0; i<1; ++i) { Py_CLEAR(clear_module_state->__pyx_slice[i]); } + for (int i=0; i<2; ++i) { Py_CLEAR(clear_module_state->__pyx_tuple[i]); } + for (int i=0; i<1; ++i) { Py_CLEAR(clear_module_state->__pyx_codeobj_tab[i]); } + for (int i=0; i<169; ++i) { Py_CLEAR(clear_module_state->__pyx_string_tab[i]); } + Py_CLEAR(clear_module_state->__pyx_int_0); + Py_CLEAR(clear_module_state->__pyx_int_1); + Py_CLEAR(clear_module_state->__pyx_int_112105877); + Py_CLEAR(clear_module_state->__pyx_int_136983863); + Py_CLEAR(clear_module_state->__pyx_int_184977713); + Py_CLEAR(clear_module_state->__pyx_int_neg_1); + return 0; +} +#endif +/* #### Code section: module_state_traverse ### */ +#if CYTHON_USE_MODULE_STATE +static CYTHON_SMALL_CODE int __pyx_m_traverse(PyObject *m, visitproc visit, void *arg) { + __pyx_mstatetype *traverse_module_state = __Pyx_PyModule_GetState(m); + if (!traverse_module_state) return 0; + Py_VISIT(traverse_module_state->__pyx_d); + Py_VISIT(traverse_module_state->__pyx_b); + Py_VISIT(traverse_module_state->__pyx_cython_runtime); + __Pyx_VISIT_CONST(traverse_module_state->__pyx_empty_tuple); + __Pyx_VISIT_CONST(traverse_module_state->__pyx_empty_bytes); + __Pyx_VISIT_CONST(traverse_module_state->__pyx_empty_unicode); + #ifdef __Pyx_CyFunction_USED + Py_VISIT(traverse_module_state->__pyx_CyFunctionType); + #endif + #ifdef __Pyx_FusedFunction_USED + Py_VISIT(traverse_module_state->__pyx_FusedFunctionType); + #endif + Py_VISIT(traverse_module_state->__pyx_array_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_array); + Py_VISIT(traverse_module_state->__pyx_MemviewEnum_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_MemviewEnum); + Py_VISIT(traverse_module_state->__pyx_memoryview_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_memoryview); + Py_VISIT(traverse_module_state->__pyx_memoryviewslice_type); + Py_VISIT(traverse_module_state->__pyx_type___pyx_memoryviewslice); + for (int i=0; i<1; ++i) { __Pyx_VISIT_CONST(traverse_module_state->__pyx_slice[i]); } + for (int i=0; i<2; ++i) { __Pyx_VISIT_CONST(traverse_module_state->__pyx_tuple[i]); } + for (int i=0; i<1; ++i) { __Pyx_VISIT_CONST(traverse_module_state->__pyx_codeobj_tab[i]); } + for (int i=0; i<169; ++i) { __Pyx_VISIT_CONST(traverse_module_state->__pyx_string_tab[i]); } + __Pyx_VISIT_CONST(traverse_module_state->__pyx_int_0); + __Pyx_VISIT_CONST(traverse_module_state->__pyx_int_1); + __Pyx_VISIT_CONST(traverse_module_state->__pyx_int_112105877); + __Pyx_VISIT_CONST(traverse_module_state->__pyx_int_136983863); + 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__pyx_memoryview_thread_locks_used + * if (__pyx_memoryview_thread_locks_used < 8 and # <<<<<<<<<<<<<< + * + * not __PYX_GET_CYTHON_COMPILING_IN_CPYTHON_FREETHREADING()): +*/ + if (__pyx_t_1) { + + /* "View.MemoryView":361 + * + * not __PYX_GET_CYTHON_COMPILING_IN_CPYTHON_FREETHREADING()): + * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] # <<<<<<<<<<<<<< + * __pyx_memoryview_thread_locks_used += 1 + * if self.lock is NULL: +*/ + __pyx_v_self->lock = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]); + + /* "View.MemoryView":362 + * not __PYX_GET_CYTHON_COMPILING_IN_CPYTHON_FREETHREADING()): + * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] + * __pyx_memoryview_thread_locks_used += 1 # <<<<<<<<<<<<<< + * if self.lock is NULL: + * self.lock = PyThread_allocate_lock() +*/ + __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used + 1); + + /* "View.MemoryView":358 + * if not __PYX_CYTHON_ATOMICS_ENABLED(): + * global __pyx_memoryview_thread_locks_used + * if (__pyx_memoryview_thread_locks_used < 8 and # <<<<<<<<<<<<<< + * + * not __PYX_GET_CYTHON_COMPILING_IN_CPYTHON_FREETHREADING()): +*/ + } + + /* "View.MemoryView":363 + * self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] + * __pyx_memoryview_thread_locks_used += 1 + * if self.lock is NULL: # <<<<<<<<<<<<<< + * self.lock = PyThread_allocate_lock() + * if self.lock is NULL: +*/ + __pyx_t_1 = (__pyx_v_self->lock == NULL); + if (__pyx_t_1) { + + /* "View.MemoryView":364 + * __pyx_memoryview_thread_locks_used += 1 + * if self.lock is NULL: + * self.lock = PyThread_allocate_lock() # <<<<<<<<<<<<<< + * if self.lock is NULL: + * raise MemoryError +*/ + __pyx_v_self->lock = PyThread_allocate_lock(); + + /* "View.MemoryView":365 + * if self.lock is NULL: + * self.lock = PyThread_allocate_lock() + * if self.lock is NULL: # <<<<<<<<<<<<<< + * raise MemoryError + * +*/ + __pyx_t_1 = 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MemoryError + * + * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< + * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') + * else: +*/ + __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0); + if (__pyx_t_1) { + + /* "View.MemoryView":369 + * + * if flags & PyBUF_FORMAT: + * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') # <<<<<<<<<<<<<< + * else: + * self.dtype_is_object = dtype_is_object +*/ + __pyx_t_2 = ((__pyx_v_self->view.format[0]) == 'O'); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L14_bool_binop_done; + } + __pyx_t_2 = ((__pyx_v_self->view.format[1]) == '\x00'); + __pyx_t_1 = __pyx_t_2; + __pyx_L14_bool_binop_done:; + __pyx_v_self->dtype_is_object = __pyx_t_1; + + /* "View.MemoryView":368 + * raise MemoryError + * + * if flags & PyBUF_FORMAT: # <<<<<<<<<<<<<< + * self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0') + * else: +*/ + goto __pyx_L13; + } + + 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+ if (__pyx_t_1) { + + /* "View.MemoryView":815 + * if not is_slice: + * + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if not 0 <= start < shape: +*/ + __pyx_t_1 = (__pyx_v_start < 0); + if (__pyx_t_1) { + + /* "View.MemoryView":816 + * + * if start < 0: + * start += shape # <<<<<<<<<<<<<< + * if not 0 <= start < shape: + * _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim) +*/ + __pyx_v_start = (__pyx_v_start + __pyx_v_shape); + + /* "View.MemoryView":815 + * if not is_slice: + * + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if not 0 <= start < shape: +*/ + } + + /* "View.MemoryView":817 + * if start < 0: + * start += shape + * if not 0 <= start < shape: # <<<<<<<<<<<<<< + * _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim) + * else: +*/ + __pyx_t_1 = (0 <= __pyx_v_start); + if (__pyx_t_1) { + __pyx_t_1 = (__pyx_v_start < __pyx_v_shape); + } + __pyx_t_2 = (!__pyx_t_1); + if (__pyx_t_2) { + + /* "View.MemoryView":818 + * start += shape + * if not 0 <= start < shape: + * _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim) # <<<<<<<<<<<<<< + * else: + * +*/ + __pyx_t_3 = __pyx_memoryview_err_dim(PyExc_IndexError, __pyx_mstate_global->__pyx_kp_u_Index_out_of_bounds_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 818, __pyx_L1_error) + + /* "View.MemoryView":817 + * if start < 0: + * start += shape + * if not 0 <= start < shape: # <<<<<<<<<<<<<< + * _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim) + * else: +*/ + } + + /* "View.MemoryView":813 + * cdef bint negative_step + * + * if not is_slice: # <<<<<<<<<<<<<< + * + * if start < 0: +*/ + goto __pyx_L3; + } + + /* "View.MemoryView":821 + * else: + * + * if have_step: # <<<<<<<<<<<<<< + * negative_step = step < 0 + * if step == 0: +*/ + /*else*/ { + __pyx_t_2 = (__pyx_v_have_step != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":822 + * + * if have_step: + * negative_step = step < 0 # <<<<<<<<<<<<<< + * if step == 0: + * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) +*/ + __pyx_v_negative_step = (__pyx_v_step < 0); + + /* "View.MemoryView":823 + * if have_step: + * negative_step = step < 0 + * if step == 0: # <<<<<<<<<<<<<< + * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) + * else: +*/ + __pyx_t_2 = (__pyx_v_step == 0); + if (__pyx_t_2) { + + /* "View.MemoryView":824 + * negative_step = step < 0 + * if step == 0: + * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) # <<<<<<<<<<<<<< + * else: + * negative_step = False +*/ + __pyx_t_3 = __pyx_memoryview_err_dim(PyExc_ValueError, __pyx_mstate_global->__pyx_kp_u_Step_may_not_be_zero_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 824, __pyx_L1_error) + + /* "View.MemoryView":823 + * if have_step: + * negative_step = step < 0 + * if step == 0: # <<<<<<<<<<<<<< + * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) + * else: +*/ + } + + /* "View.MemoryView":821 + * else: + * + * if have_step: # <<<<<<<<<<<<<< + * negative_step = step < 0 + * if step == 0: +*/ + goto __pyx_L6; + } + + /* "View.MemoryView":826 + * _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim) + * else: + * negative_step = False # <<<<<<<<<<<<<< + * step = 1 + * +*/ + /*else*/ { + __pyx_v_negative_step = 0; + + /* "View.MemoryView":827 + * else: + * negative_step = False + * step = 1 # <<<<<<<<<<<<<< + * + * +*/ + __pyx_v_step = 1; + } + __pyx_L6:; + + /* "View.MemoryView":830 + * + * + * if have_start: # <<<<<<<<<<<<<< + * if start < 0: + * start += shape +*/ + __pyx_t_2 = (__pyx_v_have_start != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":831 + * + * if have_start: + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if start < 0: +*/ + __pyx_t_2 = (__pyx_v_start < 0); + if (__pyx_t_2) { + + /* "View.MemoryView":832 + * if have_start: + * if start < 0: + * start += shape # <<<<<<<<<<<<<< + * if start < 0: + * start = 0 +*/ + __pyx_v_start = (__pyx_v_start + __pyx_v_shape); + + /* "View.MemoryView":833 + * if start < 0: + * start += shape + * if start < 0: # <<<<<<<<<<<<<< + * start = 0 + * elif start >= shape: +*/ + __pyx_t_2 = (__pyx_v_start < 0); + if (__pyx_t_2) { + + /* "View.MemoryView":834 + * start += shape + * if start < 0: + * start = 0 # <<<<<<<<<<<<<< + * elif start >= shape: + * if negative_step: +*/ + __pyx_v_start = 0; + + /* "View.MemoryView":833 + * if start < 0: + * start += shape + * if start < 0: # <<<<<<<<<<<<<< + * start = 0 + * elif start >= shape: +*/ + } + + /* "View.MemoryView":831 + * + * if have_start: + * if start < 0: # <<<<<<<<<<<<<< + * start += shape + * if start < 0: +*/ + goto __pyx_L9; + } + + /* "View.MemoryView":835 + * if start < 0: + * start = 0 + * elif start >= shape: # <<<<<<<<<<<<<< + * if negative_step: + * start = shape - 1 +*/ + __pyx_t_2 = (__pyx_v_start >= __pyx_v_shape); + if (__pyx_t_2) { + + /* "View.MemoryView":836 + * start = 0 + * elif start >= shape: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: +*/ + if (__pyx_v_negative_step) { + + /* "View.MemoryView":837 + * elif start >= shape: + * if negative_step: + * start = shape - 1 # <<<<<<<<<<<<<< + * else: + * start = shape +*/ + __pyx_v_start = (__pyx_v_shape - 1); + + /* "View.MemoryView":836 + * start = 0 + * elif start >= shape: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: +*/ + goto __pyx_L11; + } + + /* "View.MemoryView":839 + * start = shape - 1 + * else: + * start = shape # <<<<<<<<<<<<<< + * else: + * if negative_step: +*/ + /*else*/ { + __pyx_v_start = __pyx_v_shape; + } + __pyx_L11:; + + /* "View.MemoryView":835 + * if start < 0: + * start = 0 + * elif start >= shape: # <<<<<<<<<<<<<< + * if negative_step: + * start = shape - 1 +*/ + } + __pyx_L9:; + + /* "View.MemoryView":830 + * + * + * if have_start: # <<<<<<<<<<<<<< + * if start < 0: + * start += shape +*/ + goto __pyx_L8; + } + + /* "View.MemoryView":841 + * start = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: +*/ + /*else*/ { + if (__pyx_v_negative_step) { + + /* "View.MemoryView":842 + * else: + * if negative_step: + * start = shape - 1 # <<<<<<<<<<<<<< + * else: + * start = 0 +*/ + __pyx_v_start = (__pyx_v_shape - 1); + + /* "View.MemoryView":841 + * start = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * start = shape - 1 + * else: +*/ + goto __pyx_L12; + } + + /* "View.MemoryView":844 + * start = shape - 1 + * else: + * start = 0 # <<<<<<<<<<<<<< + * + * if have_stop: +*/ + /*else*/ { + __pyx_v_start = 0; + } + __pyx_L12:; + } + __pyx_L8:; + + /* "View.MemoryView":846 + * start = 0 + * + * if have_stop: # <<<<<<<<<<<<<< + * if stop < 0: + * stop += shape +*/ + __pyx_t_2 = (__pyx_v_have_stop != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":847 + * + * if have_stop: + * if stop < 0: # <<<<<<<<<<<<<< + * stop += shape + * if stop < 0: +*/ + __pyx_t_2 = (__pyx_v_stop < 0); + if (__pyx_t_2) { + + /* "View.MemoryView":848 + * if have_stop: + * if stop < 0: + * stop += shape # <<<<<<<<<<<<<< + * if stop < 0: + * stop = 0 +*/ + __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape); + + /* "View.MemoryView":849 + * if stop < 0: + * stop += shape + * if stop < 0: # <<<<<<<<<<<<<< + * stop = 0 + * elif stop > shape: +*/ + __pyx_t_2 = (__pyx_v_stop < 0); + if (__pyx_t_2) { + + /* "View.MemoryView":850 + * stop += shape + * if stop < 0: + * stop = 0 # <<<<<<<<<<<<<< + * elif stop > shape: + * stop = shape +*/ + __pyx_v_stop = 0; + + /* "View.MemoryView":849 + * if stop < 0: + * stop += shape + * if stop < 0: # <<<<<<<<<<<<<< + * stop = 0 + * elif stop > shape: +*/ + } + + /* "View.MemoryView":847 + * + * if have_stop: + * if stop < 0: # <<<<<<<<<<<<<< + * stop += shape + * if stop < 0: +*/ + goto __pyx_L14; + } + + /* "View.MemoryView":851 + * if stop < 0: + * stop = 0 + * elif stop > shape: # <<<<<<<<<<<<<< + * stop = shape + * else: +*/ + __pyx_t_2 = (__pyx_v_stop > __pyx_v_shape); + if (__pyx_t_2) { + + /* "View.MemoryView":852 + * stop = 0 + * elif stop > shape: + * stop = shape # <<<<<<<<<<<<<< + * else: + * if negative_step: +*/ + __pyx_v_stop = __pyx_v_shape; + + /* "View.MemoryView":851 + * if stop < 0: + * stop = 0 + * elif stop > shape: # <<<<<<<<<<<<<< + * stop = shape + * else: +*/ + } + __pyx_L14:; + + /* "View.MemoryView":846 + * start = 0 + * + * if have_stop: # <<<<<<<<<<<<<< + * if stop < 0: + * stop += shape +*/ + goto __pyx_L13; + } + + /* "View.MemoryView":854 + * stop = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * stop = -1 + * else: +*/ + /*else*/ { + if (__pyx_v_negative_step) { + + /* "View.MemoryView":855 + * else: + * if negative_step: + * stop = -1 # <<<<<<<<<<<<<< + * else: + * stop = shape +*/ + __pyx_v_stop = -1L; + + /* "View.MemoryView":854 + * stop = shape + * else: + * if negative_step: # <<<<<<<<<<<<<< + * stop = -1 + * else: +*/ + goto __pyx_L16; + } + + /* "View.MemoryView":857 + * stop = -1 + * else: + * stop = shape # <<<<<<<<<<<<<< + * + * +*/ + /*else*/ { + __pyx_v_stop = __pyx_v_shape; + } + __pyx_L16:; + } + __pyx_L13:; + + /* "View.MemoryView":861 + * + * with cython.cdivision(True): + * new_shape = (stop - start) // step # <<<<<<<<<<<<<< + * + * if (stop - start) - step * new_shape: +*/ + __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step); + + /* "View.MemoryView":863 + * new_shape = (stop - start) // step + * + * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< + * new_shape += 1 + * +*/ + __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0); + if (__pyx_t_2) { + + /* "View.MemoryView":864 + * + * if (stop - start) - step * new_shape: + * new_shape += 1 # <<<<<<<<<<<<<< + * + * if new_shape < 0: +*/ + __pyx_v_new_shape = (__pyx_v_new_shape + 1); + + /* "View.MemoryView":863 + * new_shape = (stop - start) // step + * + * if (stop - start) - step * new_shape: # <<<<<<<<<<<<<< + * new_shape += 1 + * +*/ + } + + /* "View.MemoryView":866 + * new_shape += 1 + * + * if new_shape < 0: # <<<<<<<<<<<<<< + * new_shape = 0 + * +*/ + __pyx_t_2 = (__pyx_v_new_shape < 0); + if (__pyx_t_2) { + + /* "View.MemoryView":867 + * + * if new_shape < 0: + * new_shape = 0 # <<<<<<<<<<<<<< + * + * +*/ + __pyx_v_new_shape = 0; + + /* "View.MemoryView":866 + * new_shape += 1 + * + * if new_shape < 0: # <<<<<<<<<<<<<< + * new_shape = 0 + * +*/ + } + + /* "View.MemoryView":870 + * + * + * dst.strides[new_ndim] = stride * step # <<<<<<<<<<<<<< + * dst.shape[new_ndim] = new_shape + * dst.suboffsets[new_ndim] = suboffset +*/ + (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step); + + /* "View.MemoryView":871 + * + * dst.strides[new_ndim] = stride * step + * dst.shape[new_ndim] = new_shape # <<<<<<<<<<<<<< + * dst.suboffsets[new_ndim] = suboffset + * +*/ + (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape; + + /* "View.MemoryView":872 + * dst.strides[new_ndim] = stride * step + * dst.shape[new_ndim] = new_shape + * dst.suboffsets[new_ndim] = suboffset # <<<<<<<<<<<<<< + * + * +*/ + (__pyx_v_dst->suboffsets[__pyx_v_new_ndim]) = __pyx_v_suboffset; + } + __pyx_L3:; + + /* "View.MemoryView":875 + * + * + * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< + * dst.data += start * stride + * else: +*/ + __pyx_t_2 = ((__pyx_v_suboffset_dim[0]) < 0); + if (__pyx_t_2) { + + /* "View.MemoryView":876 + * + * if suboffset_dim[0] < 0: + * dst.data += start * stride # <<<<<<<<<<<<<< + * else: + * dst.suboffsets[suboffset_dim[0]] += start * stride +*/ + __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride)); + + /* "View.MemoryView":875 + * + * + * if suboffset_dim[0] < 0: # <<<<<<<<<<<<<< + * dst.data += start * stride + * else: +*/ + goto __pyx_L19; + } + + /* "View.MemoryView":878 + * dst.data += start * stride + * else: + * dst.suboffsets[suboffset_dim[0]] += start * stride # <<<<<<<<<<<<<< + * + * if suboffset >= 0: +*/ + /*else*/ { + __pyx_t_3 = (__pyx_v_suboffset_dim[0]); + (__pyx_v_dst->suboffsets[__pyx_t_3]) = ((__pyx_v_dst->suboffsets[__pyx_t_3]) + (__pyx_v_start * __pyx_v_stride)); + } + __pyx_L19:; + + /* "View.MemoryView":880 + * dst.suboffsets[suboffset_dim[0]] += start * stride + * + * if suboffset >= 0: # <<<<<<<<<<<<<< + * if not is_slice: + * if new_ndim == 0: +*/ + __pyx_t_2 = (__pyx_v_suboffset >= 0); + if (__pyx_t_2) { + + /* "View.MemoryView":881 + * + * if suboffset >= 0: + * if not is_slice: # <<<<<<<<<<<<<< + * if new_ndim == 0: + * dst.data = ( dst.data)[0] + suboffset +*/ + __pyx_t_2 = (!__pyx_v_is_slice); + if (__pyx_t_2) { + + /* "View.MemoryView":882 + * if suboffset >= 0: + * if not is_slice: + * if new_ndim == 0: # <<<<<<<<<<<<<< + * dst.data = ( dst.data)[0] + suboffset + * else: +*/ + __pyx_t_2 = (__pyx_v_new_ndim == 0); + if (__pyx_t_2) { + + /* "View.MemoryView":883 + * if not is_slice: + * if new_ndim == 0: + * dst.data = ( dst.data)[0] + suboffset # <<<<<<<<<<<<<< + * else: + * _err_dim(PyExc_IndexError, "All dimensions preceding dimension %d " +*/ + __pyx_v_dst->data = ((((char **)__pyx_v_dst->data)[0]) + __pyx_v_suboffset); + + /* "View.MemoryView":882 + * if suboffset >= 0: + * if not is_slice: + * if new_ndim == 0: # <<<<<<<<<<<<<< + * dst.data = ( dst.data)[0] + suboffset + * else: +*/ + goto __pyx_L22; + } + + /* "View.MemoryView":885 + * dst.data = ( dst.data)[0] + suboffset + * else: + * _err_dim(PyExc_IndexError, "All dimensions preceding dimension %d " # <<<<<<<<<<<<<< + * "must be indexed and not sliced", dim) + * else: +*/ + /*else*/ { + + /* "View.MemoryView":886 + * else: + * _err_dim(PyExc_IndexError, "All dimensions preceding dimension %d " + * "must be indexed and not sliced", dim) # <<<<<<<<<<<<<< + * else: + * suboffset_dim[0] = new_ndim +*/ + __pyx_t_3 = __pyx_memoryview_err_dim(PyExc_IndexError, __pyx_mstate_global->__pyx_kp_u_All_dimensions_preceding_dimensi, __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 885, __pyx_L1_error) + } + __pyx_L22:; + + /* "View.MemoryView":881 + * + * if suboffset >= 0: + * if not is_slice: # <<<<<<<<<<<<<< + * if new_ndim == 0: + * dst.data = ( dst.data)[0] + suboffset +*/ + goto __pyx_L21; + } + + /* "View.MemoryView":888 + * "must be indexed and not sliced", dim) + * else: + * suboffset_dim[0] = new_ndim # <<<<<<<<<<<<<< + * + * return 0 +*/ + /*else*/ { + (__pyx_v_suboffset_dim[0]) = __pyx_v_new_ndim; + } + __pyx_L21:; + + /* "View.MemoryView":880 + * dst.suboffsets[suboffset_dim[0]] += start * stride + * + * if suboffset >= 0: # <<<<<<<<<<<<<< + * if not is_slice: + * if new_ndim == 0: +*/ + } + + /* "View.MemoryView":890 + * suboffset_dim[0] = new_ndim + * + * return 0 # <<<<<<<<<<<<<< + * + * +*/ + __pyx_r = 0; + goto __pyx_L0; + + /* "View.MemoryView":792 + * + * + * @cname('__pyx_memoryview_slice_memviewslice') # <<<<<<<<<<<<<< + * cdef int slice_memviewslice( + * __Pyx_memviewslice *dst, +*/ + + /* function exit code */ + __pyx_L1_error:; + __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + __Pyx_AddTraceback("View.MemoryView.slice_memviewslice", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = -1; + __Pyx_PyGILState_Release(__pyx_gilstate_save); + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":895 + * + * + * @cname('__pyx_pybuffer_index') # <<<<<<<<<<<<<< + * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, + * Py_ssize_t dim) except NULL: +*/ + +static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, Py_ssize_t __pyx_v_index, Py_ssize_t __pyx_v_dim) { + Py_ssize_t __pyx_v_shape; + Py_ssize_t __pyx_v_stride; + Py_ssize_t __pyx_v_suboffset; + Py_ssize_t __pyx_v_itemsize; + char *__pyx_v_resultp; + char *__pyx_r; + __Pyx_RefNannyDeclarations + Py_ssize_t __pyx_t_1; + int __pyx_t_2; + PyObject *__pyx_t_3 = NULL; + PyObject *__pyx_t_4[3]; + PyObject *__pyx_t_5 = NULL; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + __Pyx_RefNannySetupContext("pybuffer_index", 0); + + /* "View.MemoryView":898 + * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index, + * Py_ssize_t dim) except NULL: + * cdef Py_ssize_t shape, stride, suboffset = -1 # <<<<<<<<<<<<<< + * cdef Py_ssize_t itemsize = view.itemsize + * cdef char *resultp +*/ + __pyx_v_suboffset = -1L; + + /* "View.MemoryView":899 + * Py_ssize_t dim) except NULL: + * cdef Py_ssize_t shape, stride, suboffset = -1 + * cdef Py_ssize_t itemsize = view.itemsize # <<<<<<<<<<<<<< + * cdef char *resultp + * +*/ + __pyx_t_1 = __pyx_v_view->itemsize; + __pyx_v_itemsize = __pyx_t_1; + + /* "View.MemoryView":902 + * cdef char *resultp + * + * if view.ndim == 0: # <<<<<<<<<<<<<< + * shape = view.len // itemsize + * stride = itemsize +*/ + __pyx_t_2 = (__pyx_v_view->ndim == 0); + if (__pyx_t_2) { + + /* "View.MemoryView":903 + * + * if view.ndim == 0: + * shape = view.len // itemsize # <<<<<<<<<<<<<< + * stride = itemsize + * else: +*/ + if (unlikely(__pyx_v_itemsize == 0)) { + PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero"); 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+ + /* "View.MemoryView":1103 + * return memoryview_fromslice(memviewslice[0], memview.view.ndim, + * to_object_func, to_dtype_func, + * memview.dtype_is_object) # <<<<<<<<<<<<<< + * + * +*/ + __pyx_t_4 = __pyx_memoryview_fromslice((__pyx_v_memviewslice[0]), __pyx_v_memview->view.ndim, __pyx_v_to_object_func, __pyx_v_to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_4)) __PYX_ERR(1, 1101, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_4); + __pyx_r = __pyx_t_4; + __pyx_t_4 = 0; + goto __pyx_L0; + + /* "View.MemoryView":1086 + * return memoryview_copy_from_slice(memview, &memviewslice) + * + * @cname('__pyx_memoryview_copy_object_from_slice') # <<<<<<<<<<<<<< + * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice): + * """ +*/ + + /* function exit code */ + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_4); + __Pyx_AddTraceback("View.MemoryView.memoryview_copy_from_slice", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = 0; + __pyx_L0:; + __Pyx_XGIVEREF(__pyx_r); + __Pyx_RefNannyFinishContext(); + return __pyx_r; +} + +/* "View.MemoryView":1109 + * + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) noexcept nogil: # <<<<<<<<<<<<<< + * return -arg if arg < 0 else arg + * +*/ + +static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) { + Py_ssize_t __pyx_r; + Py_ssize_t __pyx_t_1; + int __pyx_t_2; + + /* "View.MemoryView":1110 + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) noexcept nogil: + * return -arg if arg < 0 else arg # <<<<<<<<<<<<<< + * + * @cname('__pyx_get_best_slice_order') +*/ + __pyx_t_2 = (__pyx_v_arg < 0); + if (__pyx_t_2) { + __pyx_t_1 = (-__pyx_v_arg); + } else { + __pyx_t_1 = __pyx_v_arg; + } + __pyx_r = __pyx_t_1; + goto __pyx_L0; + + /* "View.MemoryView":1109 + * + * + * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) noexcept nogil: # <<<<<<<<<<<<<< + * return -arg if arg < 0 else arg + * +*/ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1112 + * return -arg if arg < 0 else arg + * + * @cname('__pyx_get_best_slice_order') # <<<<<<<<<<<<<< + * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) noexcept nogil: + * """ +*/ + +static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim) { + int __pyx_v_i; + Py_ssize_t __pyx_v_c_stride; + Py_ssize_t __pyx_v_f_stride; + char __pyx_r; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + int __pyx_t_4; + + /* "View.MemoryView":1118 + * """ + * cdef int i + * cdef Py_ssize_t c_stride = 0 # <<<<<<<<<<<<<< + * cdef Py_ssize_t f_stride = 0 + * +*/ + __pyx_v_c_stride = 0; + + /* "View.MemoryView":1119 + * cdef int i + * cdef Py_ssize_t c_stride = 0 + * cdef Py_ssize_t f_stride = 0 # <<<<<<<<<<<<<< + * + * for i in range(ndim - 1, -1, -1): +*/ + __pyx_v_f_stride = 0; + + /* "View.MemoryView":1121 + * cdef Py_ssize_t f_stride = 0 + * + * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] +*/ + for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { + __pyx_v_i = __pyx_t_1; + + /* "View.MemoryView":1122 + * + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * c_stride = mslice.strides[i] + * break +*/ + __pyx_t_2 = ((__pyx_v_mslice->shape[__pyx_v_i]) > 1); + if (__pyx_t_2) { + + /* "View.MemoryView":1123 + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] # <<<<<<<<<<<<<< + * break + * +*/ + __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1124 + * if mslice.shape[i] > 1: + * c_stride = mslice.strides[i] + * break # <<<<<<<<<<<<<< + * + * for i in range(ndim): +*/ + goto __pyx_L4_break; + + /* "View.MemoryView":1122 + * + * for i in range(ndim - 1, -1, -1): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * c_stride = mslice.strides[i] + * break +*/ + } + } + __pyx_L4_break:; + + /* "View.MemoryView":1126 + * break + * + * for i in range(ndim): # <<<<<<<<<<<<<< + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] +*/ + __pyx_t_1 = __pyx_v_ndim; + __pyx_t_3 = __pyx_t_1; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_i = __pyx_t_4; + + /* "View.MemoryView":1127 + * + * for i in range(ndim): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * f_stride = mslice.strides[i] + * break +*/ + __pyx_t_2 = ((__pyx_v_mslice->shape[__pyx_v_i]) > 1); + if (__pyx_t_2) { + + /* "View.MemoryView":1128 + * for i in range(ndim): + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] # <<<<<<<<<<<<<< + * break + * +*/ + __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1129 + * if mslice.shape[i] > 1: + * f_stride = mslice.strides[i] + * break # <<<<<<<<<<<<<< + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): +*/ + goto __pyx_L7_break; + + /* "View.MemoryView":1127 + * + * for i in range(ndim): + * if mslice.shape[i] > 1: # <<<<<<<<<<<<<< + * f_stride = mslice.strides[i] + * break +*/ + } + } + __pyx_L7_break:; + + /* "View.MemoryView":1131 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: +*/ + __pyx_t_2 = (abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)); + if (__pyx_t_2) { + + /* "View.MemoryView":1132 + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): + * return 'C' # <<<<<<<<<<<<<< + * else: + * return 'F' +*/ + __pyx_r = 'C'; + goto __pyx_L0; + + /* "View.MemoryView":1131 + * break + * + * if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride): # <<<<<<<<<<<<<< + * return 'C' + * else: +*/ + } + + /* "View.MemoryView":1134 + * return 'C' + * else: + * return 'F' # <<<<<<<<<<<<<< + * + * @cython.cdivision(True) +*/ + /*else*/ { + __pyx_r = 'F'; + goto __pyx_L0; + } + + /* "View.MemoryView":1112 + * return -arg if arg < 0 else arg + * + * @cname('__pyx_get_best_slice_order') # <<<<<<<<<<<<<< + * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) noexcept nogil: + * """ +*/ + + /* function exit code */ + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1136 + * return 'F' + * + * @cython.cdivision(True) # <<<<<<<<<<<<<< + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, + * char *dst_data, Py_ssize_t *dst_strides, +*/ + +static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) { + CYTHON_UNUSED Py_ssize_t __pyx_v_i; + CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent; + Py_ssize_t __pyx_v_dst_extent; + Py_ssize_t __pyx_v_src_stride; + Py_ssize_t __pyx_v_dst_stride; + int __pyx_t_1; + int __pyx_t_2; + Py_ssize_t __pyx_t_3; + Py_ssize_t __pyx_t_4; + Py_ssize_t __pyx_t_5; + + /* "View.MemoryView":1144 + * + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] +*/ + __pyx_v_src_extent = (__pyx_v_src_shape[0]); + + /* "View.MemoryView":1145 + * cdef Py_ssize_t i + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] +*/ + __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); + + /* "View.MemoryView":1146 + * cdef Py_ssize_t src_extent = src_shape[0] + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t dst_stride = dst_strides[0] + * +*/ + __pyx_v_src_stride = (__pyx_v_src_strides[0]); + + /* "View.MemoryView":1147 + * cdef Py_ssize_t dst_extent = dst_shape[0] + * cdef Py_ssize_t src_stride = src_strides[0] + * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< + * + * if ndim == 1: +*/ + __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); + + /* "View.MemoryView":1149 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): +*/ + __pyx_t_1 = (__pyx_v_ndim == 1); + if (__pyx_t_1) { + + /* "View.MemoryView":1150 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) +*/ + __pyx_t_2 = (__pyx_v_src_stride > 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + __pyx_t_2 = (__pyx_v_dst_stride > 0); + if (__pyx_t_2) { + } else { + __pyx_t_1 = __pyx_t_2; + goto __pyx_L5_bool_binop_done; + } + + /* "View.MemoryView":1151 + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: +*/ + __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); + if (__pyx_t_2) { + __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); + } + __pyx_t_1 = __pyx_t_2; + __pyx_L5_bool_binop_done:; + + /* "View.MemoryView":1150 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) +*/ + if (__pyx_t_1) { + + /* "View.MemoryView":1152 + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): +*/ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent))); + + /* "View.MemoryView":1150 + * + * if ndim == 1: + * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< + * src_stride == itemsize == dst_stride): + * memcpy(dst_data, src_data, itemsize * dst_extent) +*/ + goto __pyx_L4; + } + + /* "View.MemoryView":1154 + * memcpy(dst_data, src_data, itemsize * dst_extent) + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride +*/ + /*else*/ { + __pyx_t_3 = __pyx_v_dst_extent; + __pyx_t_4 = __pyx_t_3; + for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { + __pyx_v_i = __pyx_t_5; + + /* "View.MemoryView":1155 + * else: + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< + * src_data += src_stride + * dst_data += dst_stride +*/ + (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize)); + + /* "View.MemoryView":1156 + * for i in range(dst_extent): + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * else: +*/ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1157 + * memcpy(dst_data, src_data, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * else: + * for i in range(dst_extent): +*/ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L4:; + + /* "View.MemoryView":1149 + * cdef Py_ssize_t dst_stride = dst_strides[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * if (src_stride > 0 and dst_stride > 0 and + * src_stride == itemsize == dst_stride): +*/ + goto __pyx_L3; + } + + /* "View.MemoryView":1159 + * dst_data += dst_stride + * else: + * for i in range(dst_extent): # <<<<<<<<<<<<<< + * _copy_strided_to_strided(src_data, src_strides + 1, + * dst_data, dst_strides + 1, +*/ + /*else*/ { + __pyx_t_3 = __pyx_v_dst_extent; + __pyx_t_4 = __pyx_t_3; + for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { + __pyx_v_i = __pyx_t_5; + + /* "View.MemoryView":1160 + * else: + * for i in range(dst_extent): + * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< + * dst_data, dst_strides + 1, + * src_shape + 1, dst_shape + 1, +*/ + _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); + + /* "View.MemoryView":1164 + * src_shape + 1, dst_shape + 1, + * ndim - 1, itemsize) + * src_data += src_stride # <<<<<<<<<<<<<< + * dst_data += dst_stride + * +*/ + __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); + + /* "View.MemoryView":1165 + * ndim - 1, itemsize) + * src_data += src_stride + * dst_data += dst_stride # <<<<<<<<<<<<<< + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, +*/ + __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); + } + } + __pyx_L3:; + + /* "View.MemoryView":1136 + * return 'F' + * + * @cython.cdivision(True) # <<<<<<<<<<<<<< + * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, + * char *dst_data, Py_ssize_t *dst_strides, +*/ + + /* function exit code */ +} + +/* "View.MemoryView":1167 + * dst_data += dst_stride + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) noexcept nogil: +*/ + +static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { + + /* "View.MemoryView":1170 + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) noexcept nogil: + * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, # <<<<<<<<<<<<<< + * src.shape, dst.shape, ndim, itemsize) + * +*/ + _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize); + + /* "View.MemoryView":1167 + * dst_data += dst_stride + * + * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< + * __Pyx_memviewslice *dst, + * int ndim, size_t itemsize) noexcept nogil: +*/ + + /* function exit code */ +} + +/* "View.MemoryView":1173 + * src.shape, dst.shape, ndim, itemsize) + * + * @cname('__pyx_memoryview_slice_get_size') # <<<<<<<<<<<<<< + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil: + * "Return the size of the memory occupied by the slice in number of bytes" +*/ + +static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) { + Py_ssize_t __pyx_v_shape; + Py_ssize_t __pyx_v_size; + Py_ssize_t __pyx_r; + Py_ssize_t __pyx_t_1; + Py_ssize_t *__pyx_t_2; + Py_ssize_t *__pyx_t_3; + Py_ssize_t *__pyx_t_4; + + /* "View.MemoryView":1176 + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil: + * "Return the size of the memory occupied by the slice in number of bytes" + * cdef Py_ssize_t shape, size = src.memview.view.itemsize # <<<<<<<<<<<<<< + * + * for shape in src.shape[:ndim]: +*/ + __pyx_t_1 = __pyx_v_src->memview->view.itemsize; + __pyx_v_size = __pyx_t_1; + + /* "View.MemoryView":1178 + * cdef Py_ssize_t shape, size = src.memview.view.itemsize + * + * for shape in src.shape[:ndim]: # <<<<<<<<<<<<<< + * size *= shape + * +*/ + __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim); + for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) { + __pyx_t_2 = __pyx_t_4; + __pyx_v_shape = (__pyx_t_2[0]); + + /* "View.MemoryView":1179 + * + * for shape in src.shape[:ndim]: + * size *= shape # <<<<<<<<<<<<<< + * + * return size +*/ + __pyx_v_size = (__pyx_v_size * __pyx_v_shape); + } + + /* "View.MemoryView":1181 + * size *= shape + * + * return size # <<<<<<<<<<<<<< + * + * @cname('__pyx_fill_contig_strides_array') +*/ + __pyx_r = __pyx_v_size; + goto __pyx_L0; + + /* "View.MemoryView":1173 + * src.shape, dst.shape, ndim, itemsize) + * + * @cname('__pyx_memoryview_slice_get_size') # <<<<<<<<<<<<<< + * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil: + * "Return the size of the memory occupied by the slice in number of bytes" +*/ + + /* function exit code */ + __pyx_L0:; 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if (unlikely(__pyx_t_7 == ((void *)0))) __PYX_ERR(1, 1304, __pyx_L1_error) + __pyx_v_tmpdata = __pyx_t_7; + + /* "View.MemoryView":1305 + * + * tmpdata = copy_data_to_temp(&src, &tmp, order, ndim) + * src = tmp # <<<<<<<<<<<<<< + * + * if not broadcasting: +*/ + __pyx_v_src = __pyx_v_tmp; + + /* "View.MemoryView":1299 + * _err_dim(PyExc_ValueError, "Dimension %d is not direct", i) + * + * if slices_overlap(&src, &dst, ndim, itemsize): # <<<<<<<<<<<<<< + * + * if not slice_is_contig(src, order, ndim): +*/ + } + + /* "View.MemoryView":1307 + * src = tmp + * + * if not broadcasting: # <<<<<<<<<<<<<< + * + * +*/ + __pyx_t_2 = (!__pyx_v_broadcasting); + if (__pyx_t_2) { + + /* "View.MemoryView":1310 + * + * + * if slice_is_contig(src, 'C', ndim): # <<<<<<<<<<<<<< + * direct_copy = slice_is_contig(dst, 'C', ndim) + * elif slice_is_contig(src, 'F', ndim): +*/ + __pyx_t_2 = __pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim); + if (__pyx_t_2) { + + /* "View.MemoryView":1311 + * + * if slice_is_contig(src, 'C', ndim): + * direct_copy = slice_is_contig(dst, 'C', ndim) # <<<<<<<<<<<<<< + * elif slice_is_contig(src, 'F', ndim): + * direct_copy = slice_is_contig(dst, 'F', ndim) +*/ + __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim); 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+ + /* "View.MemoryView":1320 + * memcpy(dst.data, src.data, slice_get_size(&src, ndim)) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * free(tmpdata) # <<<<<<<<<<<<<< + * return 0 + * +*/ + free(__pyx_v_tmpdata); + + /* "View.MemoryView":1321 + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * free(tmpdata) + * return 0 # <<<<<<<<<<<<<< + * + * if order == 'F' == get_best_order(&dst, ndim): +*/ + __pyx_r = 0; + goto __pyx_L0; + + /* "View.MemoryView":1315 + * direct_copy = slice_is_contig(dst, 'F', ndim) + * + * if direct_copy: # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) +*/ + } + + /* "View.MemoryView":1307 + * src = tmp + * + * if not broadcasting: # <<<<<<<<<<<<<< + * + * +*/ + } + + /* "View.MemoryView":1323 + * return 0 + * + * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< + * + * +*/ + __pyx_t_2 = (__pyx_v_order == 'F'); + if (__pyx_t_2) { + __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim)); + } + if (__pyx_t_2) { + + /* "View.MemoryView":1326 + * + * + * transpose_memslice(&src) # <<<<<<<<<<<<<< + * transpose_memslice(&dst) + * +*/ + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)-1))) __PYX_ERR(1, 1326, __pyx_L1_error) + + /* "View.MemoryView":1327 + * + * transpose_memslice(&src) + * transpose_memslice(&dst) # <<<<<<<<<<<<<< + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) +*/ + __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)-1))) __PYX_ERR(1, 1327, __pyx_L1_error) + + /* "View.MemoryView":1323 + * return 0 + * + * if order == 'F' == get_best_order(&dst, ndim): # <<<<<<<<<<<<<< + * + * +*/ + } + + /* "View.MemoryView":1329 + * transpose_memslice(&dst) + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) # <<<<<<<<<<<<<< + * copy_strided_to_strided(&src, &dst, ndim, itemsize) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) +*/ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0); + + /* "View.MemoryView":1330 + * + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + * copy_strided_to_strided(&src, &dst, ndim, itemsize) # <<<<<<<<<<<<<< + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * +*/ + copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize); + + /* "View.MemoryView":1331 + * refcount_copying(&dst, dtype_is_object, ndim, inc=False) + * copy_strided_to_strided(&src, &dst, ndim, itemsize) + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) # <<<<<<<<<<<<<< + * + * free(tmpdata) +*/ + __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1); + + /* "View.MemoryView":1333 + * refcount_copying(&dst, dtype_is_object, ndim, inc=True) + * + * free(tmpdata) # <<<<<<<<<<<<<< + * return 0 + * +*/ + free(__pyx_v_tmpdata); + + /* "View.MemoryView":1334 + * + * free(tmpdata) + * return 0 # <<<<<<<<<<<<<< + * + * @cname('__pyx_memoryview_broadcast_leading') +*/ + __pyx_r = 0; + goto __pyx_L0; + + /* "View.MemoryView":1264 + * + * + * @cname('__pyx_memoryview_copy_contents') # <<<<<<<<<<<<<< + * cdef int memoryview_copy_contents(__Pyx_memviewslice src, + * __Pyx_memviewslice dst, +*/ + + /* function exit code */ + __pyx_L1_error:; + __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename); + __pyx_r = -1; + __Pyx_PyGILState_Release(__pyx_gilstate_save); + __pyx_L0:; + return __pyx_r; +} + +/* "View.MemoryView":1336 + * return 0 + * + * @cname('__pyx_memoryview_broadcast_leading') # <<<<<<<<<<<<<< + * cdef void broadcast_leading(__Pyx_memviewslice *mslice, + * int ndim, +*/ + +static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim, int __pyx_v_ndim_other) { + int __pyx_v_i; + int __pyx_v_offset; + int __pyx_t_1; + int __pyx_t_2; + int __pyx_t_3; + + /* "View.MemoryView":1341 + * int ndim_other) noexcept nogil: + * cdef int i + * cdef int offset = ndim_other - ndim # <<<<<<<<<<<<<< + * + * for i in range(ndim - 1, -1, -1): +*/ + __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim); + + /* "View.MemoryView":1343 + * cdef int offset = ndim_other - ndim + * + * for i in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< + * mslice.shape[i + offset] = mslice.shape[i] + * mslice.strides[i + offset] = mslice.strides[i] +*/ + for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) { + __pyx_v_i = __pyx_t_1; + + /* "View.MemoryView":1344 + * + * for i in range(ndim - 1, -1, -1): + * mslice.shape[i + offset] = mslice.shape[i] # <<<<<<<<<<<<<< + * mslice.strides[i + offset] = mslice.strides[i] + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] +*/ + (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]); + + /* "View.MemoryView":1345 + * for i in range(ndim - 1, -1, -1): + * mslice.shape[i + offset] = mslice.shape[i] + * mslice.strides[i + offset] = mslice.strides[i] # <<<<<<<<<<<<<< + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] + * +*/ + (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]); + + /* "View.MemoryView":1346 + * mslice.shape[i + offset] = mslice.shape[i] + * mslice.strides[i + offset] = mslice.strides[i] + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] # <<<<<<<<<<<<<< + * + * for i in range(offset): +*/ + (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]); + } + + /* "View.MemoryView":1348 + * mslice.suboffsets[i + offset] = mslice.suboffsets[i] + * + * for i in range(offset): # <<<<<<<<<<<<<< + * mslice.shape[i] = 1 + * mslice.strides[i] = mslice.strides[0] +*/ + __pyx_t_1 = __pyx_v_offset; + __pyx_t_2 = __pyx_t_1; + for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { + __pyx_v_i = __pyx_t_3; + + /* "View.MemoryView":1349 + * + * for i in range(offset): + * mslice.shape[i] = 1 # <<<<<<<<<<<<<< + * mslice.strides[i] = mslice.strides[0] + * mslice.suboffsets[i] = -1 +*/ + (__pyx_v_mslice->shape[__pyx_v_i]) = 1; + + /* "View.MemoryView":1350 + * for i in range(offset): + * mslice.shape[i] = 1 + * mslice.strides[i] = mslice.strides[0] # <<<<<<<<<<<<<< + * mslice.suboffsets[i] = -1 + * +*/ + (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]); + + /* "View.MemoryView":1351 + * mslice.shape[i] = 1 + * mslice.strides[i] = mslice.strides[0] + * mslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< + * + * +*/ + (__pyx_v_mslice->suboffsets[__pyx_v_i]) = -1L; + } + + /* "View.MemoryView":1336 + * return 0 + * + * @cname('__pyx_memoryview_broadcast_leading') # <<<<<<<<<<<<<< + * cdef void broadcast_leading(__Pyx_memviewslice *mslice, + * int ndim, +*/ + + /* function exit code */ +} + +/* "View.MemoryView":1358 + * + * + * @cname('__pyx_memoryview_refcount_copying') # <<<<<<<<<<<<<< + * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, int ndim, bint inc) noexcept nogil: + * +*/ + +static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) { + + /* "View.MemoryView":1361 + * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, int ndim, bint inc) noexcept nogil: + * + * if dtype_is_object: # <<<<<<<<<<<<<< + * refcount_objects_in_slice_with_gil(dst.data, dst.shape, dst.strides, ndim, inc) + * +*/ + if (__pyx_v_dtype_is_object) { + + /* "View.MemoryView":1362 + * + * if dtype_is_object: + * refcount_objects_in_slice_with_gil(dst.data, dst.shape, dst.strides, ndim, inc) # <<<<<<<<<<<<<< + * + * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') +*/ + __pyx_memoryview_refcount_objects_in_slice_with_gil(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_inc); + + /* "View.MemoryView":1361 + * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, int ndim, bint inc) noexcept nogil: + * + * if dtype_is_object: # <<<<<<<<<<<<<< + * refcount_objects_in_slice_with_gil(dst.data, dst.shape, dst.strides, ndim, inc) + * +*/ + } + + /* "View.MemoryView":1358 + * + * + * @cname('__pyx_memoryview_refcount_copying') # <<<<<<<<<<<<<< + * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object, int ndim, bint inc) noexcept nogil: + * +*/ + + /* function exit code */ +} + +/* "View.MemoryView":1364 + * refcount_objects_in_slice_with_gil(dst.data, dst.shape, dst.strides, ndim, inc) + * + * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') # <<<<<<<<<<<<<< + * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape, + * Py_ssize_t *strides, int ndim, +*/ + +static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) { + PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure(); + + /* "View.MemoryView":1368 + * Py_ssize_t *strides, int ndim, + * bint inc) noexcept with gil: + * refcount_objects_in_slice(data, shape, strides, ndim, inc) # <<<<<<<<<<<<<< + * + * @cname('__pyx_memoryview_refcount_objects_in_slice') +*/ + __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, __pyx_v_shape, __pyx_v_strides, __pyx_v_ndim, __pyx_v_inc); + + /* "View.MemoryView":1364 + * refcount_objects_in_slice_with_gil(dst.data, dst.shape, dst.strides, ndim, inc) + * + * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil') # <<<<<<<<<<<<<< + * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape, + * Py_ssize_t *strides, int ndim, +*/ + + /* function exit code */ + __Pyx_PyGILState_Release(__pyx_gilstate_save); +} + +/* "View.MemoryView":1370 + * refcount_objects_in_slice(data, shape, strides, ndim, inc) + * + * @cname('__pyx_memoryview_refcount_objects_in_slice') # <<<<<<<<<<<<<< + * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape, + * Py_ssize_t *strides, int ndim, bint inc) noexcept: +*/ + +static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) { + CYTHON_UNUSED Py_ssize_t __pyx_v_i; + Py_ssize_t __pyx_v_stride; + Py_ssize_t __pyx_t_1; + Py_ssize_t __pyx_t_2; + Py_ssize_t __pyx_t_3; + int __pyx_t_4; + + /* "View.MemoryView":1374 + * Py_ssize_t *strides, int ndim, bint inc) noexcept: + * cdef Py_ssize_t i + * cdef Py_ssize_t stride = strides[0] # <<<<<<<<<<<<<< + * + * for i in range(shape[0]): +*/ + __pyx_v_stride = (__pyx_v_strides[0]); + + /* "View.MemoryView":1376 + * cdef Py_ssize_t stride = strides[0] + * + * for i in range(shape[0]): # <<<<<<<<<<<<<< + * if ndim == 1: + * if inc: +*/ + __pyx_t_1 = (__pyx_v_shape[0]); + __pyx_t_2 = __pyx_t_1; + for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) { + __pyx_v_i = __pyx_t_3; + + /* "View.MemoryView":1377 + * + * for i in range(shape[0]): + * if ndim == 1: # <<<<<<<<<<<<<< + * if inc: + * Py_INCREF(( data)[0]) +*/ + __pyx_t_4 = (__pyx_v_ndim == 1); + if (__pyx_t_4) { + + /* "View.MemoryView":1378 + * for i in range(shape[0]): + * if ndim == 1: + * if inc: # <<<<<<<<<<<<<< + * Py_INCREF(( data)[0]) + * else: +*/ + if (__pyx_v_inc) { + + /* "View.MemoryView":1379 + * if ndim == 1: + * if inc: + * Py_INCREF(( data)[0]) # <<<<<<<<<<<<<< + * else: + * Py_DECREF(( data)[0]) +*/ + Py_INCREF((((PyObject **)__pyx_v_data)[0])); + + /* "View.MemoryView":1378 + * for i in range(shape[0]): + * if ndim == 1: + * if inc: # <<<<<<<<<<<<<< + * Py_INCREF(( data)[0]) + * else: +*/ + goto __pyx_L6; + } + + /* "View.MemoryView":1381 + * Py_INCREF(( data)[0]) + * else: + * Py_DECREF(( data)[0]) # <<<<<<<<<<<<<< + * else: + * refcount_objects_in_slice(data, shape + 1, strides + 1, ndim - 1, inc) +*/ + /*else*/ { + Py_DECREF((((PyObject **)__pyx_v_data)[0])); + } + __pyx_L6:; + + /* "View.MemoryView":1377 + * + * for i in range(shape[0]): + * if ndim == 1: # <<<<<<<<<<<<<< + * if inc: + * Py_INCREF(( data)[0]) +*/ + goto __pyx_L5; + } + + /* "View.MemoryView":1383 + * Py_DECREF(( data)[0]) + * else: + * refcount_objects_in_slice(data, shape + 1, strides + 1, ndim - 1, inc) # <<<<<<<<<<<<<< + * + * data += stride +*/ + /*else*/ { + __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, (__pyx_v_shape + 1), (__pyx_v_strides + 1), (__pyx_v_ndim - 1), __pyx_v_inc); + } + __pyx_L5:; + + /* "View.MemoryView":1385 + * refcount_objects_in_slice(data, shape + 1, strides + 1, ndim - 1, inc) + * + * data += stride # <<<<<<<<<<<<<< + * + * +*/ + __pyx_v_data = (__pyx_v_data + __pyx_v_stride); + } + + /* "View.MemoryView":1370 + * refcount_objects_in_slice(data, shape, strides, ndim, inc) + * + * @cname('__pyx_memoryview_refcount_objects_in_slice') # <<<<<<<<<<<<<< + * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape, + * Py_ssize_t *strides, int ndim, bint inc) noexcept: +*/ + + /* function exit code */ +} + +/* "View.MemoryView":1390 + * + * + * @cname('__pyx_memoryview_slice_assign_scalar') # <<<<<<<<<<<<<< + * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim, + * size_t itemsize, void *item, +*/ + +static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item, int __pyx_v_dtype_is_object) { + + /* "View.MemoryView":1394 + * size_t itemsize, void *item, + * bint dtype_is_object) noexcept nogil: + * refcount_copying(dst, dtype_is_object, ndim, inc=False) # <<<<<<<<<<<<<< + * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, itemsize, item) + * refcount_copying(dst, dtype_is_object, ndim, inc=True) +*/ + __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 0); + + /* "View.MemoryView":1395 + * bint dtype_is_object) noexcept nogil: + * refcount_copying(dst, dtype_is_object, ndim, inc=False) + * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, itemsize, item) # <<<<<<<<<<<<<< + * refcount_copying(dst, dtype_is_object, ndim, inc=True) + * +*/ + __pyx_memoryview__slice_assign_scalar(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_itemsize, __pyx_v_item); + + /* "View.MemoryView":1396 + * refcount_copying(dst, dtype_is_object, ndim, inc=False) + * _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim, itemsize, item) + * refcount_copying(dst, dtype_is_object, ndim, inc=True) # <<<<<<<<<<<<<< + * + * +*/ + __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 1); + + /* "View.MemoryView":1390 + * + * + * @cname('__pyx_memoryview_slice_assign_scalar') # <<<<<<<<<<<<<< + * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim, + * size_t itemsize, void *item, +*/ + + /* function exit code */ +} + +/* "View.MemoryView":1399 + * + * + * @cname('__pyx_memoryview__slice_assign_scalar') # <<<<<<<<<<<<<< + * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape, + * Py_ssize_t *strides, int ndim, +*/ + +static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item) { + CYTHON_UNUSED Py_ssize_t __pyx_v_i; + Py_ssize_t __pyx_v_stride; + Py_ssize_t __pyx_v_extent; + int __pyx_t_1; + Py_ssize_t __pyx_t_2; + Py_ssize_t __pyx_t_3; + Py_ssize_t __pyx_t_4; + + /* "View.MemoryView":1404 + * size_t itemsize, void *item) noexcept nogil: + * cdef Py_ssize_t i + * cdef Py_ssize_t stride = strides[0] # <<<<<<<<<<<<<< + * cdef Py_ssize_t extent = shape[0] + * +*/ + __pyx_v_stride = (__pyx_v_strides[0]); + + /* "View.MemoryView":1405 + * cdef Py_ssize_t i + * cdef Py_ssize_t stride = strides[0] + * cdef Py_ssize_t extent = shape[0] # <<<<<<<<<<<<<< + * + * if ndim == 1: +*/ + __pyx_v_extent = (__pyx_v_shape[0]); + + /* "View.MemoryView":1407 + * cdef Py_ssize_t extent = shape[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * for i in range(extent): + * memcpy(data, item, itemsize) +*/ + __pyx_t_1 = (__pyx_v_ndim == 1); + if (__pyx_t_1) { + + /* "View.MemoryView":1408 + * + * if ndim == 1: + * for i in range(extent): # <<<<<<<<<<<<<< + * memcpy(data, item, itemsize) + * data += stride +*/ + __pyx_t_2 = __pyx_v_extent; + __pyx_t_3 = __pyx_t_2; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_i = __pyx_t_4; + + /* "View.MemoryView":1409 + * if ndim == 1: + * for i in range(extent): + * memcpy(data, item, itemsize) # <<<<<<<<<<<<<< + * data += stride + * else: +*/ + (void)(memcpy(__pyx_v_data, __pyx_v_item, __pyx_v_itemsize)); + + /* "View.MemoryView":1410 + * for i in range(extent): + * memcpy(data, item, itemsize) + * data += stride # <<<<<<<<<<<<<< + * else: + * for i in range(extent): +*/ + __pyx_v_data = (__pyx_v_data + __pyx_v_stride); + } + + /* "View.MemoryView":1407 + * cdef Py_ssize_t extent = shape[0] + * + * if ndim == 1: # <<<<<<<<<<<<<< + * for i in range(extent): + * memcpy(data, item, itemsize) +*/ + goto __pyx_L3; + } + + /* "View.MemoryView":1412 + * data += stride + * else: + * for i in range(extent): # <<<<<<<<<<<<<< + * _slice_assign_scalar(data, shape + 1, strides + 1, ndim - 1, itemsize, item) + * data += stride +*/ + /*else*/ { + __pyx_t_2 = __pyx_v_extent; + __pyx_t_3 = __pyx_t_2; + for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { + __pyx_v_i = __pyx_t_4; + + /* "View.MemoryView":1413 + * else: + * for i in range(extent): + * _slice_assign_scalar(data, shape + 1, strides + 1, ndim - 1, itemsize, item) # <<<<<<<<<<<<<< + * data += stride + * +*/ + __pyx_memoryview__slice_assign_scalar(__pyx_v_data, (__pyx_v_shape + 1), (__pyx_v_strides + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize, __pyx_v_item); + + /* "View.MemoryView":1414 + * for i in range(extent): + * _slice_assign_scalar(data, shape + 1, strides + 1, ndim - 1, itemsize, item) + * data += stride # <<<<<<<<<<<<<< + * + * +*/ + __pyx_v_data = (__pyx_v_data + __pyx_v_stride); + } + } + __pyx_L3:; + + /* "View.MemoryView":1399 + * + * + * @cname('__pyx_memoryview__slice_assign_scalar') # <<<<<<<<<<<<<< + * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape, + * Py_ssize_t *strides, int ndim, +*/ + + /* function exit code */ +} + +/* "(tree fragment)":1 + * def __pyx_unpickle_Enum(__pyx_type, long __pyx_checksum, __pyx_state): # <<<<<<<<<<<<<< + * cdef object __pyx_PickleError + * cdef object __pyx_result +*/ + +/* Python wrapper */ +static PyObject *__pyx_pw_15View_dot_MemoryView_1__pyx_unpickle_Enum(PyObject *__pyx_self, +#if CYTHON_METH_FASTCALL +PyObject *const *__pyx_args, Py_ssize_t __pyx_nargs, PyObject *__pyx_kwds +#else +PyObject *__pyx_args, PyObject *__pyx_kwds +#endif +); 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+ o_type_name = __Pyx_PyType_GetFullyQualifiedName(Py_TYPE(o)); + PyErr_Format(PyExc_NotImplementedError, + "Subscript deletion not supported by " __Pyx_FMT_TYPENAME, o_type_name); + __Pyx_DECREF_TypeName(o_type_name); + return -1; + } +} + +static PyObject *__pyx_tp_getattro_array(PyObject *o, PyObject *n) { + PyObject *v = PyObject_GenericGetAttr(o, n); + if (!v && PyErr_ExceptionMatches(PyExc_AttributeError)) { + PyErr_Clear(); + v = __pyx_array___getattr__(o, n); + } + return v; +} + +static PyObject *__pyx_getprop___pyx_array_memview(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(o); +} + +static PyMethodDef __pyx_methods_array[] = { + {"__getattr__", (PyCFunction)__pyx_array___getattr__, METH_O|METH_COEXIST, 0}, + {"__reduce_cython__", (PyCFunction)(void(*)(void))(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_array_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__setstate_cython__", (PyCFunction)(void(*)(void))(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_array_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets_array[] = { + {"memview", __pyx_getprop___pyx_array_memview, 0, 0, 0}, + {0, 0, 0, 0, 0} +}; +#if CYTHON_USE_TYPE_SPECS +#if !CYTHON_COMPILING_IN_LIMITED_API + +static PyBufferProcs __pyx_tp_as_buffer_array = { + __pyx_array_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; +#endif +static PyType_Slot __pyx_type___pyx_array_slots[] = { + {Py_tp_dealloc, (void *)__pyx_tp_dealloc_array}, + {Py_sq_length, (void *)__pyx_array___len__}, + {Py_sq_item, (void *)__pyx_sq_item_array}, + {Py_mp_length, (void *)__pyx_array___len__}, + {Py_mp_subscript, (void *)__pyx_array___getitem__}, + {Py_mp_ass_subscript, (void *)__pyx_mp_ass_subscript_array}, + {Py_tp_getattro, (void *)__pyx_tp_getattro_array}, + #if defined(Py_bf_getbuffer) + {Py_bf_getbuffer, (void *)__pyx_array_getbuffer}, + #endif + {Py_tp_methods, (void *)__pyx_methods_array}, + {Py_tp_getset, (void *)__pyx_getsets_array}, + {Py_tp_new, (void *)__pyx_tp_new_array}, + {0, 0}, +}; +static PyType_Spec __pyx_type___pyx_array_spec = { + "matris.graph.cygraph.array", + sizeof(struct __pyx_array_obj), + 0, + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_SEQUENCE, + __pyx_type___pyx_array_slots, +}; +#else + +static PySequenceMethods __pyx_tp_as_sequence_array = { + __pyx_array___len__, /*sq_length*/ + 0, /*sq_concat*/ + 0, /*sq_repeat*/ + __pyx_sq_item_array, /*sq_item*/ + 0, /*sq_slice*/ + 0, /*sq_ass_item*/ + 0, /*sq_ass_slice*/ + 0, /*sq_contains*/ + 0, /*sq_inplace_concat*/ + 0, /*sq_inplace_repeat*/ +}; + +static PyMappingMethods __pyx_tp_as_mapping_array = { + __pyx_array___len__, /*mp_length*/ + __pyx_array___getitem__, /*mp_subscript*/ + __pyx_mp_ass_subscript_array, /*mp_ass_subscript*/ +}; + +static PyBufferProcs __pyx_tp_as_buffer_array = { + __pyx_array_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; + +static PyTypeObject __pyx_type___pyx_array = { + PyVarObject_HEAD_INIT(0, 0) + "matris.graph.cygraph.""array", /*tp_name*/ + sizeof(struct __pyx_array_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_array, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + 0, /*tp_as_async*/ + 0, /*tp_repr*/ + 0, /*tp_as_number*/ + &__pyx_tp_as_sequence_array, /*tp_as_sequence*/ + &__pyx_tp_as_mapping_array, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + __pyx_tp_getattro_array, /*tp_getattro*/ + 0, /*tp_setattro*/ + &__pyx_tp_as_buffer_array, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_SEQUENCE, /*tp_flags*/ + 0, /*tp_doc*/ + 0, /*tp_traverse*/ + 0, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_array, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_array, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + #if !CYTHON_USE_TYPE_SPECS + 0, /*tp_dictoffset*/ + #endif + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_array, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if CYTHON_USE_TP_FINALIZE + 0, /*tp_finalize*/ + #else + NULL, /*tp_finalize*/ + #endif + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, /*tp_vectorcall*/ + #endif + #if __PYX_NEED_TP_PRINT_SLOT == 1 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030C0000 + 0, /*tp_watched*/ + #endif + #if PY_VERSION_HEX >= 0x030d00A4 + 0, /*tp_versions_used*/ + #endif + #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, /*tp_pypy_flags*/ + #endif +}; +#endif + +static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) { + struct __pyx_MemviewEnum_obj *p; + PyObject *o; + #if CYTHON_COMPILING_IN_LIMITED_API + allocfunc alloc_func = (allocfunc)PyType_GetSlot(t, Py_tp_alloc); + o = alloc_func(t, 0); + #else + if (likely(!__Pyx_PyType_HasFeature(t, Py_TPFLAGS_IS_ABSTRACT))) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_mstate_global->__pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + #endif + p = ((struct __pyx_MemviewEnum_obj *)o); + p->name = Py_None; Py_INCREF(Py_None); + return o; +} + +static void __pyx_tp_dealloc_Enum(PyObject *o) { + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) { + if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc_Enum) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + } + #endif + PyObject_GC_UnTrack(o); + Py_CLEAR(p->name); + #if CYTHON_USE_TYPE_SLOTS + (*Py_TYPE(o)->tp_free)(o); + #else + { + freefunc tp_free = (freefunc)PyType_GetSlot(Py_TYPE(o), Py_tp_free); + if (tp_free) tp_free(o); + } + #endif +} + +static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + { + e = __Pyx_call_type_traverse(o, 1, v, a); + if (e) return e; + } + if (p->name) { + e = (*v)(p->name, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_Enum(PyObject *o) { + PyObject* tmp; + struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o; + tmp = ((PyObject*)p->name); + p->name = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + return 0; +} + +static PyMethodDef __pyx_methods_Enum[] = { + {"__reduce_cython__", (PyCFunction)(void(*)(void))(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__setstate_cython__", (PyCFunction)(void(*)(void))(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; +#if CYTHON_USE_TYPE_SPECS +static PyType_Slot __pyx_type___pyx_MemviewEnum_slots[] = { + {Py_tp_dealloc, (void *)__pyx_tp_dealloc_Enum}, + {Py_tp_repr, (void *)__pyx_MemviewEnum___repr__}, + {Py_tp_traverse, (void *)__pyx_tp_traverse_Enum}, + {Py_tp_clear, (void *)__pyx_tp_clear_Enum}, + {Py_tp_methods, (void *)__pyx_methods_Enum}, + {Py_tp_init, (void *)__pyx_MemviewEnum___init__}, + {Py_tp_new, (void *)__pyx_tp_new_Enum}, + {0, 0}, +}; +static PyType_Spec __pyx_type___pyx_MemviewEnum_spec = { + "matris.graph.cygraph.Enum", + sizeof(struct __pyx_MemviewEnum_obj), + 0, + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, + __pyx_type___pyx_MemviewEnum_slots, +}; +#else + +static PyTypeObject __pyx_type___pyx_MemviewEnum = { + PyVarObject_HEAD_INIT(0, 0) + "matris.graph.cygraph.""Enum", /*tp_name*/ + sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_Enum, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + 0, /*tp_as_async*/ + __pyx_MemviewEnum___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + 0, /*tp_as_sequence*/ + 0, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + 0, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + 0, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + 0, /*tp_doc*/ + __pyx_tp_traverse_Enum, /*tp_traverse*/ + __pyx_tp_clear_Enum, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_Enum, /*tp_methods*/ + 0, /*tp_members*/ + 0, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + #if !CYTHON_USE_TYPE_SPECS + 0, /*tp_dictoffset*/ + #endif + __pyx_MemviewEnum___init__, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_Enum, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if CYTHON_USE_TP_FINALIZE + 0, /*tp_finalize*/ + #else + NULL, /*tp_finalize*/ + #endif + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, /*tp_vectorcall*/ + #endif + #if __PYX_NEED_TP_PRINT_SLOT == 1 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030C0000 + 0, /*tp_watched*/ + #endif + #if PY_VERSION_HEX >= 0x030d00A4 + 0, /*tp_versions_used*/ + #endif + #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, /*tp_pypy_flags*/ + #endif +}; +#endif +static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview; + +static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryview_obj *p; + PyObject *o; + #if CYTHON_COMPILING_IN_LIMITED_API + allocfunc alloc_func = (allocfunc)PyType_GetSlot(t, Py_tp_alloc); + o = alloc_func(t, 0); + #else + if (likely(!__Pyx_PyType_HasFeature(t, Py_TPFLAGS_IS_ABSTRACT))) { + o = (*t->tp_alloc)(t, 0); + } else { + o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_mstate_global->__pyx_empty_tuple, 0); + } + if (unlikely(!o)) return 0; + #endif + p = ((struct __pyx_memoryview_obj *)o); + p->__pyx_vtab = __pyx_vtabptr_memoryview; + p->obj = Py_None; Py_INCREF(Py_None); + p->_size = Py_None; Py_INCREF(Py_None); + p->_array_interface = Py_None; Py_INCREF(Py_None); + p->view.obj = NULL; + if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad; + return o; + bad: + Py_DECREF(o); o = 0; + return NULL; +} + +static void __pyx_tp_dealloc_memoryview(PyObject *o) { + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) { + if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc_memoryview) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); + __pyx_memoryview___dealloc__(o); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->obj); + Py_CLEAR(p->_size); + Py_CLEAR(p->_array_interface); + #if CYTHON_USE_TYPE_SLOTS + (*Py_TYPE(o)->tp_free)(o); + #else + { + freefunc tp_free = (freefunc)PyType_GetSlot(Py_TYPE(o), Py_tp_free); + if (tp_free) tp_free(o); + } + #endif +} + +static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + { + e = __Pyx_call_type_traverse(o, 1, v, a); + if (e) return e; + } + if (p->obj) { + e = (*v)(p->obj, a); if (e) return e; + } + if (p->_size) { + e = (*v)(p->_size, a); if (e) return e; + } + if (p->_array_interface) { + e = (*v)(p->_array_interface, a); if (e) return e; + } + if (p->view.obj) { + e = (*v)(p->view.obj, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear_memoryview(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; + tmp = ((PyObject*)p->obj); + p->obj = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_size); + p->_size = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + tmp = ((PyObject*)p->_array_interface); + p->_array_interface = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + Py_CLEAR(p->view.obj); + return 0; +} + +static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) { + PyObject *r; + PyObject *x = PyLong_FromSsize_t(i); if(!x) return 0; + #if CYTHON_USE_TYPE_SLOTS || (!CYTHON_USE_TYPE_SPECS && __PYX_LIMITED_VERSION_HEX < 0x030A0000) + r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); + #else + r = ((binaryfunc)PyType_GetSlot(Py_TYPE(o), Py_mp_subscript))(o, x); + #endif + Py_DECREF(x); + return r; +} + +static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) { + if (v) { + return __pyx_memoryview___setitem__(o, i, v); + } + else { + __Pyx_TypeName o_type_name; + o_type_name = __Pyx_PyType_GetFullyQualifiedName(Py_TYPE(o)); + PyErr_Format(PyExc_NotImplementedError, + "Subscript deletion not supported by " __Pyx_FMT_TYPENAME, o_type_name); + __Pyx_DECREF_TypeName(o_type_name); + return -1; + } +} + +static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o); +} + +static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) { + return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o); +} + +static PyMethodDef __pyx_methods_memoryview[] = { + {"is_c_contig", (PyCFunction)(void(*)(void))(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_c_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"is_f_contig", (PyCFunction)(void(*)(void))(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_f_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"copy", (PyCFunction)(void(*)(void))(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"copy_fortran", (PyCFunction)(void(*)(void))(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy_fortran, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__reduce_cython__", (PyCFunction)(void(*)(void))(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {"__setstate_cython__", (PyCFunction)(void(*)(void))(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, + {0, 0, 0, 0} +}; + +static struct PyGetSetDef __pyx_getsets_memoryview[] = { + {"T", __pyx_getprop___pyx_memoryview_T, 0, 0, 0}, + {"base", __pyx_getprop___pyx_memoryview_base, 0, 0, 0}, + {"shape", __pyx_getprop___pyx_memoryview_shape, 0, 0, 0}, + {"strides", __pyx_getprop___pyx_memoryview_strides, 0, 0, 0}, + {"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, 0, 0}, + {"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, 0, 0}, + {"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, 0, 0}, + {"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, 0, 0}, + {"size", __pyx_getprop___pyx_memoryview_size, 0, 0, 0}, + {0, 0, 0, 0, 0} +}; +#if CYTHON_USE_TYPE_SPECS +#if !CYTHON_COMPILING_IN_LIMITED_API + +static PyBufferProcs __pyx_tp_as_buffer_memoryview = { + __pyx_memoryview_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; +#endif +static PyType_Slot __pyx_type___pyx_memoryview_slots[] = { + {Py_tp_dealloc, (void *)__pyx_tp_dealloc_memoryview}, + {Py_tp_repr, (void *)__pyx_memoryview___repr__}, + {Py_sq_length, (void *)__pyx_memoryview___len__}, + {Py_sq_item, (void *)__pyx_sq_item_memoryview}, + {Py_mp_length, (void *)__pyx_memoryview___len__}, + {Py_mp_subscript, (void *)__pyx_memoryview___getitem__}, + {Py_mp_ass_subscript, (void *)__pyx_mp_ass_subscript_memoryview}, + {Py_tp_str, (void *)__pyx_memoryview___str__}, + #if defined(Py_bf_getbuffer) + {Py_bf_getbuffer, (void *)__pyx_memoryview_getbuffer}, + #endif + {Py_tp_traverse, (void *)__pyx_tp_traverse_memoryview}, + {Py_tp_clear, (void *)__pyx_tp_clear_memoryview}, + {Py_tp_methods, (void *)__pyx_methods_memoryview}, + {Py_tp_getset, (void *)__pyx_getsets_memoryview}, + {Py_tp_new, (void *)__pyx_tp_new_memoryview}, + {0, 0}, +}; +static PyType_Spec __pyx_type___pyx_memoryview_spec = { + "matris.graph.cygraph.memoryview", + sizeof(struct __pyx_memoryview_obj), + 0, + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, + __pyx_type___pyx_memoryview_slots, +}; +#else + +static PySequenceMethods __pyx_tp_as_sequence_memoryview = { + __pyx_memoryview___len__, /*sq_length*/ + 0, /*sq_concat*/ + 0, /*sq_repeat*/ + __pyx_sq_item_memoryview, /*sq_item*/ + 0, /*sq_slice*/ + 0, /*sq_ass_item*/ + 0, /*sq_ass_slice*/ + 0, /*sq_contains*/ + 0, /*sq_inplace_concat*/ + 0, /*sq_inplace_repeat*/ +}; + +static PyMappingMethods __pyx_tp_as_mapping_memoryview = { + __pyx_memoryview___len__, /*mp_length*/ + __pyx_memoryview___getitem__, /*mp_subscript*/ + __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/ +}; + +static PyBufferProcs __pyx_tp_as_buffer_memoryview = { + __pyx_memoryview_getbuffer, /*bf_getbuffer*/ + 0, /*bf_releasebuffer*/ +}; + +static PyTypeObject __pyx_type___pyx_memoryview = { + PyVarObject_HEAD_INIT(0, 0) + "matris.graph.cygraph.""memoryview", /*tp_name*/ + sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/ + 0, /*tp_itemsize*/ + __pyx_tp_dealloc_memoryview, /*tp_dealloc*/ + #if PY_VERSION_HEX < 0x030800b4 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030800b4 + 0, /*tp_vectorcall_offset*/ + #endif + 0, /*tp_getattr*/ + 0, /*tp_setattr*/ + 0, /*tp_as_async*/ + __pyx_memoryview___repr__, /*tp_repr*/ + 0, /*tp_as_number*/ + &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/ + &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/ + 0, /*tp_hash*/ + 0, /*tp_call*/ + __pyx_memoryview___str__, /*tp_str*/ + 0, /*tp_getattro*/ + 0, /*tp_setattro*/ + &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/ + Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ + 0, /*tp_doc*/ + __pyx_tp_traverse_memoryview, /*tp_traverse*/ + __pyx_tp_clear_memoryview, /*tp_clear*/ + 0, /*tp_richcompare*/ + 0, /*tp_weaklistoffset*/ + 0, /*tp_iter*/ + 0, /*tp_iternext*/ + __pyx_methods_memoryview, /*tp_methods*/ + 0, /*tp_members*/ + __pyx_getsets_memoryview, /*tp_getset*/ + 0, /*tp_base*/ + 0, /*tp_dict*/ + 0, /*tp_descr_get*/ + 0, /*tp_descr_set*/ + #if !CYTHON_USE_TYPE_SPECS + 0, /*tp_dictoffset*/ + #endif + 0, /*tp_init*/ + 0, /*tp_alloc*/ + __pyx_tp_new_memoryview, /*tp_new*/ + 0, /*tp_free*/ + 0, /*tp_is_gc*/ + 0, /*tp_bases*/ + 0, /*tp_mro*/ + 0, /*tp_cache*/ + 0, /*tp_subclasses*/ + 0, /*tp_weaklist*/ + 0, /*tp_del*/ + 0, /*tp_version_tag*/ + #if CYTHON_USE_TP_FINALIZE + 0, /*tp_finalize*/ + #else + NULL, /*tp_finalize*/ + #endif + #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) + 0, /*tp_vectorcall*/ + #endif + #if __PYX_NEED_TP_PRINT_SLOT == 1 + 0, /*tp_print*/ + #endif + #if PY_VERSION_HEX >= 0x030C0000 + 0, /*tp_watched*/ + #endif + #if PY_VERSION_HEX >= 0x030d00A4 + 0, /*tp_versions_used*/ + #endif + #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 + 0, /*tp_pypy_flags*/ + #endif +}; +#endif +static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice; + +static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) { + struct __pyx_memoryviewslice_obj *p; + PyObject *o = __pyx_tp_new_memoryview(t, a, k); + if (unlikely(!o)) return 0; + p = ((struct __pyx_memoryviewslice_obj *)o); + p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice; + p->from_object = Py_None; Py_INCREF(Py_None); + p->from_slice.memview = NULL; + return o; +} + +static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) { + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + #if CYTHON_USE_TP_FINALIZE + if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) { + if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc__memoryviewslice) { + if (PyObject_CallFinalizerFromDealloc(o)) return; + } + } + #endif + PyObject_GC_UnTrack(o); + { + PyObject *etype, *eval, *etb; + PyErr_Fetch(&etype, &eval, &etb); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); + __pyx_memoryviewslice___dealloc__(o); + __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); + PyErr_Restore(etype, eval, etb); + } + Py_CLEAR(p->from_object); + PyObject_GC_Track(o); + __pyx_tp_dealloc_memoryview(o); +} + +static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) { + int e; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e; + if (p->from_object) { + e = (*v)(p->from_object, a); if (e) return e; + } + return 0; +} + +static int __pyx_tp_clear__memoryviewslice(PyObject *o) { + PyObject* tmp; + struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; + __pyx_tp_clear_memoryview(o); + tmp = ((PyObject*)p->from_object); + p->from_object = Py_None; Py_INCREF(Py_None); + Py_XDECREF(tmp); + __PYX_XCLEAR_MEMVIEW(&p->from_slice, 1); 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+ #ifdef __cplusplus +} /* anonymous namespace */ +#endif + +/* PyModInitFuncType */ +#ifndef CYTHON_NO_PYINIT_EXPORT + #define __Pyx_PyMODINIT_FUNC PyMODINIT_FUNC +#else + #ifdef __cplusplus + #define __Pyx_PyMODINIT_FUNC extern "C" PyObject * + #else + #define __Pyx_PyMODINIT_FUNC PyObject * + #endif +#endif + +__Pyx_PyMODINIT_FUNC PyInit_cygraph(void) CYTHON_SMALL_CODE; /*proto*/ +__Pyx_PyMODINIT_FUNC PyInit_cygraph(void) +#if CYTHON_PEP489_MULTI_PHASE_INIT +{ + return PyModuleDef_Init(&__pyx_moduledef); +} +/* ModuleCreationPEP489 */ +#if CYTHON_COMPILING_IN_LIMITED_API && __PYX_LIMITED_VERSION_HEX < 0x03090000 +static PY_INT64_T __Pyx_GetCurrentInterpreterId(void) { + { + PyObject *module = PyImport_ImportModule("_interpreters"); // 3.13+ I think + if (!module) { + PyErr_Clear(); // just try the 3.8-3.12 version + module = PyImport_ImportModule("_xxsubinterpreters"); + if (!module) goto bad; + } + PyObject *current = PyObject_CallMethod(module, "get_current", NULL); + Py_DECREF(module); 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num_kwonly_args : 1; + unsigned int nlocals : 6; + unsigned int flags : 10; + unsigned int first_line : 7; + unsigned int line_table_length : 15; + } __Pyx_PyCode_New_function_description; +/* NewCodeObj.proto */ +static PyObject* __Pyx_PyCode_New( + const __Pyx_PyCode_New_function_description descr, + PyObject * const *varnames, + PyObject *filename, + PyObject *funcname, + const char *line_table, + PyObject *tuple_dedup_map +); + + +static int __Pyx_CreateCodeObjects(__pyx_mstatetype *__pyx_mstate) { + PyObject* tuple_dedup_map = PyDict_New(); + if (unlikely(!tuple_dedup_map)) return -1; + { + const __Pyx_PyCode_New_function_description descr = {6, 0, 0, 34, (unsigned int)(CO_OPTIMIZED|CO_NEWLOCALS), 70, 1017}; + PyObject* const varnames[] = {__pyx_mstate->__pyx_n_u_center_index, __pyx_mstate->__pyx_n_u_n_e, __pyx_mstate->__pyx_n_u_neighbor_index, __pyx_mstate->__pyx_n_u_image, __pyx_mstate->__pyx_n_u_distance, __pyx_mstate->__pyx_n_u_num_atoms, __pyx_mstate->__pyx_n_u_returned, __pyx_mstate->__pyx_n_u_chg_DirectedEdge, __pyx_mstate->__pyx_n_u_chg_Node, __pyx_mstate->__pyx_n_u_chg_UndirectedEdge, __pyx_mstate->__pyx_n_u_image_np, __pyx_mstate->__pyx_n_u_node_neighbors, __pyx_mstate->__pyx_n_u_this_node, __pyx_mstate->__pyx_n_u_this_entry, __pyx_mstate->__pyx_n_u_py_nodes, __pyx_mstate->__pyx_n_u_this_DE, __pyx_mstate->__pyx_n_u_idx, __pyx_mstate->__pyx_n_u_this_py_node, __pyx_mstate->__pyx_n_u_j, __pyx_mstate->__pyx_n_u_directed_edges, __pyx_mstate->__pyx_n_u_k, __pyx_mstate->__pyx_n_u_py_directed_edges_list, __pyx_mstate->__pyx_n_u_py_DE, __pyx_mstate->__pyx_n_u_py_undirected_edges_list, __pyx_mstate->__pyx_n_u_UDE, __pyx_mstate->__pyx_n_u_py_undirected_edge, __pyx_mstate->__pyx_n_u_py_undirected_edges, __pyx_mstate->__pyx_n_u_undirected_edge, __pyx_mstate->__pyx_n_u_this_set, __pyx_mstate->__pyx_n_u_node_index, __pyx_mstate->__pyx_n_u_this_neighbors, __pyx_mstate->__pyx_n_u_this_neighbor_index, __pyx_mstate->__pyx_n_u_replacement, __pyx_mstate->__pyx_n_u_edge_index}; + __pyx_mstate_global->__pyx_codeobj_tab[0] = __Pyx_PyCode_New(descr, varnames, __pyx_mstate->__pyx_kp_u_matris_graph_cygraph_pyx, __pyx_mstate->__pyx_n_u_make_graph, __pyx_k_1HA_e8STTbbccggooppvvxxyy_H_H_I, tuple_dedup_map); if (unlikely(!__pyx_mstate_global->__pyx_codeobj_tab[0])) goto bad; + } + Py_DECREF(tuple_dedup_map); + return 0; + bad: + Py_DECREF(tuple_dedup_map); + return -1; +} +/* #### Code section: init_globals ### */ + +static int __Pyx_InitGlobals(void) { + /* PythonCompatibility.init */ + if (likely(__Pyx_init_co_variables() == 0)); else + +if (unlikely(PyErr_Occurred())) __PYX_ERR(0, 1, __pyx_L1_error) + + /* AssertionsEnabled.init */ + if (likely(__Pyx_init_assertions_enabled() == 0)); else + +if (unlikely(PyErr_Occurred())) __PYX_ERR(0, 1, __pyx_L1_error) + + /* CachedMethodType.init */ + #if CYTHON_COMPILING_IN_LIMITED_API +{ + PyObject *typesModule=NULL; + typesModule = PyImport_ImportModule("types"); + if (typesModule) { + __pyx_mstate_global->__Pyx_CachedMethodType = PyObject_GetAttrString(typesModule, "MethodType"); + Py_DECREF(typesModule); + } +} // error handling follows +#endif + +if (unlikely(PyErr_Occurred())) __PYX_ERR(0, 1, __pyx_L1_error) + + return 0; + __pyx_L1_error:; + return -1; +} +/* #### Code section: cleanup_globals ### */ +/* #### Code section: cleanup_module ### */ +/* #### Code section: main_method ### */ +/* #### Code section: utility_code_pragmas ### */ +#ifdef _MSC_VER +#pragma warning( push ) +/* Warning 4127: conditional expression is constant + * Cython uses constant conditional expressions to allow in inline functions to be optimized at + * compile-time, so this warning is not useful + */ +#pragma warning( disable : 4127 ) +#endif + + + +/* #### Code section: utility_code_def ### */ + +/* --- Runtime support code --- */ +/* Refnanny */ +#if CYTHON_REFNANNY +static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { + PyObject *m = NULL, *p = NULL; + void *r = NULL; + m = PyImport_ImportModule(modname); + if (!m) goto end; + p = PyObject_GetAttrString(m, "RefNannyAPI"); + if (!p) goto end; + r = PyLong_AsVoidPtr(p); +end: + Py_XDECREF(p); + Py_XDECREF(m); + return (__Pyx_RefNannyAPIStruct *)r; +} +#endif + +/* PyErrExceptionMatches */ +#if CYTHON_FAST_THREAD_STATE +static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(tuple); + for (i=0; i= 0x030C00A6 + PyObject *current_exception = tstate->current_exception; + if (unlikely(!current_exception)) return 0; + exc_type = (PyObject*) Py_TYPE(current_exception); + if (exc_type == err) return 1; +#else + exc_type = tstate->curexc_type; + if (exc_type == err) return 1; + if (unlikely(!exc_type)) return 0; +#endif + #if CYTHON_AVOID_BORROWED_REFS + Py_INCREF(exc_type); + #endif + if (unlikely(PyTuple_Check(err))) { + result = __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err); + } else { + result = __Pyx_PyErr_GivenExceptionMatches(exc_type, err); + } + #if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(exc_type); + #endif + return result; +} +#endif + +/* PyErrFetchRestore */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { +#if PY_VERSION_HEX >= 0x030C00A6 + PyObject *tmp_value; + assert(type == NULL || (value != NULL && type == (PyObject*) Py_TYPE(value))); + if (value) { + #if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(((PyBaseExceptionObject*) value)->traceback != tb)) + #endif + PyException_SetTraceback(value, tb); + } + tmp_value = tstate->current_exception; + tstate->current_exception = value; + Py_XDECREF(tmp_value); + Py_XDECREF(type); + Py_XDECREF(tb); +#else + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#endif +} +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { +#if PY_VERSION_HEX >= 0x030C00A6 + PyObject* exc_value; + exc_value = tstate->current_exception; + tstate->current_exception = 0; + *value = exc_value; + *type = NULL; + *tb = NULL; + if (exc_value) { + *type = (PyObject*) Py_TYPE(exc_value); + Py_INCREF(*type); + #if CYTHON_COMPILING_IN_CPYTHON + *tb = ((PyBaseExceptionObject*) exc_value)->traceback; + Py_XINCREF(*tb); + #else + *tb = PyException_GetTraceback(exc_value); + #endif + } +#else + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +#endif +} +#endif + +/* PyObjectGetAttrStr */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); + return PyObject_GetAttr(obj, attr_name); +} +#endif + +/* PyObjectGetAttrStrNoError */ +#if __PYX_LIMITED_VERSION_HEX < 0x030d0000 +static void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) { + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) + __Pyx_PyErr_Clear(); +} +#endif +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) { + PyObject *result; +#if __PYX_LIMITED_VERSION_HEX >= 0x030d0000 + (void) PyObject_GetOptionalAttr(obj, attr_name, &result); + return result; +#else +#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) { + return _PyObject_GenericGetAttrWithDict(obj, attr_name, NULL, 1); + } +#endif + result = __Pyx_PyObject_GetAttrStr(obj, attr_name); + if (unlikely(!result)) { + __Pyx_PyObject_GetAttrStr_ClearAttributeError(); + } + return result; +#endif +} + +/* GetBuiltinName */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name) { + PyObject* result = __Pyx_PyObject_GetAttrStrNoError(__pyx_mstate_global->__pyx_b, name); + if (unlikely(!result) && !PyErr_Occurred()) { + PyErr_Format(PyExc_NameError, + "name '%U' is not defined", name); + } + return result; +} + +/* TupleAndListFromArray */ +#if !CYTHON_COMPILING_IN_CPYTHON && CYTHON_METH_FASTCALL +static CYTHON_INLINE PyObject * +__Pyx_PyTuple_FromArray(PyObject *const *src, Py_ssize_t n) +{ + PyObject *res; + Py_ssize_t i; + if (n <= 0) { + return __Pyx_NewRef(__pyx_mstate_global->__pyx_empty_tuple); + } + res = PyTuple_New(n); + if (unlikely(res == NULL)) return NULL; + for (i = 0; i < n; i++) { + if (unlikely(__Pyx_PyTuple_SET_ITEM(res, i, src[i]) < (0))) { + Py_DECREF(res); + return NULL; + } + Py_INCREF(src[i]); + } + return res; +} +#elif CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE void __Pyx_copy_object_array(PyObject *const *CYTHON_RESTRICT src, PyObject** CYTHON_RESTRICT dest, Py_ssize_t length) { + PyObject *v; + Py_ssize_t i; + for (i = 0; i < length; i++) { + v = dest[i] = src[i]; + Py_INCREF(v); + } +} +static CYTHON_INLINE PyObject * +__Pyx_PyTuple_FromArray(PyObject *const *src, Py_ssize_t n) +{ + PyObject *res; + if (n <= 0) { + return __Pyx_NewRef(__pyx_mstate_global->__pyx_empty_tuple); + } + res = PyTuple_New(n); + if (unlikely(res == NULL)) return NULL; + __Pyx_copy_object_array(src, ((PyTupleObject*)res)->ob_item, n); + return res; +} +static CYTHON_INLINE PyObject * +__Pyx_PyList_FromArray(PyObject *const *src, Py_ssize_t n) +{ + PyObject *res; + if (n <= 0) { + return PyList_New(0); + } + res = PyList_New(n); + if (unlikely(res == NULL)) return NULL; + __Pyx_copy_object_array(src, ((PyListObject*)res)->ob_item, n); + return res; +} +#endif + +/* BytesEquals */ +static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_COMPILING_IN_GRAAL ||\ + !(CYTHON_ASSUME_SAFE_SIZE && CYTHON_ASSUME_SAFE_MACROS) + return PyObject_RichCompareBool(s1, s2, equals); +#else + if (s1 == s2) { + return (equals == Py_EQ); + } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { + const char *ps1, *ps2; + Py_ssize_t length = PyBytes_GET_SIZE(s1); + if (length != PyBytes_GET_SIZE(s2)) + return (equals == Py_NE); + ps1 = PyBytes_AS_STRING(s1); + ps2 = PyBytes_AS_STRING(s2); + if (ps1[0] != ps2[0]) { + return (equals == Py_NE); + } else if (length == 1) { + return (equals == Py_EQ); + } else { + int result; +#if CYTHON_USE_UNICODE_INTERNALS && (PY_VERSION_HEX < 0x030B0000) + Py_hash_t hash1, hash2; + hash1 = ((PyBytesObject*)s1)->ob_shash; + hash2 = ((PyBytesObject*)s2)->ob_shash; + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + return (equals == Py_NE); + } +#endif + result = memcmp(ps1, ps2, (size_t)length); + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { + return (equals == Py_NE); + } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { + return (equals == Py_NE); + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +#endif +} + +/* UnicodeEquals */ +static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { +#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_COMPILING_IN_GRAAL + return PyObject_RichCompareBool(s1, s2, equals); +#else + int s1_is_unicode, s2_is_unicode; + if (s1 == s2) { + goto return_eq; + } + s1_is_unicode = PyUnicode_CheckExact(s1); + s2_is_unicode = PyUnicode_CheckExact(s2); + if (s1_is_unicode & s2_is_unicode) { + Py_ssize_t length, length2; + int kind; + void *data1, *data2; + #if !CYTHON_COMPILING_IN_LIMITED_API + if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0)) + return -1; + #endif + length = __Pyx_PyUnicode_GET_LENGTH(s1); + #if !CYTHON_ASSUME_SAFE_SIZE + if (unlikely(length < 0)) return -1; + #endif + length2 = __Pyx_PyUnicode_GET_LENGTH(s2); + #if !CYTHON_ASSUME_SAFE_SIZE + if (unlikely(length2 < 0)) return -1; + #endif + if (length != length2) { + goto return_ne; + } +#if CYTHON_USE_UNICODE_INTERNALS + { + Py_hash_t hash1, hash2; + hash1 = ((PyASCIIObject*)s1)->hash; + hash2 = ((PyASCIIObject*)s2)->hash; + if (hash1 != hash2 && hash1 != -1 && hash2 != -1) { + goto return_ne; + } + } +#endif + kind = __Pyx_PyUnicode_KIND(s1); + if (kind != __Pyx_PyUnicode_KIND(s2)) { + goto return_ne; + } + data1 = __Pyx_PyUnicode_DATA(s1); + data2 = __Pyx_PyUnicode_DATA(s2); + if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) { + goto return_ne; + } else if (length == 1) { + goto return_eq; + } else { + int result = memcmp(data1, data2, (size_t)(length * kind)); + return (equals == Py_EQ) ? (result == 0) : (result != 0); + } + } else if ((s1 == Py_None) & s2_is_unicode) { + goto return_ne; + } else if ((s2 == Py_None) & s1_is_unicode) { + goto return_ne; + } else { + int result; + PyObject* py_result = PyObject_RichCompare(s1, s2, equals); + if (!py_result) + return -1; + result = __Pyx_PyObject_IsTrue(py_result); + Py_DECREF(py_result); + return result; + } +return_eq: + return (equals == Py_EQ); +return_ne: + return (equals == Py_NE); +#endif +} + +/* fastcall */ +#if CYTHON_METH_FASTCALL +static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s) +{ + Py_ssize_t i, n = __Pyx_PyTuple_GET_SIZE(kwnames); + #if !CYTHON_ASSUME_SAFE_SIZE + if (unlikely(n == -1)) return NULL; + #endif + for (i = 0; i < n; i++) + { + PyObject *namei = __Pyx_PyTuple_GET_ITEM(kwnames, i); + #if !CYTHON_ASSUME_SAFE_MACROS + if (unlikely(!namei)) return NULL; + #endif + if (s == namei) return kwvalues[i]; + } + for (i = 0; i < n; i++) + { + PyObject *namei = __Pyx_PyTuple_GET_ITEM(kwnames, i); + #if !CYTHON_ASSUME_SAFE_MACROS + if (unlikely(!namei)) return NULL; + #endif + int eq = __Pyx_PyUnicode_Equals(s, namei, Py_EQ); + if (unlikely(eq != 0)) { + if (unlikely(eq < 0)) return NULL; + return kwvalues[i]; + } + } + return NULL; +} +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030d0000 || CYTHON_COMPILING_IN_LIMITED_API +CYTHON_UNUSED static PyObject *__Pyx_KwargsAsDict_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues) { + Py_ssize_t i, nkwargs; + PyObject *dict; +#if !CYTHON_ASSUME_SAFE_SIZE + nkwargs = PyTuple_Size(kwnames); + if (unlikely(nkwargs < 0)) return NULL; +#else + nkwargs = PyTuple_GET_SIZE(kwnames); +#endif + dict = PyDict_New(); + if (unlikely(!dict)) + return NULL; + for (i=0; irecursion_depth; + Py_DECREF(f); + --tstate->recursion_depth; + return result; +} +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject *const *args, Py_ssize_t nargs, PyObject *kwargs) { + PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); + PyObject *globals = PyFunction_GET_GLOBALS(func); + PyObject *argdefs = PyFunction_GET_DEFAULTS(func); + PyObject *closure; + PyObject *kwdefs; + PyObject *kwtuple, **k; + PyObject **d; + Py_ssize_t nd; + Py_ssize_t nk; + PyObject *result; + assert(kwargs == NULL || PyDict_Check(kwargs)); + nk = kwargs ? PyDict_Size(kwargs) : 0; + if (unlikely(Py_EnterRecursiveCall(" while calling a Python object"))) { + return NULL; + } + if ( + co->co_kwonlyargcount == 0 && + likely(kwargs == NULL || nk == 0) && + co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { + if (argdefs == NULL && co->co_argcount == nargs) { + result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); + goto done; + } + else if (nargs == 0 && argdefs != NULL + && co->co_argcount == Py_SIZE(argdefs)) { + /* function called with no arguments, but all parameters have + a default value: use default values as arguments .*/ + args = &PyTuple_GET_ITEM(argdefs, 0); + result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); + goto done; + } + } + if (kwargs != NULL) { + Py_ssize_t pos, i; + kwtuple = PyTuple_New(2 * nk); + if (kwtuple == NULL) { + result = NULL; + goto done; + } + k = &PyTuple_GET_ITEM(kwtuple, 0); + pos = i = 0; + while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { + Py_INCREF(k[i]); + Py_INCREF(k[i+1]); + i += 2; + } + nk = i / 2; + } + else { + kwtuple = NULL; + k = NULL; + } + closure = PyFunction_GET_CLOSURE(func); + kwdefs = PyFunction_GET_KW_DEFAULTS(func); + if (argdefs != NULL) { + d = &PyTuple_GET_ITEM(argdefs, 0); + nd = Py_SIZE(argdefs); + } + else { + d = NULL; + nd = 0; + } + result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, + args, (int)nargs, + k, (int)nk, + d, (int)nd, kwdefs, closure); + Py_XDECREF(kwtuple); +done: + Py_LeaveRecursiveCall(); + return result; +} +#endif + +/* PyObjectCall */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = Py_TYPE(func)->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); + if (unlikely(Py_EnterRecursiveCall(" while calling a Python object"))) + return NULL; + result = (*call)(func, arg, kw); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectCallMethO */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { + PyObject *self, *result; + PyCFunction cfunc; + cfunc = __Pyx_CyOrPyCFunction_GET_FUNCTION(func); + self = __Pyx_CyOrPyCFunction_GET_SELF(func); + if (unlikely(Py_EnterRecursiveCall(" while calling a Python object"))) + return NULL; + result = cfunc(self, arg); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectFastCall */ +#if PY_VERSION_HEX < 0x03090000 || CYTHON_COMPILING_IN_LIMITED_API +static PyObject* __Pyx_PyObject_FastCall_fallback(PyObject *func, PyObject * const*args, size_t nargs, PyObject *kwargs) { + PyObject *argstuple; + PyObject *result = 0; + size_t i; + argstuple = PyTuple_New((Py_ssize_t)nargs); + if (unlikely(!argstuple)) return NULL; + for (i = 0; i < nargs; i++) { + Py_INCREF(args[i]); + if (__Pyx_PyTuple_SET_ITEM(argstuple, (Py_ssize_t)i, args[i]) != (0)) goto bad; + } + result = __Pyx_PyObject_Call(func, argstuple, kwargs); + bad: + Py_DECREF(argstuple); + return result; +} +#endif +#if CYTHON_VECTORCALL && !CYTHON_COMPILING_IN_LIMITED_API + #if PY_VERSION_HEX < 0x03090000 + #define __Pyx_PyVectorcall_Function(callable) _PyVectorcall_Function(callable) + #elif CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE vectorcallfunc __Pyx_PyVectorcall_Function(PyObject *callable) { + PyTypeObject *tp = Py_TYPE(callable); + #if defined(__Pyx_CyFunction_USED) + if (__Pyx_CyFunction_CheckExact(callable)) { + return __Pyx_CyFunction_func_vectorcall(callable); + } + #endif + if (!PyType_HasFeature(tp, Py_TPFLAGS_HAVE_VECTORCALL)) { + return NULL; + } + assert(PyCallable_Check(callable)); + Py_ssize_t offset = tp->tp_vectorcall_offset; + assert(offset > 0); + vectorcallfunc ptr; + memcpy(&ptr, (char *) callable + offset, sizeof(ptr)); + return ptr; +} + #else + #define __Pyx_PyVectorcall_Function(callable) PyVectorcall_Function(callable) + #endif +#endif +static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject *const *args, size_t _nargs, PyObject *kwargs) { + Py_ssize_t nargs = __Pyx_PyVectorcall_NARGS(_nargs); +#if CYTHON_COMPILING_IN_CPYTHON + if (nargs == 0 && kwargs == NULL) { + if (__Pyx_CyOrPyCFunction_Check(func) && likely( __Pyx_CyOrPyCFunction_GET_FLAGS(func) & METH_NOARGS)) + return __Pyx_PyObject_CallMethO(func, NULL); + } + else if (nargs == 1 && kwargs == NULL) { + if (__Pyx_CyOrPyCFunction_Check(func) && likely( __Pyx_CyOrPyCFunction_GET_FLAGS(func) & METH_O)) + return __Pyx_PyObject_CallMethO(func, args[0]); + } +#endif + #if PY_VERSION_HEX < 0x030800B1 + #if CYTHON_FAST_PYCCALL + if (PyCFunction_Check(func)) { + if (kwargs) { + return _PyCFunction_FastCallDict(func, args, nargs, kwargs); + } else { + return _PyCFunction_FastCallKeywords(func, args, nargs, NULL); + } + } + if (!kwargs && __Pyx_IS_TYPE(func, &PyMethodDescr_Type)) { + return _PyMethodDescr_FastCallKeywords(func, args, nargs, NULL); + } + #endif + #if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs); + } + #endif + #endif + if (kwargs == NULL) { + #if CYTHON_VECTORCALL && !CYTHON_COMPILING_IN_LIMITED_API + vectorcallfunc f = __Pyx_PyVectorcall_Function(func); + if (f) { + return f(func, args, _nargs, NULL); + } + #elif defined(__Pyx_CyFunction_USED) && CYTHON_BACKPORT_VECTORCALL + if (__Pyx_CyFunction_CheckExact(func)) { + __pyx_vectorcallfunc f = __Pyx_CyFunction_func_vectorcall(func); + if (f) return f(func, args, _nargs, NULL); + } + #elif CYTHON_COMPILING_IN_LIMITED_API && CYTHON_VECTORCALL + return PyObject_Vectorcall(func, args, _nargs, NULL); + #endif + } + if (nargs == 0) { + return __Pyx_PyObject_Call(func, __pyx_mstate_global->__pyx_empty_tuple, kwargs); + } + #if PY_VERSION_HEX >= 0x03090000 && !CYTHON_COMPILING_IN_LIMITED_API + return PyObject_VectorcallDict(func, args, (size_t)nargs, kwargs); + #else + return __Pyx_PyObject_FastCall_fallback(func, args, (size_t)nargs, kwargs); + #endif +} + +/* UnpackUnboundCMethod */ +#if CYTHON_COMPILING_IN_LIMITED_API && __PYX_LIMITED_VERSION_HEX < 0x030C0000 +static PyObject *__Pyx_SelflessCall(PyObject *method, PyObject *args, PyObject *kwargs) { + PyObject *result; + PyObject *selfless_args = PyTuple_GetSlice(args, 1, PyTuple_Size(args)); + if (unlikely(!selfless_args)) return NULL; + result = PyObject_Call(method, selfless_args, kwargs); + Py_DECREF(selfless_args); + return result; +} +#elif CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x03090000 +static PyObject *__Pyx_SelflessCall(PyObject *method, PyObject **args, Py_ssize_t nargs, PyObject *kwnames) { + return _PyObject_Vectorcall + (method, args ? args+1 : NULL, nargs ? nargs-1 : 0, kwnames); +} +#else +static PyObject *__Pyx_SelflessCall(PyObject *method, PyObject *const *args, Py_ssize_t nargs, PyObject *kwnames) { + return +#if PY_VERSION_HEX < 0x03090000 + _PyObject_Vectorcall +#else + PyObject_Vectorcall +#endif + (method, args ? args+1 : NULL, nargs ? (size_t) nargs-1 : 0, kwnames); +} +#endif +static PyMethodDef __Pyx_UnboundCMethod_Def = { + "CythonUnboundCMethod", + __PYX_REINTERPRET_FUNCION(PyCFunction, __Pyx_SelflessCall), +#if CYTHON_COMPILING_IN_LIMITED_API && __PYX_LIMITED_VERSION_HEX < 0x030C0000 + METH_VARARGS | METH_KEYWORDS, +#else + METH_FASTCALL | METH_KEYWORDS, +#endif + NULL +}; +static int __Pyx_TryUnpackUnboundCMethod(__Pyx_CachedCFunction* target) { + PyObject *method, *result=NULL; + method = __Pyx_PyObject_GetAttrStr(target->type, *target->method_name); + if (unlikely(!method)) + return -1; + result = method; +#if CYTHON_COMPILING_IN_CPYTHON + if (likely(__Pyx_TypeCheck(method, &PyMethodDescr_Type))) + { + PyMethodDescrObject *descr = (PyMethodDescrObject*) method; + target->func = descr->d_method->ml_meth; + target->flag = descr->d_method->ml_flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_STACKLESS); + } else +#endif +#if CYTHON_COMPILING_IN_PYPY +#else + if (PyCFunction_Check(method)) +#endif + { + PyObject *self; + int self_found; +#if CYTHON_COMPILING_IN_LIMITED_API || CYTHON_COMPILING_IN_PYPY + self = PyObject_GetAttrString(method, "__self__"); + if (!self) { + PyErr_Clear(); + } +#else + self = PyCFunction_GET_SELF(method); +#endif + self_found = (self && self != Py_None); +#if CYTHON_COMPILING_IN_LIMITED_API || CYTHON_COMPILING_IN_PYPY + Py_XDECREF(self); +#endif + if (self_found) { + PyObject *unbound_method = PyCFunction_New(&__Pyx_UnboundCMethod_Def, method); + if (unlikely(!unbound_method)) return -1; + Py_DECREF(method); + result = unbound_method; + } + } +#if !CYTHON_COMPILING_IN_CPYTHON_FREETHREADING + if (unlikely(target->method)) { + Py_DECREF(result); + } else +#endif + target->method = result; + return 0; +} + +/* CallUnboundCMethod2 */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject *__Pyx_CallUnboundCMethod2(__Pyx_CachedCFunction *cfunc, PyObject *self, PyObject *arg1, PyObject *arg2) { + int was_initialized = __Pyx_CachedCFunction_GetAndSetInitializing(cfunc); + if (likely(was_initialized == 2 && cfunc->func)) { + PyObject *args[2] = {arg1, arg2}; + if (cfunc->flag == METH_FASTCALL) { + return __Pyx_CallCFunctionFast(cfunc, self, args, 2); + } + if (cfunc->flag == (METH_FASTCALL | METH_KEYWORDS)) + return __Pyx_CallCFunctionFastWithKeywords(cfunc, self, args, 2, NULL); + } +#if CYTHON_COMPILING_IN_CPYTHON_FREETHREADING + else if (unlikely(was_initialized == 1)) { + __Pyx_CachedCFunction tmp_cfunc = { +#ifndef __cplusplus + 0 +#endif + }; + tmp_cfunc.type = cfunc->type; + tmp_cfunc.method_name = cfunc->method_name; + return __Pyx__CallUnboundCMethod2(&tmp_cfunc, self, arg1, arg2); + } +#endif + PyObject *result = __Pyx__CallUnboundCMethod2(cfunc, self, arg1, arg2); + __Pyx_CachedCFunction_SetFinishedInitializing(cfunc); + return result; +} +#endif +static PyObject* __Pyx__CallUnboundCMethod2(__Pyx_CachedCFunction* cfunc, PyObject* self, PyObject* arg1, PyObject* arg2){ + if (unlikely(!cfunc->func && !cfunc->method) && unlikely(__Pyx_TryUnpackUnboundCMethod(cfunc) < 0)) return NULL; +#if CYTHON_COMPILING_IN_CPYTHON + if (cfunc->func && (cfunc->flag & METH_VARARGS)) { + PyObject *result = NULL; + PyObject *args = PyTuple_New(2); + if (unlikely(!args)) return NULL; + Py_INCREF(arg1); + PyTuple_SET_ITEM(args, 0, arg1); + Py_INCREF(arg2); + PyTuple_SET_ITEM(args, 1, arg2); + if (cfunc->flag & METH_KEYWORDS) + result = __Pyx_CallCFunctionWithKeywords(cfunc, self, args, NULL); + else + result = __Pyx_CallCFunction(cfunc, self, args); + Py_DECREF(args); + return result; + } +#endif + { + PyObject *args[4] = {NULL, self, arg1, arg2}; + return __Pyx_PyObject_FastCall(cfunc->method, args+1, 3 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET); + } +} + +/* ParseKeywords */ +static int __Pyx_ValidateDuplicatePosArgs( + PyObject *kwds, + PyObject ** const argnames[], + PyObject ** const *first_kw_arg, + const char* function_name) +{ + PyObject ** const *name = argnames; + while (name != first_kw_arg) { + PyObject *key = **name; + int found = PyDict_Contains(kwds, key); + if (unlikely(found)) { + if (found == 1) __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; + } + name++; + } + return 0; +bad: + return -1; +} +#if CYTHON_USE_UNICODE_INTERNALS +static CYTHON_INLINE int __Pyx_UnicodeKeywordsEqual(PyObject *s1, PyObject *s2) { + int kind; + Py_ssize_t len = PyUnicode_GET_LENGTH(s1); + if (len != PyUnicode_GET_LENGTH(s2)) return 0; + kind = PyUnicode_KIND(s1); + if (kind != PyUnicode_KIND(s2)) return 0; + const void *data1 = PyUnicode_DATA(s1); + const void *data2 = PyUnicode_DATA(s2); + return (memcmp(data1, data2, (size_t) len * (size_t) kind) == 0); +} +#endif +static int __Pyx_MatchKeywordArg_str( + PyObject *key, + PyObject ** const argnames[], + PyObject ** const *first_kw_arg, + size_t *index_found, + const char *function_name) +{ + PyObject ** const *name; + #if CYTHON_USE_UNICODE_INTERNALS + Py_hash_t key_hash = ((PyASCIIObject*)key)->hash; + if (unlikely(key_hash == -1)) { + key_hash = PyObject_Hash(key); + if (unlikely(key_hash == -1)) + goto bad; + } + #endif + name = first_kw_arg; + while (*name) { + PyObject *name_str = **name; + #if CYTHON_USE_UNICODE_INTERNALS + if (key_hash == ((PyASCIIObject*)name_str)->hash && __Pyx_UnicodeKeywordsEqual(name_str, key)) { + *index_found = (size_t) (name - argnames); + return 1; + } + #else + #if CYTHON_ASSUME_SAFE_SIZE + if (PyUnicode_GET_LENGTH(name_str) == PyUnicode_GET_LENGTH(key)) + #endif + { + int cmp = PyUnicode_Compare(name_str, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + *index_found = (size_t) (name - argnames); + return 1; + } + } + #endif + name++; + } + name = argnames; + while (name != first_kw_arg) { + PyObject *name_str = **name; + #if CYTHON_USE_UNICODE_INTERNALS + if (unlikely(key_hash == ((PyASCIIObject*)name_str)->hash)) { + if (__Pyx_UnicodeKeywordsEqual(name_str, key)) + goto arg_passed_twice; + } + #else + #if CYTHON_ASSUME_SAFE_SIZE + if (PyUnicode_GET_LENGTH(name_str) == PyUnicode_GET_LENGTH(key)) + #endif + { + if (unlikely(name_str == key)) goto arg_passed_twice; + int cmp = PyUnicode_Compare(name_str, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + } + #endif + name++; + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +bad: + return -1; +} +static int __Pyx_MatchKeywordArg_nostr( + PyObject *key, + PyObject ** const argnames[], + PyObject ** const *first_kw_arg, + size_t *index_found, + const char *function_name) +{ + PyObject ** const *name; + if (unlikely(!PyUnicode_Check(key))) goto invalid_keyword_type; + name = first_kw_arg; + while (*name) { + int cmp = PyObject_RichCompareBool(**name, key, Py_EQ); + if (cmp == 1) { + *index_found = (size_t) (name - argnames); + return 1; + } + if (unlikely(cmp == -1)) goto bad; + name++; + } + name = argnames; + while (name != first_kw_arg) { + int cmp = PyObject_RichCompareBool(**name, key, Py_EQ); + if (unlikely(cmp != 0)) { + if (cmp == 1) goto arg_passed_twice; + else goto bad; + } + name++; + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +bad: + return -1; +} +static CYTHON_INLINE int __Pyx_MatchKeywordArg( + PyObject *key, + PyObject ** const argnames[], + PyObject ** const *first_kw_arg, + size_t *index_found, + const char *function_name) +{ + return likely(PyUnicode_CheckExact(key)) ? + __Pyx_MatchKeywordArg_str(key, argnames, first_kw_arg, index_found, function_name) : + __Pyx_MatchKeywordArg_nostr(key, argnames, first_kw_arg, index_found, function_name); +} +static void __Pyx_RejectUnknownKeyword( + PyObject *kwds, + PyObject ** const argnames[], + PyObject ** const *first_kw_arg, + const char *function_name) +{ + Py_ssize_t pos = 0; + PyObject *key = NULL; + __Pyx_BEGIN_CRITICAL_SECTION(kwds); + while (PyDict_Next(kwds, &pos, &key, NULL)) { + PyObject** const *name = first_kw_arg; + while (*name && (**name != key)) name++; + if (!*name) { + #if CYTHON_AVOID_BORROWED_REFS + Py_INCREF(key); + #endif + size_t index_found = 0; + int cmp = __Pyx_MatchKeywordArg(key, argnames, first_kw_arg, &index_found, function_name); + if (cmp != 1) { + if (cmp == 0) { + PyErr_Format(PyExc_TypeError, + "%s() got an unexpected keyword argument '%U'", + function_name, key); + } + #if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(key); + #endif + break; + } + #if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(key); + #endif + } + } + __Pyx_END_CRITICAL_SECTION(); + assert(PyErr_Occurred()); +} +static int __Pyx_ParseKeywordDict( + PyObject *kwds, + PyObject ** const argnames[], + PyObject *values[], + Py_ssize_t num_pos_args, + Py_ssize_t num_kwargs, + const char* function_name, + int ignore_unknown_kwargs) +{ + PyObject** const *name; + PyObject** const *first_kw_arg = argnames + num_pos_args; + Py_ssize_t extracted = 0; +#if !CYTHON_COMPILING_IN_PYPY || defined(PyArg_ValidateKeywordArguments) + if (unlikely(!PyArg_ValidateKeywordArguments(kwds))) return -1; +#endif + name = first_kw_arg; + while (*name && num_kwargs > extracted) { + PyObject * key = **name; + PyObject *value; + int found = 0; + #if __PYX_LIMITED_VERSION_HEX >= 0x030d0000 + found = PyDict_GetItemRef(kwds, key, &value); + #else + value = PyDict_GetItemWithError(kwds, key); + if (value) { + Py_INCREF(value); + found = 1; + } else { + if (unlikely(PyErr_Occurred())) goto bad; + } + #endif + if (found) { + if (unlikely(found < 0)) goto bad; + values[name-argnames] = value; + extracted++; + } + name++; + } + if (num_kwargs > extracted) { + if (ignore_unknown_kwargs) { + if (unlikely(__Pyx_ValidateDuplicatePosArgs(kwds, argnames, first_kw_arg, function_name) == -1)) + goto bad; + } else { + __Pyx_RejectUnknownKeyword(kwds, argnames, first_kw_arg, function_name); + goto bad; + } + } + return 0; +bad: + return -1; +} +static int __Pyx_ParseKeywordDictToDict( + PyObject *kwds, + PyObject ** const argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject** const *name; + PyObject** const *first_kw_arg = argnames + num_pos_args; + Py_ssize_t len; +#if !CYTHON_COMPILING_IN_PYPY || defined(PyArg_ValidateKeywordArguments) + if (unlikely(!PyArg_ValidateKeywordArguments(kwds))) return -1; +#endif + if (PyDict_Update(kwds2, kwds) < 0) goto bad; + name = first_kw_arg; + while (*name) { + PyObject *key = **name; + PyObject *value; +#if !CYTHON_COMPILING_IN_LIMITED_API && (PY_VERSION_HEX >= 0x030d00A2 || defined(PyDict_Pop)) + int found = PyDict_Pop(kwds2, key, &value); + if (found) { + if (unlikely(found < 0)) goto bad; + values[name-argnames] = value; + } +#elif __PYX_LIMITED_VERSION_HEX >= 0x030d0000 + int found = PyDict_GetItemRef(kwds2, key, &value); + if (found) { + if (unlikely(found < 0)) goto bad; + values[name-argnames] = value; + if (unlikely(PyDict_DelItem(kwds2, key) < 0)) goto bad; + } +#else + #if CYTHON_COMPILING_IN_CPYTHON + value = _PyDict_Pop(kwds2, key, kwds2); + #else + value = __Pyx_CallUnboundCMethod2(&__pyx_mstate_global->__pyx_umethod_PyDict_Type_pop, kwds2, key, kwds2); + #endif + if (value == kwds2) { + Py_DECREF(value); + } else { + if (unlikely(!value)) goto bad; + values[name-argnames] = value; + } +#endif + name++; + } + len = PyDict_Size(kwds2); + if (len > 0) { + return __Pyx_ValidateDuplicatePosArgs(kwds, argnames, first_kw_arg, function_name); + } else if (unlikely(len == -1)) { + goto bad; + } + return 0; +bad: + return -1; +} +static int __Pyx_ParseKeywordsTuple( + PyObject *kwds, + PyObject * const *kwvalues, + PyObject ** const argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + Py_ssize_t num_kwargs, + const char* function_name, + int ignore_unknown_kwargs) +{ + PyObject *key = NULL; + PyObject** const * name; + PyObject** const *first_kw_arg = argnames + num_pos_args; + for (Py_ssize_t pos = 0; pos < num_kwargs; pos++) { +#if CYTHON_AVOID_BORROWED_REFS + key = __Pyx_PySequence_ITEM(kwds, pos); +#else + key = __Pyx_PyTuple_GET_ITEM(kwds, pos); +#endif +#if !CYTHON_ASSUME_SAFE_MACROS + if (unlikely(!key)) goto bad; +#endif + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + PyObject *value = kwvalues[pos]; + values[name-argnames] = __Pyx_NewRef(value); + } else { + size_t index_found = 0; + int cmp = __Pyx_MatchKeywordArg(key, argnames, first_kw_arg, &index_found, function_name); + if (cmp == 1) { + PyObject *value = kwvalues[pos]; + values[index_found] = __Pyx_NewRef(value); + } else { + if (unlikely(cmp == -1)) goto bad; + if (kwds2) { + PyObject *value = kwvalues[pos]; + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else if (!ignore_unknown_kwargs) { + goto invalid_keyword; + } + } + } + #if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(key); + key = NULL; + #endif + } + return 0; +invalid_keyword: + PyErr_Format(PyExc_TypeError, + "%s() got an unexpected keyword argument '%U'", + function_name, key); + goto bad; +bad: + #if CYTHON_AVOID_BORROWED_REFS + Py_XDECREF(key); + #endif + return -1; +} +static int __Pyx_ParseKeywords( + PyObject *kwds, + PyObject * const *kwvalues, + PyObject ** const argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + Py_ssize_t num_kwargs, + const char* function_name, + int ignore_unknown_kwargs) +{ + if (CYTHON_METH_FASTCALL && likely(PyTuple_Check(kwds))) + return __Pyx_ParseKeywordsTuple(kwds, kwvalues, argnames, kwds2, values, num_pos_args, num_kwargs, function_name, ignore_unknown_kwargs); + else if (kwds2) + return __Pyx_ParseKeywordDictToDict(kwds, argnames, kwds2, values, num_pos_args, function_name); + else + return __Pyx_ParseKeywordDict(kwds, argnames, values, num_pos_args, num_kwargs, function_name, ignore_unknown_kwargs); +} + +/* RaiseArgTupleInvalid */ +static void __Pyx_RaiseArgtupleInvalid( + const char* func_name, + int exact, + Py_ssize_t num_min, + Py_ssize_t num_max, + Py_ssize_t num_found) +{ + Py_ssize_t num_expected; + const char *more_or_less; + if (num_found < num_min) { + num_expected = num_min; + more_or_less = "at least"; + } else { + num_expected = num_max; + more_or_less = "at most"; + } + if (exact) { + more_or_less = "exactly"; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", + func_name, more_or_less, num_expected, + (num_expected == 1) ? "" : "s", num_found); +} + +/* ArgTypeTest */ +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) +{ + __Pyx_TypeName type_name; + __Pyx_TypeName obj_type_name; + PyObject *extra_info = __pyx_mstate_global->__pyx_empty_unicode; + int from_annotation_subclass = 0; + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + else if (!exact) { + if (likely(__Pyx_TypeCheck(obj, type))) return 1; + } else if (exact == 2) { + if (__Pyx_TypeCheck(obj, type)) { + from_annotation_subclass = 1; + extra_info = __pyx_mstate_global->__pyx_kp_u_Note_that_Cython_is_deliberately; + } + } + type_name = __Pyx_PyType_GetFullyQualifiedName(type); + obj_type_name = __Pyx_PyType_GetFullyQualifiedName(Py_TYPE(obj)); + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected " __Pyx_FMT_TYPENAME + ", got " __Pyx_FMT_TYPENAME ")" +#if __PYX_LIMITED_VERSION_HEX < 0x030C0000 + "%s%U" +#endif + , name, type_name, obj_type_name +#if __PYX_LIMITED_VERSION_HEX < 0x030C0000 + , (from_annotation_subclass ? ". " : ""), extra_info +#endif + ); +#if __PYX_LIMITED_VERSION_HEX >= 0x030C0000 + if (exact == 2 && from_annotation_subclass) { + PyObject *res; + PyObject *vargs[2]; + vargs[0] = PyErr_GetRaisedException(); + vargs[1] = extra_info; + res = PyObject_VectorcallMethod(__pyx_mstate_global->__pyx_kp_u_add_note, vargs, 2, NULL); + Py_XDECREF(res); + PyErr_SetRaisedException(vargs[0]); + } +#endif + __Pyx_DECREF_TypeName(type_name); + __Pyx_DECREF_TypeName(obj_type_name); + return 0; +} + +/* RaiseException */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + int is_subclass = PyObject_IsSubclass(instance_class, type); + if (!is_subclass) { + instance_class = NULL; + } else if (unlikely(is_subclass == -1)) { + goto bad; + } else { + type = instance_class; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } + if (cause) { + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { +#if PY_VERSION_HEX >= 0x030C00A6 + PyException_SetTraceback(value, tb); +#elif CYTHON_FAST_THREAD_STATE + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#else + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} + +/* PyObjectFastCallMethod */ +#if !CYTHON_VECTORCALL || PY_VERSION_HEX < 0x03090000 +static PyObject *__Pyx_PyObject_FastCallMethod(PyObject *name, PyObject *const *args, size_t nargsf) { + PyObject *result; + PyObject *attr = PyObject_GetAttr(args[0], name); + if (unlikely(!attr)) + return NULL; + result = __Pyx_PyObject_FastCall(attr, args+1, nargsf - 1); + Py_DECREF(attr); + return result; +} +#endif + +/* RaiseUnexpectedTypeError */ +static int +__Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj) +{ + __Pyx_TypeName obj_type_name = __Pyx_PyType_GetFullyQualifiedName(Py_TYPE(obj)); + PyErr_Format(PyExc_TypeError, "Expected %s, got " __Pyx_FMT_TYPENAME, + expected, obj_type_name); + __Pyx_DECREF_TypeName(obj_type_name); + return 0; +} + +/* CIntToDigits */ +static const char DIGIT_PAIRS_10[2*10*10+1] = { + "00010203040506070809" + "10111213141516171819" + "20212223242526272829" + "30313233343536373839" + "40414243444546474849" + "50515253545556575859" + "60616263646566676869" + "70717273747576777879" + "80818283848586878889" + "90919293949596979899" +}; +static const char DIGIT_PAIRS_8[2*8*8+1] = { + "0001020304050607" + "1011121314151617" + "2021222324252627" + "3031323334353637" + "4041424344454647" + "5051525354555657" + "6061626364656667" + "7071727374757677" +}; +static const char DIGITS_HEX[2*16+1] = { + "0123456789abcdef" + "0123456789ABCDEF" +}; + +/* BuildPyUnicode */ +static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, const char* chars, int clength, + int prepend_sign, char padding_char) { + PyObject *uval; + Py_ssize_t uoffset = ulength - clength; +#if CYTHON_USE_UNICODE_INTERNALS + Py_ssize_t i; + void *udata; + uval = PyUnicode_New(ulength, 127); + if (unlikely(!uval)) return NULL; + udata = PyUnicode_DATA(uval); + if (uoffset > 0) { + i = 0; + if (prepend_sign) { + __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, 0, '-'); + i++; + } + for (; i < uoffset; i++) { + __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, i, padding_char); + } + } + for (i=0; i < clength; i++) { + __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, uoffset+i, chars[i]); + } +#else + { + PyObject *sign = NULL, *padding = NULL; + uval = NULL; + if (uoffset > 0) { + prepend_sign = !!prepend_sign; + if (uoffset > prepend_sign) { + padding = PyUnicode_FromOrdinal(padding_char); + if (likely(padding) && uoffset > prepend_sign + 1) { + PyObject *tmp = PySequence_Repeat(padding, uoffset - prepend_sign); + Py_DECREF(padding); + padding = tmp; + } + if (unlikely(!padding)) goto done_or_error; + } + if (prepend_sign) { + sign = PyUnicode_FromOrdinal('-'); + if (unlikely(!sign)) goto done_or_error; + } + } + uval = PyUnicode_DecodeASCII(chars, clength, NULL); + if (likely(uval) && padding) { + PyObject *tmp = PyUnicode_Concat(padding, uval); + Py_DECREF(uval); + uval = tmp; + } + if (likely(uval) && sign) { + PyObject *tmp = PyUnicode_Concat(sign, uval); + Py_DECREF(uval); + uval = tmp; + } +done_or_error: + Py_XDECREF(padding); + Py_XDECREF(sign); + } +#endif + return uval; +} + +/* COrdinalToPyUnicode */ +static CYTHON_INLINE int __Pyx_CheckUnicodeValue(int value) { + return value <= 1114111; +} +static PyObject* __Pyx_PyUnicode_FromOrdinal_Padded(int value, Py_ssize_t ulength, char padding_char) { + if (likely(ulength <= 250)) { + char chars[256]; + if (value <= 255) { + memset(chars, padding_char, (size_t) (ulength - 1)); + chars[ulength-1] = (char) value; + return PyUnicode_DecodeLatin1(chars, ulength, NULL); + } + char *cpos = chars + sizeof(chars); + if (value < 0x800) { + *--cpos = (char) (0x80 | (value & 0x3f)); + value >>= 6; + *--cpos = (char) (0xc0 | (value & 0x1f)); + } else if (value < 0x10000) { + *--cpos = (char) (0x80 | (value & 0x3f)); + value >>= 6; + *--cpos = (char) (0x80 | (value & 0x3f)); + value >>= 6; + *--cpos = (char) (0xe0 | (value & 0x0f)); + } else { + *--cpos = (char) (0x80 | (value & 0x3f)); + value >>= 6; + *--cpos = (char) (0x80 | (value & 0x3f)); + value >>= 6; + *--cpos = (char) (0x80 | (value & 0x3f)); + value >>= 6; + *--cpos = (char) (0xf0 | (value & 0x07)); + } + cpos -= ulength; + memset(cpos, padding_char, (size_t) (ulength - 1)); + return PyUnicode_DecodeUTF8(cpos, chars + sizeof(chars) - cpos, NULL); + } + if (value <= 127 && CYTHON_USE_UNICODE_INTERNALS) { + const char chars[1] = {(char) value}; + return __Pyx_PyUnicode_BuildFromAscii(ulength, chars, 1, 0, padding_char); + } + { + PyObject *uchar, *padding_uchar, *padding, *result; + padding_uchar = PyUnicode_FromOrdinal(padding_char); + if (unlikely(!padding_uchar)) return NULL; + padding = PySequence_Repeat(padding_uchar, ulength - 1); + Py_DECREF(padding_uchar); + if (unlikely(!padding)) return NULL; + uchar = PyUnicode_FromOrdinal(value); + if (unlikely(!uchar)) { + Py_DECREF(padding); + return NULL; + } + result = PyUnicode_Concat(padding, uchar); + Py_DECREF(padding); + Py_DECREF(uchar); + return result; + } +} + +/* CIntToPyUnicode */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char) { + char digits[sizeof(int)*3+2]; + char *dpos, *end = digits + sizeof(int)*3+2; + const char *hex_digits = DIGITS_HEX; + Py_ssize_t length, ulength; + int prepend_sign, last_one_off; + int remaining; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const int neg_one = (int) -1, const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (format_char == 'c') { + if (unlikely(!(is_unsigned || value == 0 || value > 0) || + !(sizeof(value) <= 2 || value & ~ (int) 0x01fffff || __Pyx_CheckUnicodeValue((int) value)))) { + PyErr_SetString(PyExc_OverflowError, "%c arg not in range(0x110000)"); + return NULL; + } + if (width <= 1) { + return PyUnicode_FromOrdinal((int) value); + } + return __Pyx_PyUnicode_FromOrdinal_Padded((int) value, width, padding_char); + } + if (format_char == 'X') { + hex_digits += 16; + format_char = 'x'; + } + remaining = value; + last_one_off = 0; + dpos = end; + do { + int digit_pos; + switch (format_char) { + case 'o': + digit_pos = abs((int)(remaining % (8*8))); + remaining = (int) (remaining / (8*8)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2); + last_one_off = (digit_pos < 8); + break; + case 'd': + digit_pos = abs((int)(remaining % (10*10))); + remaining = (int) (remaining / (10*10)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2); + last_one_off = (digit_pos < 10); + break; + case 'x': + *(--dpos) = hex_digits[abs((int)(remaining % 16))]; + remaining = (int) (remaining / 16); + break; + default: + assert(0); + break; + } + } while (unlikely(remaining != 0)); + assert(!last_one_off || *dpos == '0'); + dpos += last_one_off; + length = end - dpos; + ulength = length; + prepend_sign = 0; + if (!is_unsigned && value <= neg_one) { + if (padding_char == ' ' || width <= length + 1) { + *(--dpos) = '-'; + ++length; + } else { + prepend_sign = 1; + } + ++ulength; + } + if (width > ulength) { + ulength = width; + } + if (ulength == 1) { + return PyUnicode_FromOrdinal(*dpos); + } + return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char); +} + +/* CIntToPyUnicode */ +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char) { + char digits[sizeof(Py_ssize_t)*3+2]; + char *dpos, *end = digits + sizeof(Py_ssize_t)*3+2; + const char *hex_digits = DIGITS_HEX; + Py_ssize_t length, ulength; + int prepend_sign, last_one_off; + Py_ssize_t remaining; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const Py_ssize_t neg_one = (Py_ssize_t) -1, const_zero = (Py_ssize_t) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (format_char == 'c') { + if (unlikely(!(is_unsigned || value == 0 || value > 0) || + !(sizeof(value) <= 2 || value & ~ (Py_ssize_t) 0x01fffff || __Pyx_CheckUnicodeValue((int) value)))) { + PyErr_SetString(PyExc_OverflowError, "%c arg not in range(0x110000)"); + return NULL; + } + if (width <= 1) { + return PyUnicode_FromOrdinal((int) value); + } + return __Pyx_PyUnicode_FromOrdinal_Padded((int) value, width, padding_char); + } + if (format_char == 'X') { + hex_digits += 16; + format_char = 'x'; + } + remaining = value; + last_one_off = 0; + dpos = end; + do { + int digit_pos; + switch (format_char) { + case 'o': + digit_pos = abs((int)(remaining % (8*8))); + remaining = (Py_ssize_t) (remaining / (8*8)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2); + last_one_off = (digit_pos < 8); + break; + case 'd': + digit_pos = abs((int)(remaining % (10*10))); + remaining = (Py_ssize_t) (remaining / (10*10)); + dpos -= 2; + memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2); + last_one_off = (digit_pos < 10); + break; + case 'x': + *(--dpos) = hex_digits[abs((int)(remaining % 16))]; + remaining = (Py_ssize_t) (remaining / 16); + break; + default: + assert(0); + break; + } + } while (unlikely(remaining != 0)); + assert(!last_one_off || *dpos == '0'); + dpos += last_one_off; + length = end - dpos; + ulength = length; + prepend_sign = 0; + if (!is_unsigned && value <= neg_one) { + if (padding_char == ' ' || width <= length + 1) { + *(--dpos) = '-'; + ++length; + } else { + prepend_sign = 1; + } + ++ulength; + } + if (width > ulength) { + ulength = width; + } + if (ulength == 1) { + return PyUnicode_FromOrdinal(*dpos); + } + return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char); +} + +/* JoinPyUnicode */ +static PyObject* __Pyx_PyUnicode_Join(PyObject** values, Py_ssize_t value_count, Py_ssize_t result_ulength, + Py_UCS4 max_char) { +#if CYTHON_USE_UNICODE_INTERNALS && CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + PyObject *result_uval; + int result_ukind, kind_shift; + Py_ssize_t i, char_pos; + void *result_udata; + if (max_char > 1114111) max_char = 1114111; + result_uval = PyUnicode_New(result_ulength, max_char); + if (unlikely(!result_uval)) return NULL; + result_ukind = (max_char <= 255) ? PyUnicode_1BYTE_KIND : (max_char <= 65535) ? PyUnicode_2BYTE_KIND : PyUnicode_4BYTE_KIND; + kind_shift = (result_ukind == PyUnicode_4BYTE_KIND) ? 2 : result_ukind - 1; + result_udata = PyUnicode_DATA(result_uval); + assert(kind_shift == 2 || kind_shift == 1 || kind_shift == 0); + if (unlikely((PY_SSIZE_T_MAX >> kind_shift) - result_ulength < 0)) + goto overflow; + char_pos = 0; + for (i=0; i < value_count; i++) { + int ukind; + Py_ssize_t ulength; + void *udata; + PyObject *uval = values[i]; + #if !CYTHON_COMPILING_IN_LIMITED_API + if (__Pyx_PyUnicode_READY(uval) == (-1)) + goto bad; + #endif + ulength = __Pyx_PyUnicode_GET_LENGTH(uval); + #if !CYTHON_ASSUME_SAFE_SIZE + if (unlikely(ulength < 0)) goto bad; + #endif + if (unlikely(!ulength)) + continue; + if (unlikely((PY_SSIZE_T_MAX >> kind_shift) - ulength < char_pos)) + goto overflow; + ukind = __Pyx_PyUnicode_KIND(uval); + udata = __Pyx_PyUnicode_DATA(uval); + if (ukind == result_ukind) { + memcpy((char *)result_udata + (char_pos << kind_shift), udata, (size_t) (ulength << kind_shift)); + } else { + #if PY_VERSION_HEX >= 0x030d0000 + if (unlikely(PyUnicode_CopyCharacters(result_uval, char_pos, uval, 0, ulength) < 0)) goto bad; + #elif CYTHON_COMPILING_IN_CPYTHON || defined(_PyUnicode_FastCopyCharacters) + _PyUnicode_FastCopyCharacters(result_uval, char_pos, uval, 0, ulength); + #else + Py_ssize_t j; + for (j=0; j < ulength; j++) { + Py_UCS4 uchar = __Pyx_PyUnicode_READ(ukind, udata, j); + __Pyx_PyUnicode_WRITE(result_ukind, result_udata, char_pos+j, uchar); + } + #endif + } + char_pos += ulength; + } + return result_uval; +overflow: + PyErr_SetString(PyExc_OverflowError, "join() result is too long for a Python string"); +bad: + Py_DECREF(result_uval); + return NULL; +#else + Py_ssize_t i; + PyObject *result = NULL; + PyObject *value_tuple = PyTuple_New(value_count); + if (unlikely(!value_tuple)) return NULL; + CYTHON_UNUSED_VAR(max_char); + CYTHON_UNUSED_VAR(result_ulength); + for (i=0; i__pyx_empty_unicode, value_tuple); +bad: + Py_DECREF(value_tuple); + return result; +#endif +} + +/* GetAttr */ +static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { +#if CYTHON_USE_TYPE_SLOTS + if (likely(PyUnicode_Check(n))) + return __Pyx_PyObject_GetAttrStr(o, n); +#endif + return PyObject_GetAttr(o, n); +} + +/* GetItemInt */ +static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { + PyObject *r; + if (unlikely(!j)) return NULL; + r = PyObject_GetItem(o, j); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && CYTHON_ASSUME_SAFE_SIZE && !CYTHON_AVOID_BORROWED_REFS && !CYTHON_AVOID_THREAD_UNSAFE_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyList_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { + PyObject *r = PyList_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyLong_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && CYTHON_ASSUME_SAFE_SIZE && !CYTHON_AVOID_BORROWED_REFS + Py_ssize_t wrapped_i = i; + if (wraparound & unlikely(i < 0)) { + wrapped_i += PyTuple_GET_SIZE(o); + } + if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); + Py_INCREF(r); + return r; + } + return __Pyx_GetItemInt_Generic(o, PyLong_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif +} +static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, + CYTHON_NCP_UNUSED int wraparound, + CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && CYTHON_ASSUME_SAFE_SIZE && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); + if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { + return __Pyx_PyList_GetItemRef(o, n); + } + } + else if (PyTuple_CheckExact(o)) { + Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); + if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, n); + Py_INCREF(r); + return r; + } + } else { + PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping; + PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence; + if (mm && mm->mp_subscript) { + PyObject *r, *key = PyLong_FromSsize_t(i); + if (unlikely(!key)) return NULL; + r = mm->mp_subscript(o, key); + Py_DECREF(key); + return r; + } + if (likely(sm && sm->sq_item)) { + if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) { + Py_ssize_t l = sm->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return NULL; + PyErr_Clear(); + } + } + return sm->sq_item(o, i); + } + } +#else + if (is_list || !PyMapping_Check(o)) { + return PySequence_GetItem(o, i); + } +#endif + return __Pyx_GetItemInt_Generic(o, PyLong_FromSsize_t(i)); +} + +/* PyObjectCallOneArg */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *args[2] = {NULL, arg}; + return __Pyx_PyObject_FastCall(func, args+1, 1 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET); +} + +/* ObjectGetItem */ +#if CYTHON_USE_TYPE_SLOTS +static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject *index) { + PyObject *runerr = NULL; + Py_ssize_t key_value; + key_value = __Pyx_PyIndex_AsSsize_t(index); + if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { + return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); + } + if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { + __Pyx_TypeName index_type_name = __Pyx_PyType_GetFullyQualifiedName(Py_TYPE(index)); + PyErr_Clear(); + PyErr_Format(PyExc_IndexError, + "cannot fit '" __Pyx_FMT_TYPENAME "' into an index-sized integer", index_type_name); + __Pyx_DECREF_TypeName(index_type_name); + } + return NULL; +} +static PyObject *__Pyx_PyObject_GetItem_Slow(PyObject *obj, PyObject *key) { + __Pyx_TypeName obj_type_name; + if (likely(PyType_Check(obj))) { + PyObject *meth = __Pyx_PyObject_GetAttrStrNoError(obj, __pyx_mstate_global->__pyx_n_u_class_getitem); + if (!meth) { + PyErr_Clear(); + } else { + PyObject *result = __Pyx_PyObject_CallOneArg(meth, key); + Py_DECREF(meth); + return result; + } + } + obj_type_name = __Pyx_PyType_GetFullyQualifiedName(Py_TYPE(obj)); + PyErr_Format(PyExc_TypeError, + "'" __Pyx_FMT_TYPENAME "' object is not subscriptable", obj_type_name); + __Pyx_DECREF_TypeName(obj_type_name); + return NULL; +} +static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key) { + PyTypeObject *tp = Py_TYPE(obj); + PyMappingMethods *mm = tp->tp_as_mapping; + PySequenceMethods *sm = tp->tp_as_sequence; + if (likely(mm && mm->mp_subscript)) { + return mm->mp_subscript(obj, key); + } + if (likely(sm && sm->sq_item)) { + return __Pyx_PyObject_GetIndex(obj, key); + } + return __Pyx_PyObject_GetItem_Slow(obj, key); +} +#endif + +/* RejectKeywords */ +static void __Pyx_RejectKeywords(const char* function_name, PyObject *kwds) { + PyObject *key = NULL; + if (CYTHON_METH_FASTCALL && likely(PyTuple_Check(kwds))) { + key = __Pyx_PySequence_ITEM(kwds, 0); + } else { + Py_ssize_t pos = 0; +#if !CYTHON_COMPILING_IN_PYPY || defined(PyArg_ValidateKeywordArguments) + if (unlikely(!PyArg_ValidateKeywordArguments(kwds))) return; +#endif + PyDict_Next(kwds, &pos, &key, NULL); + Py_INCREF(key); + } + if (likely(key)) { + PyErr_Format(PyExc_TypeError, + "%s() got an unexpected keyword argument '%U'", + function_name, key); + Py_DECREF(key); + } +} + +/* DivInt[Py_ssize_t] */ +static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b, int b_is_constant) { + Py_ssize_t q = a / b; + Py_ssize_t r = a - q*b; + Py_ssize_t adapt_python = (b_is_constant ? + ((r != 0) & ((r < 0) ^ (b < 0))) : + ((r != 0) & ((r ^ b) < 0)) + ); + return q - adapt_python; +} + +/* GetAttr3 */ +#if __PYX_LIMITED_VERSION_HEX < 0x030d0000 +static PyObject *__Pyx_GetAttr3Default(PyObject *d) { + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) + return NULL; + __Pyx_PyErr_Clear(); + Py_INCREF(d); + return d; +} +#endif +static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { + PyObject *r; +#if __PYX_LIMITED_VERSION_HEX >= 0x030d0000 + int res = PyObject_GetOptionalAttr(o, n, &r); + return (res != 0) ? r : __Pyx_NewRef(d); +#else + #if CYTHON_USE_TYPE_SLOTS + if (likely(PyUnicode_Check(n))) { + r = __Pyx_PyObject_GetAttrStrNoError(o, n); + if (unlikely(!r) && likely(!PyErr_Occurred())) { + r = __Pyx_NewRef(d); + } + return r; + } + #endif + r = PyObject_GetAttr(o, n); + return (likely(r)) ? r : __Pyx_GetAttr3Default(d); +#endif +} + +/* PyDictVersioning */ +#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { + PyObject *dict = Py_TYPE(obj)->tp_dict; + return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; +} +static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { + PyObject **dictptr = NULL; + Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; + if (offset) { +#if CYTHON_COMPILING_IN_CPYTHON + dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); +#else + dictptr = _PyObject_GetDictPtr(obj); +#endif + } + return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; +} +static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { + PyObject *dict = Py_TYPE(obj)->tp_dict; + if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) + return 0; + return obj_dict_version == __Pyx_get_object_dict_version(obj); +} +#endif + +/* GetModuleGlobalName */ +#if CYTHON_USE_DICT_VERSIONS +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) +#else +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) +#endif +{ + PyObject *result; +#if CYTHON_COMPILING_IN_LIMITED_API + if (unlikely(!__pyx_m)) { + if (!PyErr_Occurred()) + PyErr_SetNone(PyExc_NameError); + return NULL; + } + result = PyObject_GetAttr(__pyx_m, name); + if (likely(result)) { + return result; + } + PyErr_Clear(); +#elif CYTHON_AVOID_BORROWED_REFS || CYTHON_AVOID_THREAD_UNSAFE_BORROWED_REFS + if (unlikely(__Pyx_PyDict_GetItemRef(__pyx_mstate_global->__pyx_d, name, &result) == -1)) PyErr_Clear(); + __PYX_UPDATE_DICT_CACHE(__pyx_mstate_global->__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return result; + } +#else + result = _PyDict_GetItem_KnownHash(__pyx_mstate_global->__pyx_d, name, ((PyASCIIObject *) name)->hash); + __PYX_UPDATE_DICT_CACHE(__pyx_mstate_global->__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } + PyErr_Clear(); +#endif + return __Pyx_GetBuiltinName(name); +} + +/* RaiseTooManyValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +/* RaiseNeedMoreValuesToUnpack */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +/* RaiseNoneIterError */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +/* ExtTypeTest */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + __Pyx_TypeName obj_type_name; + __Pyx_TypeName type_name; + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(__Pyx_TypeCheck(obj, type))) + return 1; + obj_type_name = __Pyx_PyType_GetFullyQualifiedName(Py_TYPE(obj)); + type_name = __Pyx_PyType_GetFullyQualifiedName(type); + PyErr_Format(PyExc_TypeError, + "Cannot convert " __Pyx_FMT_TYPENAME " to " __Pyx_FMT_TYPENAME, + obj_type_name, type_name); + __Pyx_DECREF_TypeName(obj_type_name); + __Pyx_DECREF_TypeName(type_name); + return 0; +} + +/* GetTopmostException */ +#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE +static _PyErr_StackItem * +__Pyx_PyErr_GetTopmostException(PyThreadState *tstate) +{ + _PyErr_StackItem *exc_info = tstate->exc_info; + while ((exc_info->exc_value == NULL || exc_info->exc_value == Py_None) && + exc_info->previous_item != NULL) + { + exc_info = exc_info->previous_item; + } + return exc_info; +} +#endif + +/* SaveResetException */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 + _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); + PyObject *exc_value = exc_info->exc_value; + if (exc_value == NULL || exc_value == Py_None) { + *value = NULL; + *type = NULL; + *tb = NULL; + } else { + *value = exc_value; + Py_INCREF(*value); + *type = (PyObject*) Py_TYPE(exc_value); + Py_INCREF(*type); + *tb = PyException_GetTraceback(exc_value); + } + #elif CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); + *type = exc_info->exc_type; + *value = exc_info->exc_value; + *tb = exc_info->exc_traceback; + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); + #else + *type = tstate->exc_type; + *value = tstate->exc_value; + *tb = tstate->exc_traceback; + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); + #endif +} +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 + _PyErr_StackItem *exc_info = tstate->exc_info; + PyObject *tmp_value = exc_info->exc_value; + exc_info->exc_value = value; + Py_XDECREF(tmp_value); + Py_XDECREF(type); + Py_XDECREF(tb); + #else + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = type; + exc_info->exc_value = value; + exc_info->exc_traceback = tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = type; + tstate->exc_value = value; + tstate->exc_traceback = tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); + #endif +} +#endif + +/* GetException */ +#if CYTHON_FAST_THREAD_STATE +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) +#endif +{ + PyObject *local_type = NULL, *local_value, *local_tb = NULL; +#if CYTHON_FAST_THREAD_STATE + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if PY_VERSION_HEX >= 0x030C0000 + local_value = tstate->current_exception; + tstate->current_exception = 0; + #else + local_type = tstate->curexc_type; + local_value = tstate->curexc_value; + local_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; + #endif +#elif __PYX_LIMITED_VERSION_HEX > 0x030C0000 + local_value = PyErr_GetRaisedException(); +#else + PyErr_Fetch(&local_type, &local_value, &local_tb); +#endif +#if __PYX_LIMITED_VERSION_HEX > 0x030C0000 + if (likely(local_value)) { + local_type = (PyObject*) Py_TYPE(local_value); + Py_INCREF(local_type); + local_tb = PyException_GetTraceback(local_value); + } +#else + PyErr_NormalizeException(&local_type, &local_value, &local_tb); +#if CYTHON_FAST_THREAD_STATE + if (unlikely(tstate->curexc_type)) +#else + if (unlikely(PyErr_Occurred())) +#endif + goto bad; + if (local_tb) { + if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) + goto bad; + } +#endif // __PYX_LIMITED_VERSION_HEX > 0x030C0000 + Py_XINCREF(local_tb); + Py_XINCREF(local_type); + Py_XINCREF(local_value); + *type = local_type; + *value = local_value; + *tb = local_tb; +#if CYTHON_FAST_THREAD_STATE + #if CYTHON_USE_EXC_INFO_STACK + { + _PyErr_StackItem *exc_info = tstate->exc_info; + #if PY_VERSION_HEX >= 0x030B00a4 + tmp_value = exc_info->exc_value; + exc_info->exc_value = local_value; + tmp_type = NULL; + tmp_tb = NULL; + Py_XDECREF(local_type); + Py_XDECREF(local_tb); + #else + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = local_type; + exc_info->exc_value = local_value; + exc_info->exc_traceback = local_tb; + #endif + } + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = local_type; + tstate->exc_value = local_value; + tstate->exc_traceback = local_tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#elif __PYX_LIMITED_VERSION_HEX >= 0x030b0000 + PyErr_SetHandledException(local_value); + Py_XDECREF(local_value); + Py_XDECREF(local_type); + Py_XDECREF(local_tb); +#else + PyErr_SetExcInfo(local_type, local_value, local_tb); +#endif + return 0; +#if __PYX_LIMITED_VERSION_HEX <= 0x030C0000 +bad: + *type = 0; + *value = 0; + *tb = 0; + Py_XDECREF(local_type); + Py_XDECREF(local_value); + Py_XDECREF(local_tb); + return -1; +#endif +} + +/* SwapException */ +#if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_value = exc_info->exc_value; + exc_info->exc_value = *value; + if (tmp_value == NULL || tmp_value == Py_None) { + Py_XDECREF(tmp_value); + tmp_value = NULL; + tmp_type = NULL; + tmp_tb = NULL; + } else { + tmp_type = (PyObject*) Py_TYPE(tmp_value); + Py_INCREF(tmp_type); + #if CYTHON_COMPILING_IN_CPYTHON + tmp_tb = ((PyBaseExceptionObject*) tmp_value)->traceback; + Py_XINCREF(tmp_tb); + #else + tmp_tb = PyException_GetTraceback(tmp_value); + #endif + } + #elif CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = *type; + exc_info->exc_value = *value; + exc_info->exc_traceback = *tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = *type; + tstate->exc_value = *value; + tstate->exc_traceback = *tb; + #endif + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#else +static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); + PyErr_SetExcInfo(*type, *value, *tb); + *type = tmp_type; + *value = tmp_value; + *tb = tmp_tb; +} +#endif + +/* Import */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *module = 0; + PyObject *empty_dict = 0; + PyObject *empty_list = 0; + empty_dict = PyDict_New(); + if (unlikely(!empty_dict)) + goto bad; + if (level == -1) { + const char* package_sep = strchr(__Pyx_MODULE_NAME, '.'); + if (package_sep != (0)) { + module = PyImport_ImportModuleLevelObject( + name, __pyx_mstate_global->__pyx_d, empty_dict, from_list, 1); + if (unlikely(!module)) { + if (unlikely(!PyErr_ExceptionMatches(PyExc_ImportError))) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + if (!module) { + module = PyImport_ImportModuleLevelObject( + name, __pyx_mstate_global->__pyx_d, empty_dict, from_list, level); + } +bad: + Py_XDECREF(empty_dict); + Py_XDECREF(empty_list); + return module; +} + +/* ImportDottedModule */ +static PyObject *__Pyx__ImportDottedModule_Error(PyObject *name, PyObject *parts_tuple, Py_ssize_t count) { + PyObject *partial_name = NULL, *slice = NULL, *sep = NULL; + Py_ssize_t size; + if (unlikely(PyErr_Occurred())) { + PyErr_Clear(); + } +#if CYTHON_ASSUME_SAFE_SIZE + size = PyTuple_GET_SIZE(parts_tuple); +#else + size = PyTuple_Size(parts_tuple); + if (size < 0) goto bad; +#endif + if (likely(size == count)) { + partial_name = name; + } else { + slice = PySequence_GetSlice(parts_tuple, 0, count); + if (unlikely(!slice)) + goto bad; + sep = PyUnicode_FromStringAndSize(".", 1); + if (unlikely(!sep)) + goto bad; + partial_name = PyUnicode_Join(sep, slice); + } + PyErr_Format( + PyExc_ModuleNotFoundError, + "No module named '%U'", partial_name); +bad: + Py_XDECREF(sep); + Py_XDECREF(slice); + Py_XDECREF(partial_name); + return NULL; +} +static PyObject *__Pyx__ImportDottedModule_Lookup(PyObject *name) { + PyObject *imported_module; +#if (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM < 0x07030400) ||\ + CYTHON_COMPILING_IN_GRAAL + PyObject *modules = PyImport_GetModuleDict(); + if (unlikely(!modules)) + return NULL; + imported_module = __Pyx_PyDict_GetItemStr(modules, name); + Py_XINCREF(imported_module); +#else + imported_module = PyImport_GetModule(name); +#endif + return imported_module; +} +static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple) { + Py_ssize_t i, nparts; +#if CYTHON_ASSUME_SAFE_SIZE + nparts = PyTuple_GET_SIZE(parts_tuple); +#else + nparts = PyTuple_Size(parts_tuple); + if (nparts < 0) return NULL; +#endif + for (i=1; i < nparts && module; i++) { + PyObject *part, *submodule; +#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + part = PyTuple_GET_ITEM(parts_tuple, i); +#else + part = __Pyx_PySequence_ITEM(parts_tuple, i); + if (!part) return NULL; +#endif + submodule = __Pyx_PyObject_GetAttrStrNoError(module, part); +#if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) + Py_DECREF(part); +#endif + Py_DECREF(module); + module = submodule; + } + if (unlikely(!module)) { + return __Pyx__ImportDottedModule_Error(name, parts_tuple, i); + } + return module; +} +static PyObject *__Pyx__ImportDottedModule(PyObject *name, PyObject *parts_tuple) { + PyObject *imported_module; + PyObject *module = __Pyx_Import(name, NULL, 0); + if (!parts_tuple || unlikely(!module)) + return module; + imported_module = __Pyx__ImportDottedModule_Lookup(name); + if (likely(imported_module)) { + Py_DECREF(module); + return imported_module; + } + PyErr_Clear(); + return __Pyx_ImportDottedModule_WalkParts(module, name, parts_tuple); +} +static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple) { +#if CYTHON_COMPILING_IN_CPYTHON + PyObject *module = __Pyx__ImportDottedModule_Lookup(name); + if (likely(module)) { + PyObject *spec = __Pyx_PyObject_GetAttrStrNoError(module, __pyx_mstate_global->__pyx_n_u_spec); + if (likely(spec)) { + PyObject *unsafe = __Pyx_PyObject_GetAttrStrNoError(spec, __pyx_mstate_global->__pyx_n_u_initializing); + if (likely(!unsafe || !__Pyx_PyObject_IsTrue(unsafe))) { + Py_DECREF(spec); + spec = NULL; + } + Py_XDECREF(unsafe); + } + if (likely(!spec)) { + PyErr_Clear(); + return module; + } + Py_DECREF(spec); + Py_DECREF(module); + } else if (PyErr_Occurred()) { + PyErr_Clear(); + } +#endif + return __Pyx__ImportDottedModule(name, parts_tuple); +} + +/* FastTypeChecks */ +#if CYTHON_COMPILING_IN_CPYTHON +static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { + while (a) { + a = __Pyx_PyType_GetSlot(a, tp_base, PyTypeObject*); + if (a == b) + return 1; + } + return b == &PyBaseObject_Type; +} +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { + PyObject *mro; + if (a == b) return 1; + mro = a->tp_mro; + if (likely(mro)) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(mro); + for (i = 0; i < n; i++) { + if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) + return 1; + } + return 0; + } + return __Pyx_InBases(a, b); +} +static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b) { + PyObject *mro; + if (cls == a || cls == b) return 1; + mro = cls->tp_mro; + if (likely(mro)) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(mro); + for (i = 0; i < n; i++) { + PyObject *base = PyTuple_GET_ITEM(mro, i); + if (base == (PyObject *)a || base == (PyObject *)b) + return 1; + } + return 0; + } + return __Pyx_InBases(cls, a) || __Pyx_InBases(cls, b); +} +static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { + if (exc_type1) { + return __Pyx_IsAnySubtype2((PyTypeObject*)err, (PyTypeObject*)exc_type1, (PyTypeObject*)exc_type2); + } else { + return __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); + } +} +static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { + Py_ssize_t i, n; + assert(PyExceptionClass_Check(exc_type)); + n = PyTuple_GET_SIZE(tuple); + for (i=0; itp_as_sequence && type->tp_as_sequence->sq_repeat)) { + return type->tp_as_sequence->sq_repeat(seq, mul); + } else +#endif + { + return __Pyx_PySequence_Multiply_Generic(seq, mul); + } +} + +/* PyObjectFormatAndDecref */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_FormatSimpleAndDecref(PyObject* s, PyObject* f) { + if (unlikely(!s)) return NULL; + if (likely(PyUnicode_CheckExact(s))) return s; + return __Pyx_PyObject_FormatAndDecref(s, f); +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_FormatAndDecref(PyObject* s, PyObject* f) { + PyObject *result; + if (unlikely(!s)) return NULL; + result = PyObject_Format(s, f); + Py_DECREF(s); + return result; +} + +/* PyObjectFormat */ +#if CYTHON_USE_UNICODE_WRITER +static PyObject* __Pyx_PyObject_Format(PyObject* obj, PyObject* format_spec) { + int ret; + _PyUnicodeWriter writer; + if (likely(PyFloat_CheckExact(obj))) { + _PyUnicodeWriter_Init(&writer); + ret = _PyFloat_FormatAdvancedWriter( + &writer, + obj, + format_spec, 0, PyUnicode_GET_LENGTH(format_spec)); + } else if (likely(PyLong_CheckExact(obj))) { + _PyUnicodeWriter_Init(&writer); + ret = _PyLong_FormatAdvancedWriter( + &writer, + obj, + format_spec, 0, PyUnicode_GET_LENGTH(format_spec)); + } else { + return PyObject_Format(obj, format_spec); + } + if (unlikely(ret == -1)) { + _PyUnicodeWriter_Dealloc(&writer); + return NULL; + } + return _PyUnicodeWriter_Finish(&writer); +} +#endif + +/* SetItemInt */ +static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) { + int r; + if (unlikely(!j)) return -1; + r = PyObject_SetItem(o, j, v); + Py_DECREF(j); + return r; +} +static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, int is_list, + CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { +#if CYTHON_ASSUME_SAFE_MACROS && CYTHON_ASSUME_SAFE_SIZE && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS + if (is_list || PyList_CheckExact(o)) { + Py_ssize_t n = (!wraparound) ? i : ((likely(i >= 0)) ? i : i + PyList_GET_SIZE(o)); + if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o)))) { + Py_INCREF(v); +#if CYTHON_AVOID_THREAD_UNSAFE_BORROWED_REFS + PyList_SetItem(o, n, v); +#else + PyObject* old = PyList_GET_ITEM(o, n); + PyList_SET_ITEM(o, n, v); + Py_DECREF(old); +#endif + return 1; + } + } else { + PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping; + PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence; + if (mm && mm->mp_ass_subscript) { + int r; + PyObject *key = PyLong_FromSsize_t(i); + if (unlikely(!key)) return -1; + r = mm->mp_ass_subscript(o, key, v); + Py_DECREF(key); + return r; + } + if (likely(sm && sm->sq_ass_item)) { + if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) { + Py_ssize_t l = sm->sq_length(o); + if (likely(l >= 0)) { + i += l; + } else { + if (!PyErr_ExceptionMatches(PyExc_OverflowError)) + return -1; + PyErr_Clear(); + } + } + return sm->sq_ass_item(o, i, v); + } + } +#else + if (is_list || !PyMapping_Check(o)) + { + return PySequence_SetItem(o, i, v); + } +#endif + return __Pyx_SetItemInt_Generic(o, PyLong_FromSsize_t(i), v); +} + +/* RaiseUnboundLocalError */ +static void __Pyx_RaiseUnboundLocalError(const char *varname) { + PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); +} + +/* DivInt[long] */ +static CYTHON_INLINE long __Pyx_div_long(long a, long b, int b_is_constant) { + long q = a / b; + long r = a - q*b; + long adapt_python = (b_is_constant ? + ((r != 0) & ((r < 0) ^ (b < 0))) : + ((r != 0) & ((r ^ b) < 0)) + ); + return q - adapt_python; +} + +/* ImportFrom */ +static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { + PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); + if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { + const char* module_name_str = 0; + PyObject* module_name = 0; + PyObject* module_dot = 0; + PyObject* full_name = 0; + PyErr_Clear(); + module_name_str = PyModule_GetName(module); + if (unlikely(!module_name_str)) { goto modbad; } + module_name = PyUnicode_FromString(module_name_str); + if (unlikely(!module_name)) { goto modbad; } + module_dot = PyUnicode_Concat(module_name, __pyx_mstate_global->__pyx_kp_u__2); + if (unlikely(!module_dot)) { goto modbad; } + full_name = PyUnicode_Concat(module_dot, name); + if (unlikely(!full_name)) { goto modbad; } + #if (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM < 0x07030400) ||\ + CYTHON_COMPILING_IN_GRAAL + { + PyObject *modules = PyImport_GetModuleDict(); + if (unlikely(!modules)) + goto modbad; + value = PyObject_GetItem(modules, full_name); + } + #else + value = PyImport_GetModule(full_name); + #endif + modbad: + Py_XDECREF(full_name); + Py_XDECREF(module_dot); + Py_XDECREF(module_name); + } + if (unlikely(!value)) { + PyErr_Format(PyExc_ImportError, "cannot import name %S", name); + } + return value; +} + +/* HasAttr */ +#if __PYX_LIMITED_VERSION_HEX < 0x030d0000 +static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { + PyObject *r; + if (unlikely(!PyUnicode_Check(n))) { + PyErr_SetString(PyExc_TypeError, + "hasattr(): attribute name must be string"); + return -1; + } + r = __Pyx_PyObject_GetAttrStrNoError(o, n); + if (!r) { + return (unlikely(PyErr_Occurred())) ? -1 : 0; + } else { + Py_DECREF(r); + return 1; + } +} +#endif + +/* BufferIndexError */ +static void __Pyx_RaiseBufferIndexError(int axis) { + PyErr_Format(PyExc_IndexError, + "Out of bounds on buffer access (axis %d)", axis); +} + +/* PyObjectVectorCallKwBuilder */ +#if CYTHON_VECTORCALL +static int __Pyx_VectorcallBuilder_AddArg(PyObject *key, PyObject *value, PyObject *builder, PyObject **args, int n) { + (void)__Pyx_PyObject_FastCallDict; + if (__Pyx_PyTuple_SET_ITEM(builder, n, key) != (0)) return -1; + Py_INCREF(key); + args[n] = value; + return 0; +} +CYTHON_UNUSED static int __Pyx_VectorcallBuilder_AddArg_Check(PyObject *key, PyObject *value, PyObject *builder, PyObject **args, int n) { + (void)__Pyx_VectorcallBuilder_AddArgStr; + if (unlikely(!PyUnicode_Check(key))) { + PyErr_SetString(PyExc_TypeError, "keywords must be strings"); + return -1; + } + return __Pyx_VectorcallBuilder_AddArg(key, value, builder, args, n); +} +static int __Pyx_VectorcallBuilder_AddArgStr(const char *key, PyObject *value, PyObject *builder, PyObject **args, int n) { + PyObject *pyKey = PyUnicode_FromString(key); + if (!pyKey) return -1; + return __Pyx_VectorcallBuilder_AddArg(pyKey, value, builder, args, n); +} +#else // CYTHON_VECTORCALL +CYTHON_UNUSED static int __Pyx_VectorcallBuilder_AddArg_Check(PyObject *key, PyObject *value, PyObject *builder, CYTHON_UNUSED PyObject **args, CYTHON_UNUSED int n) { + if (unlikely(!PyUnicode_Check(key))) { + PyErr_SetString(PyExc_TypeError, "keywords must be strings"); + return -1; + } + return PyDict_SetItem(builder, key, value); +} +#endif + +/* PyObjectCall2Args */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) { + PyObject *args[3] = {NULL, arg1, arg2}; + return __Pyx_PyObject_FastCall(function, args+1, 2 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET); +} + +/* PyObjectGetMethod */ +static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method) { + PyObject *attr; +#if CYTHON_UNPACK_METHODS && CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_PYTYPE_LOOKUP + __Pyx_TypeName type_name; + PyTypeObject *tp = Py_TYPE(obj); + PyObject *descr; + descrgetfunc f = NULL; + PyObject **dictptr, *dict; + int meth_found = 0; + assert (*method == NULL); + if (unlikely(tp->tp_getattro != PyObject_GenericGetAttr)) { + attr = __Pyx_PyObject_GetAttrStr(obj, name); + goto try_unpack; + } + if (unlikely(tp->tp_dict == NULL) && unlikely(PyType_Ready(tp) < 0)) { + return 0; + } + descr = _PyType_Lookup(tp, name); + if (likely(descr != NULL)) { + Py_INCREF(descr); +#if defined(Py_TPFLAGS_METHOD_DESCRIPTOR) && Py_TPFLAGS_METHOD_DESCRIPTOR + if (__Pyx_PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_METHOD_DESCRIPTOR)) +#else + #ifdef __Pyx_CyFunction_USED + if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type) || __Pyx_CyFunction_Check(descr))) + #else + if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type))) + #endif +#endif + { + meth_found = 1; + } else { + f = Py_TYPE(descr)->tp_descr_get; + if (f != NULL && PyDescr_IsData(descr)) { + attr = f(descr, obj, (PyObject *)Py_TYPE(obj)); + Py_DECREF(descr); + goto try_unpack; + } + } + } + dictptr = _PyObject_GetDictPtr(obj); + if (dictptr != NULL && (dict = *dictptr) != NULL) { + Py_INCREF(dict); + attr = __Pyx_PyDict_GetItemStr(dict, name); + if (attr != NULL) { + Py_INCREF(attr); + Py_DECREF(dict); + Py_XDECREF(descr); + goto try_unpack; + } + Py_DECREF(dict); + } + if (meth_found) { + *method = descr; + return 1; + } + if (f != NULL) { + attr = f(descr, obj, (PyObject *)Py_TYPE(obj)); + Py_DECREF(descr); + goto try_unpack; + } + if (likely(descr != NULL)) { + *method = descr; + return 0; + } + type_name = __Pyx_PyType_GetFullyQualifiedName(tp); + PyErr_Format(PyExc_AttributeError, + "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'", + type_name, name); + __Pyx_DECREF_TypeName(type_name); + return 0; +#else + attr = __Pyx_PyObject_GetAttrStr(obj, name); + goto try_unpack; +#endif +try_unpack: +#if CYTHON_UNPACK_METHODS + if (likely(attr) && PyMethod_Check(attr) && likely(PyMethod_GET_SELF(attr) == obj)) { + PyObject *function = PyMethod_GET_FUNCTION(attr); + Py_INCREF(function); + Py_DECREF(attr); + *method = function; + return 1; + } +#endif + *method = attr; + return 0; +} + +/* PyObjectCallMethod1 */ +#if !(CYTHON_VECTORCALL && (__PYX_LIMITED_VERSION_HEX >= 0x030C0000 || (!CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX >= 0x03090000))) +static PyObject* __Pyx__PyObject_CallMethod1(PyObject* method, PyObject* arg) { + PyObject *result = __Pyx_PyObject_CallOneArg(method, arg); + Py_DECREF(method); + return result; +} +#endif +static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg) { +#if CYTHON_VECTORCALL && (__PYX_LIMITED_VERSION_HEX >= 0x030C0000 || (!CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX >= 0x03090000)) + PyObject *args[2] = {obj, arg}; + (void) __Pyx_PyObject_GetMethod; + (void) __Pyx_PyObject_CallOneArg; + (void) __Pyx_PyObject_Call2Args; + return PyObject_VectorcallMethod(method_name, args, 2 | PY_VECTORCALL_ARGUMENTS_OFFSET, NULL); +#else + PyObject *method = NULL, *result; + int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method); + if (likely(is_method)) { + result = __Pyx_PyObject_Call2Args(method, obj, arg); + Py_DECREF(method); + return result; + } + if (unlikely(!method)) return NULL; + return __Pyx__PyObject_CallMethod1(method, arg); +#endif +} + +/* append */ +static CYTHON_INLINE int __Pyx_PyObject_Append(PyObject* L, PyObject* x) { + if (likely(PyList_CheckExact(L))) { + if (unlikely(__Pyx_PyList_Append(L, x) < 0)) return -1; + } else { + PyObject* retval = __Pyx_PyObject_CallMethod1(L, __pyx_mstate_global->__pyx_n_u_append, x); + if (unlikely(!retval)) + return -1; + Py_DECREF(retval); + } + return 0; +} + +/* DictGetItem */ +#if !CYTHON_COMPILING_IN_PYPY +static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { + PyObject *value; + if (unlikely(__Pyx_PyDict_GetItemRef(d, key, &value) == 0)) { // no value, no error + if (unlikely(PyTuple_Check(key))) { + PyObject* args = PyTuple_Pack(1, key); + if (likely(args)) { + PyErr_SetObject(PyExc_KeyError, args); + Py_DECREF(args); + } + } else { + PyErr_SetObject(PyExc_KeyError, key); + } + } + return value; +} +#endif + +/* PyObjectCallNoArg */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { + PyObject *arg[2] = {NULL, NULL}; + return __Pyx_PyObject_FastCall(func, arg + 1, 0 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET); +} + +/* pyfrozenset_new */ +static CYTHON_INLINE PyObject* __Pyx_PyFrozenSet_New(PyObject* it) { + if (it) { + PyObject* result; +#if CYTHON_COMPILING_IN_PYPY + PyObject* args; + args = PyTuple_Pack(1, it); + if (unlikely(!args)) + return NULL; + result = PyObject_Call((PyObject*)&PyFrozenSet_Type, args, NULL); + Py_DECREF(args); + return result; +#else + if (PyFrozenSet_CheckExact(it)) { + Py_INCREF(it); + return it; + } + result = PyFrozenSet_New(it); + if (unlikely(!result)) + return NULL; + if ((__PYX_LIMITED_VERSION_HEX >= 0x030A0000) +#if CYTHON_COMPILING_IN_LIMITED_API + || __Pyx_get_runtime_version() >= 0x030A0000 +#endif + ) + return result; + { + Py_ssize_t size = __Pyx_PySet_GET_SIZE(result); + if (likely(size > 0)) + return result; +#if !CYTHON_ASSUME_SAFE_SIZE + if (unlikely(size < 0)) { + Py_DECREF(result); + return NULL; + } +#endif + } + Py_DECREF(result); +#endif + } + return __Pyx_PyObject_CallNoArg((PyObject*) &PyFrozenSet_Type); +} + +/* CallTypeTraverse */ +#if !CYTHON_USE_TYPE_SPECS || (!CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x03090000) +#else +static int __Pyx_call_type_traverse(PyObject *o, int always_call, visitproc visit, void *arg) { + #if CYTHON_COMPILING_IN_LIMITED_API && __PYX_LIMITED_VERSION_HEX < 0x03090000 + if (__Pyx_get_runtime_version() < 0x03090000) return 0; + #endif + if (!always_call) { + PyTypeObject *base = __Pyx_PyObject_GetSlot(o, tp_base, PyTypeObject*); + unsigned long flags = PyType_GetFlags(base); + if (flags & Py_TPFLAGS_HEAPTYPE) { + return 0; + } + } + Py_VISIT((PyObject*)Py_TYPE(o)); + return 0; +} +#endif + +/* LimitedApiGetTypeDict */ +#if CYTHON_COMPILING_IN_LIMITED_API +static Py_ssize_t __Pyx_GetTypeDictOffset(void) { + PyObject *tp_dictoffset_o; + Py_ssize_t tp_dictoffset; + tp_dictoffset_o = PyObject_GetAttrString((PyObject*)(&PyType_Type), "__dictoffset__"); + if (unlikely(!tp_dictoffset_o)) return -1; + tp_dictoffset = PyLong_AsSsize_t(tp_dictoffset_o); + Py_DECREF(tp_dictoffset_o); + if (unlikely(tp_dictoffset == 0)) { + PyErr_SetString( + PyExc_TypeError, + "'type' doesn't have a dictoffset"); + return -1; + } else if (unlikely(tp_dictoffset < 0)) { + PyErr_SetString( + PyExc_TypeError, + "'type' has an unexpected negative dictoffset. " + "Please report this as Cython bug"); + return -1; + } + return tp_dictoffset; +} +static PyObject *__Pyx_GetTypeDict(PyTypeObject *tp) { + static Py_ssize_t tp_dictoffset = 0; + if (unlikely(tp_dictoffset == 0)) { + tp_dictoffset = __Pyx_GetTypeDictOffset(); + if (unlikely(tp_dictoffset == -1 && PyErr_Occurred())) { + tp_dictoffset = 0; // try again next time? + return NULL; + } + } + return *(PyObject**)((char*)tp + tp_dictoffset); +} +#endif + +/* SetItemOnTypeDict */ +static int __Pyx__SetItemOnTypeDict(PyTypeObject *tp, PyObject *k, PyObject *v) { + int result; + PyObject *tp_dict; +#if CYTHON_COMPILING_IN_LIMITED_API + tp_dict = __Pyx_GetTypeDict(tp); + if (unlikely(!tp_dict)) return -1; +#else + tp_dict = tp->tp_dict; +#endif + result = PyDict_SetItem(tp_dict, k, v); + if (likely(!result)) { + PyType_Modified(tp); + if (unlikely(PyObject_HasAttr(v, __pyx_mstate_global->__pyx_n_u_set_name))) { + PyObject *setNameResult = PyObject_CallMethodObjArgs(v, __pyx_mstate_global->__pyx_n_u_set_name, (PyObject *) tp, k, NULL); + if (!setNameResult) return -1; + Py_DECREF(setNameResult); + } + } + return result; +} + +/* FixUpExtensionType */ +static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type) { +#if __PYX_LIMITED_VERSION_HEX > 0x030900B1 + CYTHON_UNUSED_VAR(spec); + CYTHON_UNUSED_VAR(type); + CYTHON_UNUSED_VAR(__Pyx__SetItemOnTypeDict); +#else + const PyType_Slot *slot = spec->slots; + int changed = 0; +#if !CYTHON_COMPILING_IN_LIMITED_API + while (slot && slot->slot && slot->slot != Py_tp_members) + slot++; + if (slot && slot->slot == Py_tp_members) { +#if !CYTHON_COMPILING_IN_CPYTHON + const +#endif // !CYTHON_COMPILING_IN_CPYTHON) + PyMemberDef *memb = (PyMemberDef*) slot->pfunc; + while (memb && memb->name) { + if (memb->name[0] == '_' && memb->name[1] == '_') { + if (strcmp(memb->name, "__weaklistoffset__") == 0) { + assert(memb->type == T_PYSSIZET); + assert(memb->flags == READONLY); + type->tp_weaklistoffset = memb->offset; + changed = 1; + } + else if (strcmp(memb->name, "__dictoffset__") == 0) { + assert(memb->type == T_PYSSIZET); + assert(memb->flags == READONLY); + type->tp_dictoffset = memb->offset; + changed = 1; + } +#if CYTHON_METH_FASTCALL + else if (strcmp(memb->name, "__vectorcalloffset__") == 0) { + assert(memb->type == T_PYSSIZET); + assert(memb->flags == READONLY); +#if PY_VERSION_HEX >= 0x030800b4 + type->tp_vectorcall_offset = memb->offset; +#else + type->tp_print = (printfunc) memb->offset; +#endif + changed = 1; + } +#endif // CYTHON_METH_FASTCALL +#if !CYTHON_COMPILING_IN_PYPY + else if (strcmp(memb->name, "__module__") == 0) { + PyObject *descr; + assert(memb->type == T_OBJECT); + assert(memb->flags == 0 || memb->flags == READONLY); + descr = PyDescr_NewMember(type, memb); + if (unlikely(!descr)) + return -1; + int set_item_result = PyDict_SetItem(type->tp_dict, PyDescr_NAME(descr), descr); + Py_DECREF(descr); + if (unlikely(set_item_result < 0)) { + return -1; + } + changed = 1; + } +#endif // !CYTHON_COMPILING_IN_PYPY + } + memb++; + } + } +#endif // !CYTHON_COMPILING_IN_LIMITED_API +#if !CYTHON_COMPILING_IN_PYPY + slot = spec->slots; + while (slot && slot->slot && slot->slot != Py_tp_getset) + slot++; + if (slot && slot->slot == Py_tp_getset) { + PyGetSetDef *getset = (PyGetSetDef*) slot->pfunc; + while (getset && getset->name) { + if (getset->name[0] == '_' && getset->name[1] == '_' && strcmp(getset->name, "__module__") == 0) { + PyObject *descr = PyDescr_NewGetSet(type, getset); + if (unlikely(!descr)) + return -1; + #if CYTHON_COMPILING_IN_LIMITED_API + PyObject *pyname = PyUnicode_FromString(getset->name); + if (unlikely(!pyname)) { + Py_DECREF(descr); + return -1; + } + int set_item_result = __Pyx_SetItemOnTypeDict(type, pyname, descr); + Py_DECREF(pyname); + #else + CYTHON_UNUSED_VAR(__Pyx__SetItemOnTypeDict); + int set_item_result = PyDict_SetItem(type->tp_dict, PyDescr_NAME(descr), descr); + #endif + Py_DECREF(descr); + if (unlikely(set_item_result < 0)) { + return -1; + } + changed = 1; + } + ++getset; + } + } +#endif // !CYTHON_COMPILING_IN_PYPY + if (changed) + PyType_Modified(type); +#endif // PY_VERSION_HEX > 0x030900B1 + return 0; +} + +/* PyObjectCallMethod0 */ +static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name) { +#if CYTHON_VECTORCALL && (__PYX_LIMITED_VERSION_HEX >= 0x030C0000 || (!CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX >= 0x03090000)) + PyObject *args[1] = {obj}; + (void) __Pyx_PyObject_GetMethod; + (void) __Pyx_PyObject_CallOneArg; + (void) __Pyx_PyObject_CallNoArg; + return PyObject_VectorcallMethod(method_name, args, 1 | PY_VECTORCALL_ARGUMENTS_OFFSET, NULL); +#else + PyObject *method = NULL, *result = NULL; + int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method); + if (likely(is_method)) { + result = __Pyx_PyObject_CallOneArg(method, obj); + Py_DECREF(method); + return result; + } + if (unlikely(!method)) goto bad; + result = __Pyx_PyObject_CallNoArg(method); + Py_DECREF(method); +bad: + return result; +#endif +} + +/* ValidateBasesTuple */ +#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS +static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases) { + Py_ssize_t i, n; +#if CYTHON_ASSUME_SAFE_SIZE + n = PyTuple_GET_SIZE(bases); +#else + n = PyTuple_Size(bases); + if (unlikely(n < 0)) return -1; +#endif + for (i = 1; i < n; i++) + { + PyTypeObject *b; +#if CYTHON_AVOID_BORROWED_REFS + PyObject *b0 = PySequence_GetItem(bases, i); + if (!b0) return -1; +#elif CYTHON_ASSUME_SAFE_MACROS + PyObject *b0 = PyTuple_GET_ITEM(bases, i); +#else + PyObject *b0 = PyTuple_GetItem(bases, i); + if (!b0) return -1; +#endif + b = (PyTypeObject*) b0; + if (!__Pyx_PyType_HasFeature(b, Py_TPFLAGS_HEAPTYPE)) + { + __Pyx_TypeName b_name = __Pyx_PyType_GetFullyQualifiedName(b); + PyErr_Format(PyExc_TypeError, + "base class '" __Pyx_FMT_TYPENAME "' is not a heap type", b_name); + __Pyx_DECREF_TypeName(b_name); +#if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(b0); +#endif + return -1; + } + if (dictoffset == 0) + { + Py_ssize_t b_dictoffset = 0; +#if CYTHON_USE_TYPE_SLOTS + b_dictoffset = b->tp_dictoffset; +#else + PyObject *py_b_dictoffset = PyObject_GetAttrString((PyObject*)b, "__dictoffset__"); + if (!py_b_dictoffset) goto dictoffset_return; + b_dictoffset = PyLong_AsSsize_t(py_b_dictoffset); + Py_DECREF(py_b_dictoffset); + if (b_dictoffset == -1 && PyErr_Occurred()) goto dictoffset_return; +#endif + if (b_dictoffset) { + { + __Pyx_TypeName b_name = __Pyx_PyType_GetFullyQualifiedName(b); + PyErr_Format(PyExc_TypeError, + "extension type '%.200s' has no __dict__ slot, " + "but base type '" __Pyx_FMT_TYPENAME "' has: " + "either add 'cdef dict __dict__' to the extension type " + "or add '__slots__ = [...]' to the base type", + type_name, b_name); + __Pyx_DECREF_TypeName(b_name); + } +#if !CYTHON_USE_TYPE_SLOTS + dictoffset_return: +#endif +#if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(b0); +#endif + return -1; + } + } +#if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(b0); +#endif + } + return 0; +} +#endif + +/* PyType_Ready */ +CYTHON_UNUSED static int __Pyx_PyType_HasMultipleInheritance(PyTypeObject *t) { + while (t) { + PyObject *bases = __Pyx_PyType_GetSlot(t, tp_bases, PyObject*); + if (bases) { + return 1; + } + t = __Pyx_PyType_GetSlot(t, tp_base, PyTypeObject*); + } + return 0; +} +static int __Pyx_PyType_Ready(PyTypeObject *t) { +#if CYTHON_USE_TYPE_SPECS || !CYTHON_COMPILING_IN_CPYTHON || defined(PYSTON_MAJOR_VERSION) + (void)__Pyx_PyObject_CallMethod0; +#if CYTHON_USE_TYPE_SPECS + (void)__Pyx_validate_bases_tuple; +#endif + return PyType_Ready(t); +#else + int r; + if (!__Pyx_PyType_HasMultipleInheritance(t)) { + return PyType_Ready(t); + } + PyObject *bases = __Pyx_PyType_GetSlot(t, tp_bases, PyObject*); + if (bases && unlikely(__Pyx_validate_bases_tuple(t->tp_name, t->tp_dictoffset, bases) == -1)) + return -1; +#if !defined(PYSTON_MAJOR_VERSION) + { + int gc_was_enabled; + #if PY_VERSION_HEX >= 0x030A00b1 + gc_was_enabled = PyGC_Disable(); + (void)__Pyx_PyObject_CallMethod0; + #else + PyObject *ret, *py_status; + PyObject *gc = NULL; + #if (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM+0 >= 0x07030400) &&\ + !CYTHON_COMPILING_IN_GRAAL + gc = PyImport_GetModule(__pyx_mstate_global->__pyx_kp_u_gc); + #endif + if (unlikely(!gc)) gc = PyImport_Import(__pyx_mstate_global->__pyx_kp_u_gc); + if (unlikely(!gc)) return -1; + py_status = __Pyx_PyObject_CallMethod0(gc, __pyx_mstate_global->__pyx_kp_u_isenabled); + if (unlikely(!py_status)) { + Py_DECREF(gc); + return -1; + } + gc_was_enabled = __Pyx_PyObject_IsTrue(py_status); + Py_DECREF(py_status); + if (gc_was_enabled > 0) { + ret = __Pyx_PyObject_CallMethod0(gc, __pyx_mstate_global->__pyx_kp_u_disable); + if (unlikely(!ret)) { + Py_DECREF(gc); + return -1; + } + Py_DECREF(ret); + } else if (unlikely(gc_was_enabled == -1)) { + Py_DECREF(gc); + return -1; + } + #endif + t->tp_flags |= Py_TPFLAGS_HEAPTYPE; +#if PY_VERSION_HEX >= 0x030A0000 + t->tp_flags |= Py_TPFLAGS_IMMUTABLETYPE; +#endif +#else + (void)__Pyx_PyObject_CallMethod0; +#endif + r = PyType_Ready(t); +#if !defined(PYSTON_MAJOR_VERSION) + t->tp_flags &= ~Py_TPFLAGS_HEAPTYPE; + #if PY_VERSION_HEX >= 0x030A00b1 + if (gc_was_enabled) + PyGC_Enable(); + #else + if (gc_was_enabled) { + PyObject *tp, *v, *tb; + PyErr_Fetch(&tp, &v, &tb); + ret = __Pyx_PyObject_CallMethod0(gc, __pyx_mstate_global->__pyx_kp_u_enable); + if (likely(ret || r == -1)) { + Py_XDECREF(ret); + PyErr_Restore(tp, v, tb); + } else { + Py_XDECREF(tp); + Py_XDECREF(v); + Py_XDECREF(tb); + r = -1; + } + } + Py_DECREF(gc); + #endif + } +#endif + return r; +#endif +} + +/* SetVTable */ +static int __Pyx_SetVtable(PyTypeObject *type, void *vtable) { + PyObject *ob = PyCapsule_New(vtable, 0, 0); + if (unlikely(!ob)) + goto bad; +#if CYTHON_COMPILING_IN_LIMITED_API + if (unlikely(PyObject_SetAttr((PyObject *) type, __pyx_mstate_global->__pyx_n_u_pyx_vtable, ob) < 0)) +#else + if (unlikely(PyDict_SetItem(type->tp_dict, __pyx_mstate_global->__pyx_n_u_pyx_vtable, ob) < 0)) +#endif + goto bad; + Py_DECREF(ob); + return 0; +bad: + Py_XDECREF(ob); + return -1; +} + +/* GetVTable */ +static void* __Pyx_GetVtable(PyTypeObject *type) { + void* ptr; +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject *ob = PyObject_GetAttr((PyObject *)type, __pyx_mstate_global->__pyx_n_u_pyx_vtable); +#else + PyObject *ob = PyObject_GetItem(type->tp_dict, __pyx_mstate_global->__pyx_n_u_pyx_vtable); +#endif + if (!ob) + goto bad; + ptr = PyCapsule_GetPointer(ob, 0); + if (!ptr && !PyErr_Occurred()) + PyErr_SetString(PyExc_RuntimeError, "invalid vtable found for imported type"); + Py_DECREF(ob); + return ptr; +bad: + Py_XDECREF(ob); + return NULL; +} + +/* MergeVTables */ +static int __Pyx_MergeVtables(PyTypeObject *type) { + int i=0; + Py_ssize_t size; + void** base_vtables; + __Pyx_TypeName tp_base_name = NULL; + __Pyx_TypeName base_name = NULL; + void* unknown = (void*)-1; + PyObject* bases = __Pyx_PyType_GetSlot(type, tp_bases, PyObject*); + int base_depth = 0; + { + PyTypeObject* base = __Pyx_PyType_GetSlot(type, tp_base, PyTypeObject*); + while (base) { + base_depth += 1; + base = __Pyx_PyType_GetSlot(base, tp_base, PyTypeObject*); + } + } + base_vtables = (void**) PyMem_Malloc(sizeof(void*) * (size_t)(base_depth + 1)); + base_vtables[0] = unknown; +#if CYTHON_COMPILING_IN_LIMITED_API + size = PyTuple_Size(bases); + if (size < 0) goto other_failure; +#else + size = PyTuple_GET_SIZE(bases); +#endif + for (i = 1; i < size; i++) { + PyObject *basei; + void* base_vtable; +#if CYTHON_AVOID_BORROWED_REFS + basei = PySequence_GetItem(bases, i); + if (unlikely(!basei)) goto other_failure; +#elif !CYTHON_ASSUME_SAFE_MACROS + basei = PyTuple_GetItem(bases, i); + if (unlikely(!basei)) goto other_failure; +#else + basei = PyTuple_GET_ITEM(bases, i); +#endif + base_vtable = __Pyx_GetVtable((PyTypeObject*)basei); +#if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(basei); +#endif + if (base_vtable != NULL) { + int j; + PyTypeObject* base = __Pyx_PyType_GetSlot(type, tp_base, PyTypeObject*); + for (j = 0; j < base_depth; j++) { + if (base_vtables[j] == unknown) { + base_vtables[j] = __Pyx_GetVtable(base); + base_vtables[j + 1] = unknown; + } + if (base_vtables[j] == base_vtable) { + break; + } else if (base_vtables[j] == NULL) { + goto bad; + } + base = __Pyx_PyType_GetSlot(base, tp_base, PyTypeObject*); + } + } + } + PyErr_Clear(); + PyMem_Free(base_vtables); + return 0; +bad: + { + PyTypeObject* basei = NULL; + PyTypeObject* tp_base = __Pyx_PyType_GetSlot(type, tp_base, PyTypeObject*); + tp_base_name = __Pyx_PyType_GetFullyQualifiedName(tp_base); +#if CYTHON_AVOID_BORROWED_REFS + basei = (PyTypeObject*)PySequence_GetItem(bases, i); + if (unlikely(!basei)) goto really_bad; +#elif !CYTHON_ASSUME_SAFE_MACROS + basei = (PyTypeObject*)PyTuple_GetItem(bases, i); + if (unlikely(!basei)) goto really_bad; +#else + basei = (PyTypeObject*)PyTuple_GET_ITEM(bases, i); +#endif + base_name = __Pyx_PyType_GetFullyQualifiedName(basei); +#if CYTHON_AVOID_BORROWED_REFS + Py_DECREF(basei); +#endif + } + PyErr_Format(PyExc_TypeError, + "multiple bases have vtable conflict: '" __Pyx_FMT_TYPENAME "' and '" __Pyx_FMT_TYPENAME "'", tp_base_name, base_name); +#if CYTHON_AVOID_BORROWED_REFS || !CYTHON_ASSUME_SAFE_MACROS +really_bad: // bad has failed! +#endif + __Pyx_DECREF_TypeName(tp_base_name); + __Pyx_DECREF_TypeName(base_name); +#if CYTHON_COMPILING_IN_LIMITED_API || CYTHON_AVOID_BORROWED_REFS || !CYTHON_ASSUME_SAFE_MACROS +other_failure: +#endif + PyMem_Free(base_vtables); + return -1; +} + +/* DelItemOnTypeDict */ +static int __Pyx__DelItemOnTypeDict(PyTypeObject *tp, PyObject *k) { + int result; + PyObject *tp_dict; +#if CYTHON_COMPILING_IN_LIMITED_API + tp_dict = __Pyx_GetTypeDict(tp); + if (unlikely(!tp_dict)) return -1; +#else + tp_dict = tp->tp_dict; +#endif + result = PyDict_DelItem(tp_dict, k); + if (likely(!result)) PyType_Modified(tp); + return result; +} + +/* SetupReduce */ +static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { + int ret; + PyObject *name_attr; + name_attr = __Pyx_PyObject_GetAttrStrNoError(meth, __pyx_mstate_global->__pyx_n_u_name_2); + if (likely(name_attr)) { + ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); + } else { + ret = -1; + } + if (unlikely(ret < 0)) { + PyErr_Clear(); + ret = 0; + } + Py_XDECREF(name_attr); + return ret; +} +static int __Pyx_setup_reduce(PyObject* type_obj) { + int ret = 0; + PyObject *object_reduce = NULL; + PyObject *object_getstate = NULL; + PyObject *object_reduce_ex = NULL; + PyObject *reduce = NULL; + PyObject *reduce_ex = NULL; + PyObject *reduce_cython = NULL; + PyObject *setstate = NULL; + PyObject *setstate_cython = NULL; + PyObject *getstate = NULL; +#if CYTHON_USE_PYTYPE_LOOKUP + getstate = _PyType_Lookup((PyTypeObject*)type_obj, __pyx_mstate_global->__pyx_n_u_getstate); +#else + getstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_mstate_global->__pyx_n_u_getstate); + if (!getstate && PyErr_Occurred()) { + goto __PYX_BAD; + } +#endif + if (getstate) { +#if CYTHON_USE_PYTYPE_LOOKUP + object_getstate = _PyType_Lookup(&PyBaseObject_Type, __pyx_mstate_global->__pyx_n_u_getstate); +#else + object_getstate = __Pyx_PyObject_GetAttrStrNoError((PyObject*)&PyBaseObject_Type, __pyx_mstate_global->__pyx_n_u_getstate); + if (!object_getstate && PyErr_Occurred()) { + goto __PYX_BAD; + } +#endif + if (object_getstate != getstate) { + goto __PYX_GOOD; + } + } +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_mstate_global->__pyx_n_u_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; +#else + object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_mstate_global->__pyx_n_u_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; +#endif + reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_mstate_global->__pyx_n_u_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD; + if (reduce_ex == object_reduce_ex) { +#if CYTHON_USE_PYTYPE_LOOKUP + object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_mstate_global->__pyx_n_u_reduce); if (!object_reduce) goto __PYX_BAD; +#else + object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_mstate_global->__pyx_n_u_reduce); if (!object_reduce) goto __PYX_BAD; +#endif + reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_mstate_global->__pyx_n_u_reduce); if (unlikely(!reduce)) goto __PYX_BAD; + if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_mstate_global->__pyx_n_u_reduce_cython)) { + reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_mstate_global->__pyx_n_u_reduce_cython); + if (likely(reduce_cython)) { + ret = __Pyx_SetItemOnTypeDict((PyTypeObject*)type_obj, __pyx_mstate_global->__pyx_n_u_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + ret = __Pyx_DelItemOnTypeDict((PyTypeObject*)type_obj, __pyx_mstate_global->__pyx_n_u_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + } else if (reduce == object_reduce || PyErr_Occurred()) { + goto __PYX_BAD; + } + setstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_mstate_global->__pyx_n_u_setstate); + if (!setstate) PyErr_Clear(); + if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_mstate_global->__pyx_n_u_setstate_cython)) { + setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_mstate_global->__pyx_n_u_setstate_cython); + if (likely(setstate_cython)) { + ret = __Pyx_SetItemOnTypeDict((PyTypeObject*)type_obj, __pyx_mstate_global->__pyx_n_u_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + ret = __Pyx_DelItemOnTypeDict((PyTypeObject*)type_obj, __pyx_mstate_global->__pyx_n_u_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; + } else if (!setstate || PyErr_Occurred()) { + goto __PYX_BAD; + } + } + PyType_Modified((PyTypeObject*)type_obj); + } + } + goto __PYX_GOOD; +__PYX_BAD: + if (!PyErr_Occurred()) { + __Pyx_TypeName type_obj_name = + __Pyx_PyType_GetFullyQualifiedName((PyTypeObject*)type_obj); + PyErr_Format(PyExc_RuntimeError, + "Unable to initialize pickling for " __Pyx_FMT_TYPENAME, type_obj_name); + __Pyx_DECREF_TypeName(type_obj_name); + } + ret = -1; +__PYX_GOOD: +#if !CYTHON_USE_PYTYPE_LOOKUP + Py_XDECREF(object_reduce); + Py_XDECREF(object_reduce_ex); + Py_XDECREF(object_getstate); + Py_XDECREF(getstate); +#endif + Py_XDECREF(reduce); + Py_XDECREF(reduce_ex); + Py_XDECREF(reduce_cython); + Py_XDECREF(setstate); + Py_XDECREF(setstate_cython); + return ret; +} + +/* ListPack */ +static PyObject *__Pyx_PyList_Pack(Py_ssize_t n, ...) { + va_list va; + PyObject *l = PyList_New(n); + va_start(va, n); + if (unlikely(!l)) goto end; + for (Py_ssize_t i=0; i= 0x030d0000 + PyDict_SetDefaultRef(d, key, default_value, &value); +#else + value = PyDict_SetDefault(d, key, default_value); + if (unlikely(!value)) return NULL; + Py_INCREF(value); +#endif + return value; +} + +/* FetchCommonType */ +#if __PYX_LIMITED_VERSION_HEX < 0x030C0000 +static PyObject* __Pyx_PyType_FromMetaclass(PyTypeObject *metaclass, PyObject *module, PyType_Spec *spec, PyObject *bases) { + PyObject *result = __Pyx_PyType_FromModuleAndSpec(module, spec, bases); + if (result && metaclass) { + PyObject *old_tp = (PyObject*)Py_TYPE(result); + Py_INCREF((PyObject*)metaclass); +#if __PYX_LIMITED_VERSION_HEX >= 0x03090000 + Py_SET_TYPE(result, metaclass); +#else + result->ob_type = metaclass; +#endif + Py_DECREF(old_tp); + } + return result; +} +#else +#define __Pyx_PyType_FromMetaclass(me, mo, s, b) PyType_FromMetaclass(me, mo, s, b) +#endif +static int __Pyx_VerifyCachedType(PyObject *cached_type, + const char *name, + Py_ssize_t expected_basicsize) { + Py_ssize_t basicsize; + if (!PyType_Check(cached_type)) { + PyErr_Format(PyExc_TypeError, + "Shared Cython type %.200s is not a type object", name); + return -1; + } + if (expected_basicsize == 0) { + return 0; // size is inherited, nothing useful to check + } +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject *py_basicsize; + py_basicsize = PyObject_GetAttrString(cached_type, "__basicsize__"); + if (unlikely(!py_basicsize)) return -1; + basicsize = PyLong_AsSsize_t(py_basicsize); + Py_DECREF(py_basicsize); + py_basicsize = NULL; + if (unlikely(basicsize == (Py_ssize_t)-1) && PyErr_Occurred()) return -1; +#else + basicsize = ((PyTypeObject*) cached_type)->tp_basicsize; +#endif + if (basicsize != expected_basicsize) { + PyErr_Format(PyExc_TypeError, + "Shared Cython type %.200s has the wrong size, try recompiling", + name); + return -1; + } + return 0; +} +static PyTypeObject *__Pyx_FetchCommonTypeFromSpec(PyTypeObject *metaclass, PyObject *module, PyType_Spec *spec, PyObject *bases) { + PyObject *abi_module = NULL, *cached_type = NULL, *abi_module_dict, *new_cached_type, *py_object_name; + int get_item_ref_result; + const char* object_name = strrchr(spec->name, '.'); + object_name = object_name ? object_name+1 : spec->name; + py_object_name = PyUnicode_FromString(object_name); + if (!py_object_name) return NULL; + abi_module = __Pyx_FetchSharedCythonABIModule(); + if (!abi_module) goto done; + abi_module_dict = PyModule_GetDict(abi_module); + if (!abi_module_dict) goto done; + get_item_ref_result = __Pyx_PyDict_GetItemRef(abi_module_dict, py_object_name, &cached_type); + if (get_item_ref_result == 1) { + if (__Pyx_VerifyCachedType( + cached_type, + object_name, + spec->basicsize) < 0) { + goto bad; + } + goto done; + } else if (unlikely(get_item_ref_result == -1)) { + goto bad; + } + CYTHON_UNUSED_VAR(module); + cached_type = __Pyx_PyType_FromMetaclass(metaclass, abi_module, spec, bases); + if (unlikely(!cached_type)) goto bad; + if (unlikely(__Pyx_fix_up_extension_type_from_spec(spec, (PyTypeObject *) cached_type) < 0)) goto bad; + new_cached_type = __Pyx_PyDict_SetDefault(abi_module_dict, py_object_name, cached_type, 1); + if (unlikely(new_cached_type != cached_type)) { + if (unlikely(!new_cached_type)) goto bad; + Py_DECREF(cached_type); + cached_type = new_cached_type; + if (__Pyx_VerifyCachedType( + cached_type, + object_name, + spec->basicsize) < 0) { + goto bad; + } + goto done; + } else { + Py_DECREF(new_cached_type); + } +done: + Py_XDECREF(abi_module); + Py_DECREF(py_object_name); + assert(cached_type == NULL || PyType_Check(cached_type)); + return (PyTypeObject *) cached_type; +bad: + Py_XDECREF(cached_type); + cached_type = NULL; + goto done; +} + +/* CommonTypesMetaclass */ +static PyObject* __pyx_CommonTypesMetaclass_get_module(CYTHON_UNUSED PyObject *self, CYTHON_UNUSED void* context) { + return PyUnicode_FromString(__PYX_ABI_MODULE_NAME); +} +static PyGetSetDef __pyx_CommonTypesMetaclass_getset[] = { + {"__module__", __pyx_CommonTypesMetaclass_get_module, NULL, NULL, NULL}, + {0, 0, 0, 0, 0} +}; +static PyType_Slot __pyx_CommonTypesMetaclass_slots[] = { + {Py_tp_getset, (void *)__pyx_CommonTypesMetaclass_getset}, + {0, 0} +}; +static PyType_Spec __pyx_CommonTypesMetaclass_spec = { + __PYX_TYPE_MODULE_PREFIX "_common_types_metatype", + 0, + 0, +#if PY_VERSION_HEX >= 0x030A0000 + Py_TPFLAGS_IMMUTABLETYPE | + Py_TPFLAGS_DISALLOW_INSTANTIATION | +#endif + Py_TPFLAGS_DEFAULT, + __pyx_CommonTypesMetaclass_slots +}; +static int __pyx_CommonTypesMetaclass_init(PyObject *module) { + __pyx_mstatetype *mstate = __Pyx_PyModule_GetState(module); + PyObject *bases = PyTuple_Pack(1, &PyType_Type); + if (unlikely(!bases)) { + return -1; + } + mstate->__pyx_CommonTypesMetaclassType = __Pyx_FetchCommonTypeFromSpec(NULL, module, &__pyx_CommonTypesMetaclass_spec, bases); + Py_DECREF(bases); + if (unlikely(mstate->__pyx_CommonTypesMetaclassType == NULL)) { + return -1; + } + return 0; +} + +/* PyMethodNew */ +#if CYTHON_COMPILING_IN_LIMITED_API +static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, PyObject *typ) { + PyObject *result; + CYTHON_UNUSED_VAR(typ); + if (!self) + return __Pyx_NewRef(func); + #if __PYX_LIMITED_VERSION_HEX >= 0x030C0000 + { + PyObject *args[] = {func, self}; + result = PyObject_Vectorcall(__pyx_mstate_global->__Pyx_CachedMethodType, args, 2, NULL); + } + #else + result = PyObject_CallFunctionObjArgs(__pyx_mstate_global->__Pyx_CachedMethodType, func, self, NULL); + #endif + return result; +} +#else +static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, PyObject *typ) { + CYTHON_UNUSED_VAR(typ); + if (!self) + return __Pyx_NewRef(func); + return PyMethod_New(func, self); +} +#endif + +/* PyVectorcallFastCallDict */ +#if CYTHON_METH_FASTCALL && (CYTHON_VECTORCALL || CYTHON_BACKPORT_VECTORCALL) +static PyObject *__Pyx_PyVectorcall_FastCallDict_kw(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw) +{ + PyObject *res = NULL; + PyObject *kwnames; + PyObject **newargs; + PyObject **kwvalues; + Py_ssize_t i, pos; + size_t j; + PyObject *key, *value; + unsigned long keys_are_strings; + #if !CYTHON_ASSUME_SAFE_SIZE + Py_ssize_t nkw = PyDict_Size(kw); + if (unlikely(nkw == -1)) return NULL; + #else + Py_ssize_t nkw = PyDict_GET_SIZE(kw); + #endif + newargs = (PyObject **)PyMem_Malloc((nargs + (size_t)nkw) * sizeof(args[0])); + if (unlikely(newargs == NULL)) { + PyErr_NoMemory(); + return NULL; + } + for (j = 0; j < nargs; j++) newargs[j] = args[j]; + kwnames = PyTuple_New(nkw); + if (unlikely(kwnames == NULL)) { + PyMem_Free(newargs); + return NULL; + } + kwvalues = newargs + nargs; + pos = i = 0; + keys_are_strings = Py_TPFLAGS_UNICODE_SUBCLASS; + while (PyDict_Next(kw, &pos, &key, &value)) { + keys_are_strings &= + #if CYTHON_COMPILING_IN_LIMITED_API + PyType_GetFlags(Py_TYPE(key)); + #else + Py_TYPE(key)->tp_flags; + #endif + Py_INCREF(key); + Py_INCREF(value); + #if !CYTHON_ASSUME_SAFE_MACROS + if (unlikely(PyTuple_SetItem(kwnames, i, key) < 0)) goto cleanup; + #else + PyTuple_SET_ITEM(kwnames, i, key); + #endif + kwvalues[i] = value; + i++; + } + if (unlikely(!keys_are_strings)) { + PyErr_SetString(PyExc_TypeError, "keywords must be strings"); + goto cleanup; + } + res = vc(func, newargs, nargs, kwnames); +cleanup: + Py_DECREF(kwnames); + for (i = 0; i < nkw; i++) + Py_DECREF(kwvalues[i]); + PyMem_Free(newargs); + return res; +} +static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw) +{ + Py_ssize_t kw_size = + likely(kw == NULL) ? + 0 : +#if !CYTHON_ASSUME_SAFE_SIZE + PyDict_Size(kw); +#else + PyDict_GET_SIZE(kw); +#endif + if (kw_size == 0) { + return vc(func, args, nargs, NULL); + } +#if !CYTHON_ASSUME_SAFE_SIZE + else if (unlikely(kw_size == -1)) { + return NULL; + } +#endif + return __Pyx_PyVectorcall_FastCallDict_kw(func, vc, args, nargs, kw); +} +#endif + +/* CythonFunctionShared */ +#if CYTHON_COMPILING_IN_LIMITED_API +static CYTHON_INLINE int __Pyx__IsSameCyOrCFunctionNoMethod(PyObject *func, void (*cfunc)(void)) { + if (__Pyx_CyFunction_Check(func)) { + return PyCFunction_GetFunction(((__pyx_CyFunctionObject*)func)->func) == (PyCFunction) cfunc; + } else if (PyCFunction_Check(func)) { + return PyCFunction_GetFunction(func) == (PyCFunction) cfunc; + } + return 0; +} +static CYTHON_INLINE int __Pyx__IsSameCyOrCFunction(PyObject *func, void (*cfunc)(void)) { + if ((PyObject*)Py_TYPE(func) == __pyx_mstate_global->__Pyx_CachedMethodType) { + int result; + PyObject *newFunc = PyObject_GetAttr(func, __pyx_mstate_global->__pyx_n_u_func); + if (unlikely(!newFunc)) { + PyErr_Clear(); // It's only an optimization, so don't throw an error + return 0; + } + result = __Pyx__IsSameCyOrCFunctionNoMethod(newFunc, cfunc); + Py_DECREF(newFunc); + return result; + } + return __Pyx__IsSameCyOrCFunctionNoMethod(func, cfunc); +} +#else +static CYTHON_INLINE int __Pyx__IsSameCyOrCFunction(PyObject *func, void (*cfunc)(void)) { + if (PyMethod_Check(func)) { + func = PyMethod_GET_FUNCTION(func); + } + return __Pyx_CyOrPyCFunction_Check(func) && __Pyx_CyOrPyCFunction_GET_FUNCTION(func) == (PyCFunction) cfunc; +} +#endif +static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj) { +#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + __Pyx_Py_XDECREF_SET( + __Pyx_CyFunction_GetClassObj(f), + ((classobj) ? __Pyx_NewRef(classobj) : NULL)); +#else + __Pyx_Py_XDECREF_SET( + ((PyCMethodObject *) (f))->mm_class, + (PyTypeObject*)((classobj) ? __Pyx_NewRef(classobj) : NULL)); +#endif +} +static PyObject * +__Pyx_CyFunction_get_doc_locked(__pyx_CyFunctionObject *op) +{ + if (unlikely(op->func_doc == NULL)) { +#if CYTHON_COMPILING_IN_LIMITED_API + op->func_doc = PyObject_GetAttrString(op->func, "__doc__"); + if (unlikely(!op->func_doc)) return NULL; +#else + if (((PyCFunctionObject*)op)->m_ml->ml_doc) { + op->func_doc = PyUnicode_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc); + if (unlikely(op->func_doc == NULL)) + return NULL; + } else { + Py_INCREF(Py_None); + return Py_None; + } +#endif + } + Py_INCREF(op->func_doc); + return op->func_doc; +} +static PyObject * +__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, void *closure) { + PyObject *result; + CYTHON_UNUSED_VAR(closure); + __Pyx_BEGIN_CRITICAL_SECTION(op); + result = __Pyx_CyFunction_get_doc_locked(op); + __Pyx_END_CRITICAL_SECTION(); + return result; +} +static int +__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (value == NULL) { + value = Py_None; + } + Py_INCREF(value); + __Pyx_BEGIN_CRITICAL_SECTION(op); + __Pyx_Py_XDECREF_SET(op->func_doc, value); + __Pyx_END_CRITICAL_SECTION(); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_name_locked(__pyx_CyFunctionObject *op) +{ + if (unlikely(op->func_name == NULL)) { +#if CYTHON_COMPILING_IN_LIMITED_API + op->func_name = PyObject_GetAttrString(op->func, "__name__"); +#else + op->func_name = PyUnicode_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name); +#endif + if (unlikely(op->func_name == NULL)) + return NULL; + } + Py_INCREF(op->func_name); + return op->func_name; +} +static PyObject * +__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, void *context) +{ + PyObject *result = NULL; + CYTHON_UNUSED_VAR(context); + __Pyx_BEGIN_CRITICAL_SECTION(op); + result = __Pyx_CyFunction_get_name_locked(op); + __Pyx_END_CRITICAL_SECTION(); + return result; +} +static int +__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (unlikely(value == NULL || !PyUnicode_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "__name__ must be set to a string object"); + return -1; + } + Py_INCREF(value); + __Pyx_BEGIN_CRITICAL_SECTION(op); + __Pyx_Py_XDECREF_SET(op->func_name, value); + __Pyx_END_CRITICAL_SECTION(); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(context); + PyObject *result; + __Pyx_BEGIN_CRITICAL_SECTION(op); + Py_INCREF(op->func_qualname); + result = op->func_qualname; + __Pyx_END_CRITICAL_SECTION(); + return result; +} +static int +__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (unlikely(value == NULL || !PyUnicode_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "__qualname__ must be set to a string object"); + return -1; + } + Py_INCREF(value); + __Pyx_BEGIN_CRITICAL_SECTION(op); + __Pyx_Py_XDECREF_SET(op->func_qualname, value); + __Pyx_END_CRITICAL_SECTION(); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_dict_locked(__pyx_CyFunctionObject *op) +{ + if (unlikely(op->func_dict == NULL)) { + op->func_dict = PyDict_New(); + if (unlikely(op->func_dict == NULL)) + return NULL; + } + Py_INCREF(op->func_dict); + return op->func_dict; +} +static PyObject * +__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(context); + PyObject *result; + __Pyx_BEGIN_CRITICAL_SECTION(op); + result = __Pyx_CyFunction_get_dict_locked(op); + __Pyx_END_CRITICAL_SECTION(); + return result; +} +static int +__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value, void *context) +{ + CYTHON_UNUSED_VAR(context); + if (unlikely(value == NULL)) { + PyErr_SetString(PyExc_TypeError, + "function's dictionary may not be deleted"); + return -1; + } + if (unlikely(!PyDict_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "setting function's dictionary to a non-dict"); + return -1; + } + Py_INCREF(value); + __Pyx_BEGIN_CRITICAL_SECTION(op); + __Pyx_Py_XDECREF_SET(op->func_dict, value); + __Pyx_END_CRITICAL_SECTION(); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(context); + Py_INCREF(op->func_globals); + return op->func_globals; +} +static PyObject * +__Pyx_CyFunction_get_closure(__pyx_CyFunctionObject *op, void *context) +{ + CYTHON_UNUSED_VAR(op); + CYTHON_UNUSED_VAR(context); + Py_INCREF(Py_None); + return Py_None; +} +static PyObject * +__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op, void *context) +{ + PyObject* result = (op->func_code) ? op->func_code : Py_None; + CYTHON_UNUSED_VAR(context); + Py_INCREF(result); + return result; +} +static int +__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) { + int result = 0; + PyObject *res = op->defaults_getter((PyObject *) op); + if (unlikely(!res)) + return -1; + #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + op->defaults_tuple = PyTuple_GET_ITEM(res, 0); + Py_INCREF(op->defaults_tuple); + op->defaults_kwdict = PyTuple_GET_ITEM(res, 1); + Py_INCREF(op->defaults_kwdict); + #else + op->defaults_tuple = __Pyx_PySequence_ITEM(res, 0); + if (unlikely(!op->defaults_tuple)) result = -1; + else { + op->defaults_kwdict = __Pyx_PySequence_ITEM(res, 1); + if (unlikely(!op->defaults_kwdict)) result = -1; + } + #endif + Py_DECREF(res); + return result; +} +static int +__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + if (!value) { + value = Py_None; + } else if (unlikely(value != Py_None && !PyTuple_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "__defaults__ must be set to a tuple object"); + return -1; + } + PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__defaults__ will not " + "currently affect the values used in function calls", 1); + Py_INCREF(value); + __Pyx_BEGIN_CRITICAL_SECTION(op); + __Pyx_Py_XDECREF_SET(op->defaults_tuple, value); + __Pyx_END_CRITICAL_SECTION(); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_defaults_locked(__pyx_CyFunctionObject *op) { + PyObject* result = op->defaults_tuple; + if (unlikely(!result)) { + if (op->defaults_getter) { + if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL; + result = op->defaults_tuple; + } else { + result = Py_None; + } + } + Py_INCREF(result); + return result; +} +static PyObject * +__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op, void *context) { + PyObject* result = NULL; + CYTHON_UNUSED_VAR(context); + __Pyx_BEGIN_CRITICAL_SECTION(op); + result = __Pyx_CyFunction_get_defaults_locked(op); + __Pyx_END_CRITICAL_SECTION(); + return result; +} +static int +__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + if (!value) { + value = Py_None; + } else if (unlikely(value != Py_None && !PyDict_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "__kwdefaults__ must be set to a dict object"); + return -1; + } + PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__kwdefaults__ will not " + "currently affect the values used in function calls", 1); + Py_INCREF(value); + __Pyx_BEGIN_CRITICAL_SECTION(op); + __Pyx_Py_XDECREF_SET(op->defaults_kwdict, value); + __Pyx_END_CRITICAL_SECTION(); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_kwdefaults_locked(__pyx_CyFunctionObject *op) { + PyObject* result = op->defaults_kwdict; + if (unlikely(!result)) { + if (op->defaults_getter) { + if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL; + result = op->defaults_kwdict; + } else { + result = Py_None; + } + } + Py_INCREF(result); + return result; +} +static PyObject * +__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op, void *context) { + PyObject* result; + CYTHON_UNUSED_VAR(context); + __Pyx_BEGIN_CRITICAL_SECTION(op); + result = __Pyx_CyFunction_get_kwdefaults_locked(op); + __Pyx_END_CRITICAL_SECTION(); + return result; +} +static int +__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + if (!value || value == Py_None) { + value = NULL; + } else if (unlikely(!PyDict_Check(value))) { + PyErr_SetString(PyExc_TypeError, + "__annotations__ must be set to a dict object"); + return -1; + } + Py_XINCREF(value); + __Pyx_BEGIN_CRITICAL_SECTION(op); + __Pyx_Py_XDECREF_SET(op->func_annotations, value); + __Pyx_END_CRITICAL_SECTION(); + return 0; +} +static PyObject * +__Pyx_CyFunction_get_annotations_locked(__pyx_CyFunctionObject *op) { + PyObject* result = op->func_annotations; + if (unlikely(!result)) { + result = PyDict_New(); + if (unlikely(!result)) return NULL; + op->func_annotations = result; + } + Py_INCREF(result); + return result; +} +static PyObject * +__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op, void *context) { + PyObject *result; + CYTHON_UNUSED_VAR(context); + __Pyx_BEGIN_CRITICAL_SECTION(op); + result = __Pyx_CyFunction_get_annotations_locked(op); + __Pyx_END_CRITICAL_SECTION(); + return result; +} +static PyObject * +__Pyx_CyFunction_get_is_coroutine_value(__pyx_CyFunctionObject *op) { + int is_coroutine = op->flags & __Pyx_CYFUNCTION_COROUTINE; + if (is_coroutine) { + PyObject *is_coroutine_value, *module, *fromlist, *marker = __pyx_mstate_global->__pyx_n_u_is_coroutine; + fromlist = PyList_New(1); + if (unlikely(!fromlist)) return NULL; + Py_INCREF(marker); +#if CYTHON_ASSUME_SAFE_MACROS + PyList_SET_ITEM(fromlist, 0, marker); +#else + if (unlikely(PyList_SetItem(fromlist, 0, marker) < 0)) { + Py_DECREF(marker); + Py_DECREF(fromlist); + return NULL; + } +#endif + module = PyImport_ImportModuleLevelObject(__pyx_mstate_global->__pyx_n_u_asyncio_coroutines, NULL, NULL, fromlist, 0); + Py_DECREF(fromlist); + if (unlikely(!module)) goto ignore; + is_coroutine_value = __Pyx_PyObject_GetAttrStr(module, marker); + Py_DECREF(module); + if (likely(is_coroutine_value)) { + return is_coroutine_value; + } +ignore: + PyErr_Clear(); + } + return __Pyx_PyBool_FromLong(is_coroutine); +} +static PyObject * +__Pyx_CyFunction_get_is_coroutine(__pyx_CyFunctionObject *op, void *context) { + PyObject *result; + CYTHON_UNUSED_VAR(context); + if (op->func_is_coroutine) { + return __Pyx_NewRef(op->func_is_coroutine); + } + result = __Pyx_CyFunction_get_is_coroutine_value(op); + if (unlikely(!result)) + return NULL; + __Pyx_BEGIN_CRITICAL_SECTION(op); + if (op->func_is_coroutine) { + Py_DECREF(result); + result = __Pyx_NewRef(op->func_is_coroutine); + } else { + op->func_is_coroutine = __Pyx_NewRef(result); + } + __Pyx_END_CRITICAL_SECTION(); + return result; +} +static void __Pyx_CyFunction_raise_argument_count_error(__pyx_CyFunctionObject *func, const char* message, Py_ssize_t size) { +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject *py_name = __Pyx_CyFunction_get_name(func, NULL); + if (!py_name) return; + PyErr_Format(PyExc_TypeError, + "%.200S() %s (%" CYTHON_FORMAT_SSIZE_T "d given)", + py_name, message, size); + Py_DECREF(py_name); +#else + const char* name = ((PyCFunctionObject*)func)->m_ml->ml_name; + PyErr_Format(PyExc_TypeError, + "%.200s() %s (%" CYTHON_FORMAT_SSIZE_T "d given)", + name, message, size); +#endif +} +static void __Pyx_CyFunction_raise_type_error(__pyx_CyFunctionObject *func, const char* message) { +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject *py_name = __Pyx_CyFunction_get_name(func, NULL); + if (!py_name) return; + PyErr_Format(PyExc_TypeError, + "%.200S() %s", + py_name, message); + Py_DECREF(py_name); +#else + const char* name = ((PyCFunctionObject*)func)->m_ml->ml_name; + PyErr_Format(PyExc_TypeError, + "%.200s() %s", + name, message); +#endif +} +#if CYTHON_COMPILING_IN_LIMITED_API +static PyObject * +__Pyx_CyFunction_get_module(__pyx_CyFunctionObject *op, void *context) { + CYTHON_UNUSED_VAR(context); + return PyObject_GetAttrString(op->func, "__module__"); +} +static int +__Pyx_CyFunction_set_module(__pyx_CyFunctionObject *op, PyObject* value, void *context) { + CYTHON_UNUSED_VAR(context); + return PyObject_SetAttrString(op->func, "__module__", value); +} +#endif +static PyGetSetDef __pyx_CyFunction_getsets[] = { + {"func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, + {"__doc__", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0}, + {"func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, + {"__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0}, + {"__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0}, + {"func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, + {"__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0}, + {"func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, + {"__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0}, + {"func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, + {"__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0}, + {"func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, + {"__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0}, + {"func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, + {"__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0}, + {"__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0}, + {"__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0}, + {"_is_coroutine", (getter)__Pyx_CyFunction_get_is_coroutine, 0, 0, 0}, +#if CYTHON_COMPILING_IN_LIMITED_API + {"__module__", (getter)__Pyx_CyFunction_get_module, (setter)__Pyx_CyFunction_set_module, 0, 0}, +#endif + {0, 0, 0, 0, 0} +}; +static PyMemberDef __pyx_CyFunction_members[] = { +#if !CYTHON_COMPILING_IN_LIMITED_API + {"__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), 0, 0}, +#endif + {"__dictoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_dict), READONLY, 0}, +#if CYTHON_METH_FASTCALL +#if CYTHON_BACKPORT_VECTORCALL || CYTHON_COMPILING_IN_LIMITED_API + {"__vectorcalloffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_vectorcall), READONLY, 0}, +#else + {"__vectorcalloffset__", T_PYSSIZET, offsetof(PyCFunctionObject, vectorcall), READONLY, 0}, +#endif +#if CYTHON_COMPILING_IN_LIMITED_API + {"__weaklistoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_weakreflist), READONLY, 0}, +#else + {"__weaklistoffset__", T_PYSSIZET, offsetof(PyCFunctionObject, m_weakreflist), READONLY, 0}, +#endif +#endif + {0, 0, 0, 0, 0} +}; +static PyObject * +__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, PyObject *args) +{ + PyObject *result = NULL; + CYTHON_UNUSED_VAR(args); + __Pyx_BEGIN_CRITICAL_SECTION(m); + Py_INCREF(m->func_qualname); + result = m->func_qualname; + __Pyx_END_CRITICAL_SECTION(); + return result; +} +static PyMethodDef __pyx_CyFunction_methods[] = { + {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0}, + {0, 0, 0, 0} +}; +#if CYTHON_COMPILING_IN_LIMITED_API +#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist) +#else +#define __Pyx_CyFunction_weakreflist(cyfunc) (((PyCFunctionObject*)cyfunc)->m_weakreflist) +#endif +static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject *op, PyMethodDef *ml, int flags, PyObject* qualname, + PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { +#if !CYTHON_COMPILING_IN_LIMITED_API + PyCFunctionObject *cf = (PyCFunctionObject*) op; +#endif + if (unlikely(op == NULL)) + return NULL; +#if CYTHON_COMPILING_IN_LIMITED_API + op->func = PyCFunction_NewEx(ml, (PyObject*)op, module); + if (unlikely(!op->func)) return NULL; +#endif + op->flags = flags; + __Pyx_CyFunction_weakreflist(op) = NULL; +#if !CYTHON_COMPILING_IN_LIMITED_API + cf->m_ml = ml; + cf->m_self = (PyObject *) op; +#endif + Py_XINCREF(closure); + op->func_closure = closure; +#if !CYTHON_COMPILING_IN_LIMITED_API + Py_XINCREF(module); + cf->m_module = module; +#endif + op->func_dict = NULL; + op->func_name = NULL; + Py_INCREF(qualname); + op->func_qualname = qualname; + op->func_doc = NULL; +#if PY_VERSION_HEX < 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API + op->func_classobj = NULL; +#else + ((PyCMethodObject*)op)->mm_class = NULL; +#endif + op->func_globals = globals; + Py_INCREF(op->func_globals); + Py_XINCREF(code); + op->func_code = code; + op->defaults = NULL; + op->defaults_tuple = NULL; + op->defaults_kwdict = NULL; + op->defaults_getter = NULL; + op->func_annotations = NULL; + op->func_is_coroutine = NULL; +#if CYTHON_METH_FASTCALL + switch (ml->ml_flags & (METH_VARARGS | METH_FASTCALL | METH_NOARGS | METH_O | METH_KEYWORDS | METH_METHOD)) { + case METH_NOARGS: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_NOARGS; + break; + case METH_O: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_O; + break; + case METH_METHOD | METH_FASTCALL | METH_KEYWORDS: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD; + break; + case METH_FASTCALL | METH_KEYWORDS: + __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS; + break; + case METH_VARARGS | METH_KEYWORDS: + __Pyx_CyFunction_func_vectorcall(op) = NULL; + break; + default: + PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction"); + Py_DECREF(op); + return NULL; + } +#endif + return (PyObject *) op; +} +static int +__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m) +{ + Py_CLEAR(m->func_closure); +#if CYTHON_COMPILING_IN_LIMITED_API + Py_CLEAR(m->func); +#else + Py_CLEAR(((PyCFunctionObject*)m)->m_module); +#endif + Py_CLEAR(m->func_dict); + Py_CLEAR(m->func_name); + Py_CLEAR(m->func_qualname); + Py_CLEAR(m->func_doc); + Py_CLEAR(m->func_globals); + Py_CLEAR(m->func_code); +#if !CYTHON_COMPILING_IN_LIMITED_API +#if PY_VERSION_HEX < 0x030900B1 + Py_CLEAR(__Pyx_CyFunction_GetClassObj(m)); +#else + { + PyObject *cls = (PyObject*) ((PyCMethodObject *) (m))->mm_class; + ((PyCMethodObject *) (m))->mm_class = NULL; + Py_XDECREF(cls); + } +#endif +#endif + Py_CLEAR(m->defaults_tuple); + Py_CLEAR(m->defaults_kwdict); + Py_CLEAR(m->func_annotations); + Py_CLEAR(m->func_is_coroutine); + Py_CLEAR(m->defaults); + return 0; +} +static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m) +{ + if (__Pyx_CyFunction_weakreflist(m) != NULL) + PyObject_ClearWeakRefs((PyObject *) m); + __Pyx_CyFunction_clear(m); + __Pyx_PyHeapTypeObject_GC_Del(m); +} +static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m) +{ + PyObject_GC_UnTrack(m); + __Pyx__CyFunction_dealloc(m); +} +static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg) +{ + { + int e = __Pyx_call_type_traverse((PyObject*)m, 1, visit, arg); + if (e) return e; + } + Py_VISIT(m->func_closure); +#if CYTHON_COMPILING_IN_LIMITED_API + Py_VISIT(m->func); +#else + Py_VISIT(((PyCFunctionObject*)m)->m_module); +#endif + Py_VISIT(m->func_dict); + __Pyx_VISIT_CONST(m->func_name); + __Pyx_VISIT_CONST(m->func_qualname); + Py_VISIT(m->func_doc); + Py_VISIT(m->func_globals); + __Pyx_VISIT_CONST(m->func_code); +#if !CYTHON_COMPILING_IN_LIMITED_API + Py_VISIT(__Pyx_CyFunction_GetClassObj(m)); +#endif + Py_VISIT(m->defaults_tuple); + Py_VISIT(m->defaults_kwdict); + Py_VISIT(m->func_is_coroutine); + Py_VISIT(m->defaults); + return 0; +} +static PyObject* +__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op) +{ + PyObject *repr; + __Pyx_BEGIN_CRITICAL_SECTION(op); + repr = PyUnicode_FromFormat("", + op->func_qualname, (void *)op); + __Pyx_END_CRITICAL_SECTION(); + return repr; +} +static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) { +#if CYTHON_COMPILING_IN_LIMITED_API + PyObject *f = ((__pyx_CyFunctionObject*)func)->func; + PyCFunction meth; + int flags; + meth = PyCFunction_GetFunction(f); + if (unlikely(!meth)) return NULL; + flags = PyCFunction_GetFlags(f); + if (unlikely(flags < 0)) return NULL; +#else + PyCFunctionObject* f = (PyCFunctionObject*)func; + PyCFunction meth = f->m_ml->ml_meth; + int flags = f->m_ml->ml_flags; +#endif + Py_ssize_t size; + switch (flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) { + case METH_VARARGS: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) + return (*meth)(self, arg); + break; + case METH_VARARGS | METH_KEYWORDS: + return (*(PyCFunctionWithKeywords)(void(*)(void))meth)(self, arg, kw); + case METH_NOARGS: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) { +#if CYTHON_ASSUME_SAFE_SIZE + size = PyTuple_GET_SIZE(arg); +#else + size = PyTuple_Size(arg); + if (unlikely(size < 0)) return NULL; +#endif + if (likely(size == 0)) + return (*meth)(self, NULL); + __Pyx_CyFunction_raise_argument_count_error( + (__pyx_CyFunctionObject*)func, + "takes no arguments", size); + return NULL; + } + break; + case METH_O: + if (likely(kw == NULL || PyDict_Size(kw) == 0)) { +#if CYTHON_ASSUME_SAFE_SIZE + size = PyTuple_GET_SIZE(arg); +#else + size = PyTuple_Size(arg); + if (unlikely(size < 0)) return NULL; +#endif + if (likely(size == 1)) { + PyObject *result, *arg0; + #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS + arg0 = PyTuple_GET_ITEM(arg, 0); + #else + arg0 = __Pyx_PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL; + #endif + result = (*meth)(self, arg0); + #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) + Py_DECREF(arg0); + #endif + return result; + } + __Pyx_CyFunction_raise_argument_count_error( + (__pyx_CyFunctionObject*)func, + "takes exactly one argument", size); + return NULL; + } + break; + default: + PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction"); + return NULL; + } + __Pyx_CyFunction_raise_type_error( + (__pyx_CyFunctionObject*)func, "takes no keyword arguments"); + return NULL; +} +static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *self, *result; +#if CYTHON_COMPILING_IN_LIMITED_API + self = PyCFunction_GetSelf(((__pyx_CyFunctionObject*)func)->func); + if (unlikely(!self) && PyErr_Occurred()) return NULL; +#else + self = ((PyCFunctionObject*)func)->m_self; +#endif + result = __Pyx_CyFunction_CallMethod(func, self, arg, kw); + return result; +} +static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) { + PyObject *result; + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func; +#if CYTHON_METH_FASTCALL && (CYTHON_VECTORCALL || CYTHON_BACKPORT_VECTORCALL) + __pyx_vectorcallfunc vc = __Pyx_CyFunction_func_vectorcall(cyfunc); + if (vc) { +#if CYTHON_ASSUME_SAFE_MACROS && CYTHON_ASSUME_SAFE_SIZE + return __Pyx_PyVectorcall_FastCallDict(func, vc, &PyTuple_GET_ITEM(args, 0), (size_t)PyTuple_GET_SIZE(args), kw); +#else + (void) &__Pyx_PyVectorcall_FastCallDict; + return PyVectorcall_Call(func, args, kw); +#endif + } +#endif + if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) { + Py_ssize_t argc; + PyObject *new_args; + PyObject *self; +#if CYTHON_ASSUME_SAFE_SIZE + argc = PyTuple_GET_SIZE(args); +#else + argc = PyTuple_Size(args); + if (unlikely(argc < 0)) return NULL; +#endif + new_args = PyTuple_GetSlice(args, 1, argc); + if (unlikely(!new_args)) + return NULL; + self = PyTuple_GetItem(args, 0); + if (unlikely(!self)) { + Py_DECREF(new_args); + PyErr_Format(PyExc_TypeError, + "unbound method %.200S() needs an argument", + cyfunc->func_qualname); + return NULL; + } + result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw); + Py_DECREF(new_args); + } else { + result = __Pyx_CyFunction_Call(func, args, kw); + } + return result; +} +#if CYTHON_METH_FASTCALL && (CYTHON_VECTORCALL || CYTHON_BACKPORT_VECTORCALL) +static CYTHON_INLINE int __Pyx_CyFunction_Vectorcall_CheckArgs(__pyx_CyFunctionObject *cyfunc, Py_ssize_t nargs, PyObject *kwnames) +{ + int ret = 0; + if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) { + if (unlikely(nargs < 1)) { + __Pyx_CyFunction_raise_type_error( + cyfunc, "needs an argument"); + return -1; + } + ret = 1; + } + if (unlikely(kwnames) && unlikely(__Pyx_PyTuple_GET_SIZE(kwnames))) { + __Pyx_CyFunction_raise_type_error( + cyfunc, "takes no keyword arguments"); + return -1; + } + return ret; +} +static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; +#if CYTHON_COMPILING_IN_LIMITED_API + PyCFunction meth = PyCFunction_GetFunction(cyfunc->func); + if (unlikely(!meth)) return NULL; +#else + PyCFunction meth = ((PyCFunctionObject*)cyfunc)->m_ml->ml_meth; +#endif + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: +#if CYTHON_COMPILING_IN_LIMITED_API + self = PyCFunction_GetSelf(((__pyx_CyFunctionObject*)cyfunc)->func); + if (unlikely(!self) && PyErr_Occurred()) return NULL; +#else + self = ((PyCFunctionObject*)cyfunc)->m_self; +#endif + break; + default: + return NULL; + } + if (unlikely(nargs != 0)) { + __Pyx_CyFunction_raise_argument_count_error( + cyfunc, "takes no arguments", nargs); + return NULL; + } + return meth(self, NULL); +} +static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; +#if CYTHON_COMPILING_IN_LIMITED_API + PyCFunction meth = PyCFunction_GetFunction(cyfunc->func); + if (unlikely(!meth)) return NULL; +#else + PyCFunction meth = ((PyCFunctionObject*)cyfunc)->m_ml->ml_meth; +#endif + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: +#if CYTHON_COMPILING_IN_LIMITED_API + self = PyCFunction_GetSelf(((__pyx_CyFunctionObject*)cyfunc)->func); + if (unlikely(!self) && PyErr_Occurred()) return NULL; +#else + self = ((PyCFunctionObject*)cyfunc)->m_self; +#endif + break; + default: + return NULL; + } + if (unlikely(nargs != 1)) { + __Pyx_CyFunction_raise_argument_count_error( + cyfunc, "takes exactly one argument", nargs); + return NULL; + } + return meth(self, args[0]); +} +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; +#if CYTHON_COMPILING_IN_LIMITED_API + PyCFunction meth = PyCFunction_GetFunction(cyfunc->func); + if (unlikely(!meth)) return NULL; +#else + PyCFunction meth = ((PyCFunctionObject*)cyfunc)->m_ml->ml_meth; +#endif + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: +#if CYTHON_COMPILING_IN_LIMITED_API + self = PyCFunction_GetSelf(((__pyx_CyFunctionObject*)cyfunc)->func); + if (unlikely(!self) && PyErr_Occurred()) return NULL; +#else + self = ((PyCFunctionObject*)cyfunc)->m_self; +#endif + break; + default: + return NULL; + } + return ((__Pyx_PyCFunctionFastWithKeywords)(void(*)(void))meth)(self, args, nargs, kwnames); +} +static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames) +{ + __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func; + PyTypeObject *cls = (PyTypeObject *) __Pyx_CyFunction_GetClassObj(cyfunc); +#if CYTHON_BACKPORT_VECTORCALL + Py_ssize_t nargs = (Py_ssize_t)nargsf; +#else + Py_ssize_t nargs = PyVectorcall_NARGS(nargsf); +#endif + PyObject *self; +#if CYTHON_COMPILING_IN_LIMITED_API + PyCFunction meth = PyCFunction_GetFunction(cyfunc->func); + if (unlikely(!meth)) return NULL; +#else + PyCFunction meth = ((PyCFunctionObject*)cyfunc)->m_ml->ml_meth; +#endif + switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) { + case 1: + self = args[0]; + args += 1; + nargs -= 1; + break; + case 0: +#if CYTHON_COMPILING_IN_LIMITED_API + self = PyCFunction_GetSelf(((__pyx_CyFunctionObject*)cyfunc)->func); + if (unlikely(!self) && PyErr_Occurred()) return NULL; +#else + self = ((PyCFunctionObject*)cyfunc)->m_self; +#endif + break; + default: + return NULL; + } + return ((__Pyx_PyCMethod)(void(*)(void))meth)(self, cls, args, (size_t)nargs, kwnames); +} +#endif +static PyType_Slot __pyx_CyFunctionType_slots[] = { + {Py_tp_dealloc, (void *)__Pyx_CyFunction_dealloc}, + {Py_tp_repr, (void *)__Pyx_CyFunction_repr}, + {Py_tp_call, (void *)__Pyx_CyFunction_CallAsMethod}, + {Py_tp_traverse, (void *)__Pyx_CyFunction_traverse}, + {Py_tp_clear, (void *)__Pyx_CyFunction_clear}, + {Py_tp_methods, (void *)__pyx_CyFunction_methods}, + {Py_tp_members, (void *)__pyx_CyFunction_members}, + {Py_tp_getset, (void *)__pyx_CyFunction_getsets}, + {Py_tp_descr_get, (void *)__Pyx_PyMethod_New}, + {0, 0}, +}; +static PyType_Spec __pyx_CyFunctionType_spec = { + __PYX_TYPE_MODULE_PREFIX "cython_function_or_method", + sizeof(__pyx_CyFunctionObject), + 0, +#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR + Py_TPFLAGS_METHOD_DESCRIPTOR | +#endif +#if CYTHON_METH_FASTCALL +#if defined(Py_TPFLAGS_HAVE_VECTORCALL) + Py_TPFLAGS_HAVE_VECTORCALL | +#elif defined(_Py_TPFLAGS_HAVE_VECTORCALL) + _Py_TPFLAGS_HAVE_VECTORCALL | +#endif +#endif // CYTHON_METH_FASTCALL +#if PY_VERSION_HEX >= 0x030A0000 + Py_TPFLAGS_IMMUTABLETYPE | +#endif + Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE, + __pyx_CyFunctionType_slots +}; +static int __pyx_CyFunction_init(PyObject *module) { + __pyx_mstatetype *mstate = __Pyx_PyModule_GetState(module); + mstate->__pyx_CyFunctionType = __Pyx_FetchCommonTypeFromSpec( + mstate->__pyx_CommonTypesMetaclassType, module, &__pyx_CyFunctionType_spec, NULL); + if (unlikely(mstate->__pyx_CyFunctionType == NULL)) { + return -1; + } + return 0; +} +static CYTHON_INLINE PyObject *__Pyx_CyFunction_InitDefaults(PyObject *func, PyTypeObject *defaults_type) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults = PyObject_CallObject((PyObject*)defaults_type, NULL); // _PyObject_New(defaults_type); + if (unlikely(!m->defaults)) + return NULL; + return m->defaults; +} +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults_tuple = tuple; + Py_INCREF(tuple); +} +static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->defaults_kwdict = dict; + Py_INCREF(dict); +} +static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) { + __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; + m->func_annotations = dict; + Py_INCREF(dict); +} + +/* CythonFunction */ +static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, int flags, PyObject* qualname, + PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { + PyObject *op = __Pyx_CyFunction_Init( + PyObject_GC_New(__pyx_CyFunctionObject, __pyx_mstate_global->__pyx_CyFunctionType), + ml, flags, qualname, closure, module, globals, code + ); + if (likely(op)) { + PyObject_GC_Track(op); + } + return op; +} + +/* CLineInTraceback */ +#if CYTHON_CLINE_IN_TRACEBACK && CYTHON_CLINE_IN_TRACEBACK_RUNTIME +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line) { + PyObject *use_cline; + PyObject *ptype, *pvalue, *ptraceback; +#if CYTHON_COMPILING_IN_CPYTHON + PyObject **cython_runtime_dict; +#endif + CYTHON_MAYBE_UNUSED_VAR(tstate); + if (unlikely(!__pyx_mstate_global->__pyx_cython_runtime)) { + return c_line; + } + __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); +#if CYTHON_COMPILING_IN_CPYTHON + cython_runtime_dict = _PyObject_GetDictPtr(__pyx_mstate_global->__pyx_cython_runtime); + if (likely(cython_runtime_dict)) { + __Pyx_BEGIN_CRITICAL_SECTION(*cython_runtime_dict); + __PYX_PY_DICT_LOOKUP_IF_MODIFIED( + use_cline, *cython_runtime_dict, + __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_mstate_global->__pyx_n_u_cline_in_traceback)) + Py_XINCREF(use_cline); + __Pyx_END_CRITICAL_SECTION(); + } else +#endif + { + PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStrNoError(__pyx_mstate_global->__pyx_cython_runtime, __pyx_mstate_global->__pyx_n_u_cline_in_traceback); + if (use_cline_obj) { + use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; + Py_INCREF(use_cline); + Py_DECREF(use_cline_obj); + } else { + PyErr_Clear(); + use_cline = NULL; + } + } + if (!use_cline) { + c_line = 0; + (void) PyObject_SetAttr(__pyx_mstate_global->__pyx_cython_runtime, __pyx_mstate_global->__pyx_n_u_cline_in_traceback, Py_False); + } + else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { + c_line = 0; + } + Py_XDECREF(use_cline); + __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); + return c_line; +} +#endif + +/* CodeObjectCache */ +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static __Pyx_CachedCodeObjectType *__pyx__find_code_object(struct __Pyx_CodeObjectCache *code_cache, int code_line) { + __Pyx_CachedCodeObjectType* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!code_cache->entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(code_cache->entries, code_cache->count, code_line); + if (unlikely(pos >= code_cache->count) || unlikely(code_cache->entries[pos].code_line != code_line)) { + return NULL; + } + code_object = code_cache->entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static __Pyx_CachedCodeObjectType *__pyx_find_code_object(int code_line) { +#if CYTHON_COMPILING_IN_CPYTHON_FREETHREADING && !CYTHON_ATOMICS + (void)__pyx__find_code_object; + return NULL; // Most implementation should have atomics. But otherwise, don't make it thread-safe, just miss. +#else + struct __Pyx_CodeObjectCache *code_cache = &__pyx_mstate_global->__pyx_code_cache; +#if CYTHON_COMPILING_IN_CPYTHON_FREETHREADING + __pyx_nonatomic_int_type old_count = __pyx_atomic_incr_acq_rel(&code_cache->accessor_count); + if (old_count < 0) { + __pyx_atomic_decr_acq_rel(&code_cache->accessor_count); + return NULL; + } +#endif + __Pyx_CachedCodeObjectType *result = __pyx__find_code_object(code_cache, code_line); +#if CYTHON_COMPILING_IN_CPYTHON_FREETHREADING + __pyx_atomic_decr_acq_rel(&code_cache->accessor_count); +#endif + return result; +#endif +} +static void __pyx__insert_code_object(struct __Pyx_CodeObjectCache *code_cache, int code_line, __Pyx_CachedCodeObjectType* code_object) +{ + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = code_cache->entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + code_cache->entries = entries; + code_cache->max_count = 64; + code_cache->count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(code_cache->entries, code_cache->count, code_line); + if ((pos < code_cache->count) && unlikely(code_cache->entries[pos].code_line == code_line)) { + __Pyx_CachedCodeObjectType* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_INCREF(code_object); + Py_DECREF(tmp); + return; + } + if (code_cache->count == code_cache->max_count) { + int new_max = code_cache->max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + code_cache->entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + code_cache->entries = entries; + code_cache->max_count = new_max; + } + for (i=code_cache->count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + code_cache->count++; + Py_INCREF(code_object); +} +static void __pyx_insert_code_object(int code_line, __Pyx_CachedCodeObjectType* code_object) { +#if CYTHON_COMPILING_IN_CPYTHON_FREETHREADING && !CYTHON_ATOMICS + (void)__pyx__insert_code_object; + return; // Most implementation should have atomics. But otherwise, don't make it thread-safe, just fail. +#else + struct __Pyx_CodeObjectCache *code_cache = &__pyx_mstate_global->__pyx_code_cache; +#if CYTHON_COMPILING_IN_CPYTHON_FREETHREADING + __pyx_nonatomic_int_type expected = 0; + if (!__pyx_atomic_int_cmp_exchange(&code_cache->accessor_count, &expected, INT_MIN)) { + return; + } +#endif + __pyx__insert_code_object(code_cache, code_line, code_object); +#if CYTHON_COMPILING_IN_CPYTHON_FREETHREADING + __pyx_atomic_sub(&code_cache->accessor_count, INT_MIN); +#endif +#endif +} + +/* AddTraceback */ +#include "compile.h" +#include "frameobject.h" +#include "traceback.h" +#if PY_VERSION_HEX >= 0x030b00a6 && !CYTHON_COMPILING_IN_LIMITED_API && !defined(PYPY_VERSION) + #ifndef Py_BUILD_CORE + #define Py_BUILD_CORE 1 + #endif + #include "internal/pycore_frame.h" +#endif +#if CYTHON_COMPILING_IN_LIMITED_API +static PyObject *__Pyx_PyCode_Replace_For_AddTraceback(PyObject *code, PyObject *scratch_dict, + PyObject *firstlineno, PyObject *name) { + PyObject *replace = NULL; + if (unlikely(PyDict_SetItemString(scratch_dict, "co_firstlineno", firstlineno))) return NULL; + if (unlikely(PyDict_SetItemString(scratch_dict, "co_name", name))) return NULL; + replace = PyObject_GetAttrString(code, "replace"); + if (likely(replace)) { + PyObject *result = PyObject_Call(replace, __pyx_mstate_global->__pyx_empty_tuple, scratch_dict); + Py_DECREF(replace); + return result; + } + PyErr_Clear(); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyObject *code_object = NULL, *py_py_line = NULL, *py_funcname = NULL, *dict = NULL; + PyObject *replace = NULL, *getframe = NULL, *frame = NULL; + PyObject *exc_type, *exc_value, *exc_traceback; + int success = 0; + if (c_line) { + (void) __pyx_cfilenm; + (void) __Pyx_CLineForTraceback(__Pyx_PyThreadState_Current, c_line); + } + PyErr_Fetch(&exc_type, &exc_value, &exc_traceback); + code_object = __pyx_find_code_object(c_line ? -c_line : py_line); + if (!code_object) { + code_object = Py_CompileString("_getframe()", filename, Py_eval_input); + if (unlikely(!code_object)) goto bad; + py_py_line = PyLong_FromLong(py_line); + if (unlikely(!py_py_line)) goto bad; + py_funcname = PyUnicode_FromString(funcname); + if (unlikely(!py_funcname)) goto bad; + dict = PyDict_New(); + if (unlikely(!dict)) goto bad; + { + PyObject *old_code_object = code_object; + code_object = __Pyx_PyCode_Replace_For_AddTraceback(code_object, dict, py_py_line, py_funcname); + Py_DECREF(old_code_object); + } + if (unlikely(!code_object)) goto bad; + __pyx_insert_code_object(c_line ? -c_line : py_line, code_object); + } else { + dict = PyDict_New(); + } + getframe = PySys_GetObject("_getframe"); + if (unlikely(!getframe)) goto bad; + if (unlikely(PyDict_SetItemString(dict, "_getframe", getframe))) goto bad; + frame = PyEval_EvalCode(code_object, dict, dict); + if (unlikely(!frame) || frame == Py_None) goto bad; + success = 1; + bad: + PyErr_Restore(exc_type, exc_value, exc_traceback); + Py_XDECREF(code_object); + Py_XDECREF(py_py_line); + Py_XDECREF(py_funcname); + Py_XDECREF(dict); + Py_XDECREF(replace); + if (success) { + PyTraceBack_Here( + (struct _frame*)frame); + } + Py_XDECREF(frame); +} +#else +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = NULL; + PyObject *py_funcname = NULL; + if (c_line) { + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + if (!py_funcname) goto bad; + funcname = PyUnicode_AsUTF8(py_funcname); + if (!funcname) goto bad; + } + py_code = PyCode_NewEmpty(filename, funcname, py_line); + Py_XDECREF(py_funcname); + return py_code; +bad: + Py_XDECREF(py_funcname); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject *ptype, *pvalue, *ptraceback; + if (c_line) { + c_line = __Pyx_CLineForTraceback(tstate, c_line); + } + py_code = __pyx_find_code_object(c_line ? -c_line : py_line); + if (!py_code) { + __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) { + /* If the code object creation fails, then we should clear the + fetched exception references and propagate the new exception */ + Py_XDECREF(ptype); + Py_XDECREF(pvalue); + Py_XDECREF(ptraceback); + goto bad; + } + __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); + __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); + } + py_frame = PyFrame_New( + tstate, /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_mstate_global->__pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + __Pyx_PyFrame_SetLineNumber(py_frame, py_line); + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} +#endif + +/* MemviewSliceIsContig */ +static int +__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim) +{ + int i, index, step, start; + Py_ssize_t itemsize = mvs.memview->view.itemsize; + if (order == 'F') { + step = 1; + start = 0; + } else { + step = -1; + start = ndim - 1; + } + for (i = 0; i < ndim; i++) { + index = start + step * i; + if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize) + return 0; + itemsize *= mvs.shape[index]; + } + return 1; +} + +/* OverlappingSlices */ +static void +__pyx_get_array_memory_extents(__Pyx_memviewslice *slice, + void **out_start, void **out_end, + int ndim, size_t itemsize) +{ + char *start, *end; + int i; + start = end = slice->data; + for (i = 0; i < ndim; i++) { + Py_ssize_t stride = slice->strides[i]; + Py_ssize_t extent = slice->shape[i]; + if (extent == 0) { + *out_start = *out_end = start; + return; + } else { + if (stride > 0) + end += stride * (extent - 1); + else + start += stride * (extent - 1); + } + } + *out_start = start; + *out_end = end + itemsize; +} +static int +__pyx_slices_overlap(__Pyx_memviewslice *slice1, + __Pyx_memviewslice *slice2, + int ndim, size_t itemsize) +{ + void *start1, *end1, *start2, *end2; + __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize); + __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize); + return (start1 < end2) && (start2 < end1); +} + +/* IsLittleEndian */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) +{ + union { + uint32_t u32; + uint8_t u8[4]; + } S; + S.u32 = 0x01020304; + return S.u8[0] == 4; +} + +/* BufferFormatCheck */ +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + const __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t <= '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case '?': return "'bool'"; + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparsable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, int is_complex) { + CYTHON_UNUSED_VAR(is_complex); + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, int is_complex) { + CYTHON_UNUSED_VAR(is_complex); + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case '?': case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + const __Pyx_StructField* field = ctx->head->field; + const __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + const __Pyx_StructField* field = ctx->head->field; + const __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static int +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number, ndim; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return -1; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return -1; + ndim = ctx->head->field->type->ndim; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return -1; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + return -1; + } + if (*ts != ',' && *ts != ')') { + PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + return -1; + } + if (*ts == ',') ts++; + i++; + } + if (i != ndim) { + PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + return -1; + } + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return -1; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return 0; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case '\r': + case '\n': + ++ts; + break; + case '<': + if (!__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } + CYTHON_FALLTHROUGH; + case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 'p': + if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) && + (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) { + ctx->enc_count += ctx->new_count; + ctx->new_count = 1; + got_Z = 0; + ++ts; + break; + } + CYTHON_FALLTHROUGH; + case 's': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (__pyx_buffmt_parse_array(ctx, &ts) < 0) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} + +/* TypeInfoCompare */ + static int +__pyx_typeinfo_cmp(const __Pyx_TypeInfo *a, const __Pyx_TypeInfo *b) +{ + int i; + if (!a || !b) + return 0; + if (a == b) + return 1; + if (a->size != b->size || a->typegroup != b->typegroup || + a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { + if (a->typegroup == 'H' || b->typegroup == 'H') { + return a->size == b->size; + } else { + return 0; + } + } + if (a->ndim) { + for (i = 0; i < a->ndim; i++) + if (a->arraysize[i] != b->arraysize[i]) + return 0; + } + if (a->typegroup == 'S') { + if (a->flags != b->flags) + return 0; + if (a->fields || b->fields) { + if (!(a->fields && b->fields)) + return 0; + for (i = 0; a->fields[i].type && b->fields[i].type; i++) { + const __Pyx_StructField *field_a = a->fields + i; + const __Pyx_StructField *field_b = b->fields + i; + if (field_a->offset != field_b->offset || + !__pyx_typeinfo_cmp(field_a->type, field_b->type)) + return 0; + } + return !a->fields[i].type && !b->fields[i].type; + } + } + return 1; +} + +/* MemviewSliceValidateAndInit */ + static int +__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) +{ + if (buf->shape[dim] <= 1) + return 1; + if (buf->strides) { + if (spec & __Pyx_MEMVIEW_CONTIG) { + if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { + if (unlikely(buf->strides[dim] != sizeof(void *))) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly contiguous " + "in dimension %d.", dim); + goto fail; + } + } else if (unlikely(buf->strides[dim] != buf->itemsize)) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_FOLLOW) { + Py_ssize_t stride = buf->strides[dim]; + if (stride < 0) + stride = -stride; + if (unlikely(stride < buf->itemsize)) { + PyErr_SetString(PyExc_ValueError, + "Buffer and memoryview are not contiguous " + "in the same dimension."); + goto fail; + } + } + } else { + if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not contiguous in " + "dimension %d", dim); + goto fail; + } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) { + PyErr_Format(PyExc_ValueError, + "C-contiguous buffer is not indirect in " + "dimension %d", dim); + goto fail; + } else if (unlikely(buf->suboffsets)) { + PyErr_SetString(PyExc_ValueError, + "Buffer exposes suboffsets but no strides"); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_check_suboffsets(Py_buffer *buf, int dim, int ndim, int spec) +{ + CYTHON_UNUSED_VAR(ndim); + if (spec & __Pyx_MEMVIEW_DIRECT) { + if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) { + PyErr_Format(PyExc_ValueError, + "Buffer not compatible with direct access " + "in dimension %d.", dim); + goto fail; + } + } + if (spec & __Pyx_MEMVIEW_PTR) { + if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) { + PyErr_Format(PyExc_ValueError, + "Buffer is not indirectly accessible " + "in dimension %d.", dim); + goto fail; + } + } + return 1; +fail: + return 0; +} +static int +__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) +{ + int i; + if (c_or_f_flag & __Pyx_IS_F_CONTIG) { + Py_ssize_t stride = 1; + for (i = 0; i < ndim; i++) { + if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { + PyErr_SetString(PyExc_ValueError, + "Buffer not fortran contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { + Py_ssize_t stride = 1; + for (i = ndim - 1; i >- 1; i--) { + if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { + PyErr_SetString(PyExc_ValueError, + "Buffer not C contiguous."); + goto fail; + } + stride = stride * buf->shape[i]; + } + } + return 1; +fail: + return 0; +} +static int __Pyx_ValidateAndInit_memviewslice( + int *axes_specs, + int c_or_f_flag, + int buf_flags, + int ndim, + const __Pyx_TypeInfo *dtype, + __Pyx_BufFmt_StackElem stack[], + __Pyx_memviewslice *memviewslice, + PyObject *original_obj) +{ + struct __pyx_memoryview_obj *memview, *new_memview; + __Pyx_RefNannyDeclarations + Py_buffer *buf; + int i, spec = 0, retval = -1; + __Pyx_BufFmt_Context ctx; + int from_memoryview = __pyx_memoryview_check(original_obj); + __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); + if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) + original_obj)->typeinfo)) { + memview = (struct __pyx_memoryview_obj *) original_obj; + new_memview = NULL; + } else { + memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + original_obj, buf_flags, 0, dtype); + new_memview = memview; + if (unlikely(!memview)) + goto fail; + } + buf = &memview->view; + if (unlikely(buf->ndim != ndim)) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + ndim, buf->ndim); + goto fail; + } + if (new_memview) { + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail; + } + if (unlikely((unsigned) buf->itemsize != dtype->size)) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " + "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", + buf->itemsize, + (buf->itemsize > 1) ? "s" : "", + dtype->name, + dtype->size, + (dtype->size > 1) ? "s" : ""); + goto fail; + } + if (buf->len > 0) { + for (i = 0; i < ndim; i++) { + spec = axes_specs[i]; + if (unlikely(!__pyx_check_strides(buf, i, ndim, spec))) + goto fail; + if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec))) + goto fail; + } + if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag))) + goto fail; + } + if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, + new_memview != NULL) == -1)) { + goto fail; + } + retval = 0; + goto no_fail; +fail: + Py_XDECREF((PyObject*)new_memview); + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_long__const__(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, + (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1, + &__Pyx_TypeInfo_long__const__, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* CIntFromPyVerify */ + #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_dc_long__const__(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, + (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 2, + &__Pyx_TypeInfo_long__const__, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* ObjectToMemviewSlice */ + static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_double__const__(PyObject *obj, int writable_flag) { + __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; + __Pyx_BufFmt_StackElem stack[1]; + int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; + int retcode; + if (obj == Py_None) { + result.memview = (struct __pyx_memoryview_obj *) Py_None; + return result; + } + retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, + (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1, + &__Pyx_TypeInfo_double__const__, stack, + &result, obj); + if (unlikely(retcode == -1)) + goto __pyx_fail; + return result; +__pyx_fail: + result.memview = NULL; + result.data = NULL; + return result; +} + +/* MemviewDtypeToObject */ + static CYTHON_INLINE PyObject *__pyx_memview_get_long__const__(const char *itemp) { + return (PyObject *) __Pyx_PyLong_From_long(*(long const *) itemp); +} + +/* MemviewSliceCopyTemplate */ + static __Pyx_memviewslice +__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, + const char *mode, int ndim, + size_t sizeof_dtype, int contig_flag, + int dtype_is_object) +{ + __Pyx_RefNannyDeclarations + int i; + __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; + struct __pyx_memoryview_obj *from_memview = from_mvs->memview; + Py_buffer *buf = &from_memview->view; + PyObject *shape_tuple = NULL; + PyObject *temp_int = NULL; + struct __pyx_array_obj *array_obj = NULL; + struct __pyx_memoryview_obj *memview_obj = NULL; + __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); + for (i = 0; i < ndim; i++) { + if (unlikely(from_mvs->suboffsets[i] >= 0)) { + PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " + "indirect dimensions (axis %d)", i); + goto fail; + } + } + shape_tuple = PyTuple_New(ndim); + if (unlikely(!shape_tuple)) { + goto fail; + } + __Pyx_GOTREF(shape_tuple); + for(i = 0; i < ndim; i++) { + temp_int = PyLong_FromSsize_t(from_mvs->shape[i]); + if(unlikely(!temp_int)) { + goto fail; + } else { +#if CYTHON_ASSUME_SAFE_MACROS + PyTuple_SET_ITEM(shape_tuple, i, temp_int); +#else + if (PyTuple_SetItem(shape_tuple, i, temp_int) < 0) { + goto fail; + } +#endif + temp_int = NULL; + } + } + array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, mode, NULL); + if (unlikely(!array_obj)) { + goto fail; + } + __Pyx_GOTREF(array_obj); + memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( + (PyObject *) array_obj, contig_flag, + dtype_is_object, + from_mvs->memview->typeinfo); + if (unlikely(!memview_obj)) + goto fail; + if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) + goto fail; + if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, + dtype_is_object) < 0)) + goto fail; + goto no_fail; +fail: + __Pyx_XDECREF((PyObject *) new_mvs.memview); + new_mvs.memview = NULL; + new_mvs.data = NULL; +no_fail: + __Pyx_XDECREF(shape_tuple); + __Pyx_XDECREF(temp_int); + __Pyx_XDECREF((PyObject *) array_obj); + __Pyx_RefNannyFinishContext(); + return new_mvs; +} + +/* MemviewSliceInit */ + static int +__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, + int ndim, + __Pyx_memviewslice *memviewslice, + int memview_is_new_reference) +{ + __Pyx_RefNannyDeclarations + int i, retval=-1; + Py_buffer *buf = &memview->view; + __Pyx_RefNannySetupContext("init_memviewslice", 0); + if (unlikely(memviewslice->memview || memviewslice->data)) { + PyErr_SetString(PyExc_ValueError, + "memviewslice is already initialized!"); + goto fail; + } + if (buf->strides) { + for (i = 0; i < ndim; i++) { + memviewslice->strides[i] = buf->strides[i]; + } + } else { + Py_ssize_t stride = buf->itemsize; + for (i = ndim - 1; i >= 0; i--) { + memviewslice->strides[i] = stride; + stride *= buf->shape[i]; + } + } + for (i = 0; i < ndim; i++) { + memviewslice->shape[i] = buf->shape[i]; + if (buf->suboffsets) { + memviewslice->suboffsets[i] = buf->suboffsets[i]; + } else { + memviewslice->suboffsets[i] = -1; + } + } + memviewslice->memview = memview; + memviewslice->data = (char *)buf->buf; + if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { + Py_INCREF((PyObject*)memview); + } + retval = 0; + goto no_fail; +fail: + memviewslice->memview = 0; + memviewslice->data = 0; + retval = -1; +no_fail: + __Pyx_RefNannyFinishContext(); + return retval; +} +#ifndef Py_NO_RETURN +#define Py_NO_RETURN +#endif +static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN { + va_list vargs; + char msg[200]; +#if PY_VERSION_HEX >= 0x030A0000 || defined(HAVE_STDARG_PROTOTYPES) + va_start(vargs, fmt); +#else + va_start(vargs); +#endif + vsnprintf(msg, 200, fmt, vargs); + va_end(vargs); + Py_FatalError(msg); +} +static CYTHON_INLINE int +__pyx_add_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)++; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE int +__pyx_sub_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count, + PyThread_type_lock lock) +{ + int result; + PyThread_acquire_lock(lock, 1); + result = (*acquisition_count)--; + PyThread_release_lock(lock); + return result; +} +static CYTHON_INLINE void +__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) +{ + __pyx_nonatomic_int_type old_acquisition_count; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (unlikely(!memview || (PyObject *) memview == Py_None)) { + return; + } + old_acquisition_count = __pyx_add_acquisition_count(memview); + if (unlikely(old_acquisition_count <= 0)) { + if (likely(old_acquisition_count == 0)) { + if (have_gil) { + Py_INCREF((PyObject *) memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_INCREF((PyObject *) memview); + PyGILState_Release(_gilstate); + } + } else { + __pyx_fatalerror("Acquisition count is %d (line %d)", + old_acquisition_count+1, lineno); + } + } +} +static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *memslice, + int have_gil, int lineno) { + __pyx_nonatomic_int_type old_acquisition_count; + struct __pyx_memoryview_obj *memview = memslice->memview; + if (unlikely(!memview || (PyObject *) memview == Py_None)) { + memslice->memview = NULL; + return; + } + old_acquisition_count = __pyx_sub_acquisition_count(memview); + memslice->data = NULL; + if (likely(old_acquisition_count > 1)) { + memslice->memview = NULL; + } else if (likely(old_acquisition_count == 1)) { + if (have_gil) { + Py_CLEAR(memslice->memview); + } else { + PyGILState_STATE _gilstate = PyGILState_Ensure(); + Py_CLEAR(memslice->memview); + PyGILState_Release(_gilstate); + } + } else { + __pyx_fatalerror("Acquisition count is %d (line %d)", + old_acquisition_count-1, lineno); + } +} + +/* CIntFromPy */ + static CYTHON_INLINE long __Pyx_PyLong_As_long(PyObject *x) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const long neg_one = (long) -1, const_zero = (long) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (unlikely(!PyLong_Check(x))) { + long val; + PyObject *tmp = __Pyx_PyNumber_Long(x); + if (!tmp) return (long) -1; + val = __Pyx_PyLong_As_long(tmp); + Py_DECREF(tmp); + return val; + } + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + if (unlikely(__Pyx_PyLong_IsNeg(x))) { + goto raise_neg_overflow; + } else if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_DigitCount(x)) { + case 2: + if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) >= 2 * PyLong_SHIFT)) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) >= 3 * PyLong_SHIFT)) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) >= 4 * PyLong_SHIFT)) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if ((sizeof(long) <= sizeof(unsigned long))) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(long) <= sizeof(unsigned PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_SignedDigitCount(x)) { + case -2: + if ((8 * sizeof(long) - 1 > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } + } +#endif + if ((sizeof(long) <= sizeof(long))) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(long) <= sizeof(PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { + long val; + int ret = -1; +#if PY_VERSION_HEX >= 0x030d00A6 && !CYTHON_COMPILING_IN_LIMITED_API + Py_ssize_t bytes_copied = PyLong_AsNativeBytes( + x, &val, sizeof(val), Py_ASNATIVEBYTES_NATIVE_ENDIAN | (is_unsigned ? Py_ASNATIVEBYTES_UNSIGNED_BUFFER | Py_ASNATIVEBYTES_REJECT_NEGATIVE : 0)); + if (unlikely(bytes_copied == -1)) { + } else if (unlikely(bytes_copied > (Py_ssize_t) sizeof(val))) { + goto raise_overflow; + } else { + ret = 0; + } +#elif PY_VERSION_HEX < 0x030d0000 && !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + ret = _PyLong_AsByteArray((PyLongObject *)x, + bytes, sizeof(val), + is_little, !is_unsigned); +#else + PyObject *v; + PyObject *stepval = NULL, *mask = NULL, *shift = NULL; + int bits, remaining_bits, is_negative = 0; + int chunk_size = (sizeof(long) < 8) ? 30 : 62; + if (likely(PyLong_CheckExact(x))) { + v = __Pyx_NewRef(x); + } else { + v = PyNumber_Long(x); + if (unlikely(!v)) return (long) -1; + assert(PyLong_CheckExact(v)); + } + { + int result = PyObject_RichCompareBool(v, Py_False, Py_LT); + if (unlikely(result < 0)) { + Py_DECREF(v); + return (long) -1; + } + is_negative = result == 1; + } + if (is_unsigned && unlikely(is_negative)) { + Py_DECREF(v); + goto raise_neg_overflow; + } else if (is_negative) { + stepval = PyNumber_Invert(v); + Py_DECREF(v); + if (unlikely(!stepval)) + return (long) -1; + } else { + stepval = v; + } + v = NULL; + val = (long) 0; + mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; + shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; + for (bits = 0; bits < (int) sizeof(long) * 8 - chunk_size; bits += chunk_size) { + PyObject *tmp, *digit; + long idigit; + digit = PyNumber_And(stepval, mask); + if (unlikely(!digit)) goto done; + idigit = PyLong_AsLong(digit); + Py_DECREF(digit); + if (unlikely(idigit < 0)) goto done; + val |= ((long) idigit) << bits; + tmp = PyNumber_Rshift(stepval, shift); + if (unlikely(!tmp)) goto done; + Py_DECREF(stepval); stepval = tmp; + } + Py_DECREF(shift); shift = NULL; + Py_DECREF(mask); mask = NULL; + { + long idigit = PyLong_AsLong(stepval); + if (unlikely(idigit < 0)) goto done; + remaining_bits = ((int) sizeof(long) * 8) - bits - (is_unsigned ? 0 : 1); + if (unlikely(idigit >= (1L << remaining_bits))) + goto raise_overflow; + val |= ((long) idigit) << bits; + } + if (!is_unsigned) { + if (unlikely(val & (((long) 1) << (sizeof(long) * 8 - 1)))) + goto raise_overflow; + if (is_negative) + val = ~val; + } + ret = 0; + done: + Py_XDECREF(shift); + Py_XDECREF(mask); + Py_XDECREF(stepval); +#endif + if (unlikely(ret)) + return (long) -1; + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyLong_From_long(long value) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const long neg_one = (long) -1, const_zero = (long) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyLong_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#if defined(HAVE_LONG_LONG) && !CYTHON_COMPILING_IN_PYPY + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyLong_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + unsigned char *bytes = (unsigned char *)&value; +#if !CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX >= 0x030d00A4 + if (is_unsigned) { + return PyLong_FromUnsignedNativeBytes(bytes, sizeof(value), -1); + } else { + return PyLong_FromNativeBytes(bytes, sizeof(value), -1); + } +#elif !CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030d0000 + int one = 1; int little = (int)*(unsigned char *)&one; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); +#else + int one = 1; int little = (int)*(unsigned char *)&one; + PyObject *from_bytes, *result = NULL, *kwds = NULL; + PyObject *py_bytes = NULL, *order_str = NULL; + from_bytes = PyObject_GetAttrString((PyObject*)&PyLong_Type, "from_bytes"); + if (!from_bytes) return NULL; + py_bytes = PyBytes_FromStringAndSize((char*)bytes, sizeof(long)); + if (!py_bytes) goto limited_bad; + order_str = PyUnicode_FromString(little ? "little" : "big"); + if (!order_str) goto limited_bad; + { + PyObject *args[3+(CYTHON_VECTORCALL ? 1 : 0)] = { NULL, py_bytes, order_str }; + if (!is_unsigned) { + kwds = __Pyx_MakeVectorcallBuilderKwds(1); + if (!kwds) goto limited_bad; + if (__Pyx_VectorcallBuilder_AddArgStr("signed", __Pyx_NewRef(Py_True), kwds, args+3, 0) < 0) goto limited_bad; + } + result = __Pyx_Object_Vectorcall_CallFromBuilder(from_bytes, args+1, 2 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET, kwds); + } + limited_bad: + Py_XDECREF(kwds); + Py_XDECREF(order_str); + Py_XDECREF(py_bytes); + Py_XDECREF(from_bytes); + return result; +#endif + } +} + +/* CIntFromPy */ + static CYTHON_INLINE int __Pyx_PyLong_As_int(PyObject *x) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const int neg_one = (int) -1, const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (unlikely(!PyLong_Check(x))) { + int val; + PyObject *tmp = __Pyx_PyNumber_Long(x); + if (!tmp) return (int) -1; + val = __Pyx_PyLong_As_int(tmp); + Py_DECREF(tmp); + return val; + } + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + if (unlikely(__Pyx_PyLong_IsNeg(x))) { + goto raise_neg_overflow; + } else if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_DigitCount(x)) { + case 2: + if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) >= 2 * PyLong_SHIFT)) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) >= 3 * PyLong_SHIFT)) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) >= 4 * PyLong_SHIFT)) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if ((sizeof(int) <= sizeof(unsigned long))) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(int) <= sizeof(unsigned PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_SignedDigitCount(x)) { + case -2: + if ((8 * sizeof(int) - 1 > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } + } +#endif + if ((sizeof(int) <= sizeof(long))) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(int) <= sizeof(PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { + int val; + int ret = -1; +#if PY_VERSION_HEX >= 0x030d00A6 && !CYTHON_COMPILING_IN_LIMITED_API + Py_ssize_t bytes_copied = PyLong_AsNativeBytes( + x, &val, sizeof(val), Py_ASNATIVEBYTES_NATIVE_ENDIAN | (is_unsigned ? Py_ASNATIVEBYTES_UNSIGNED_BUFFER | Py_ASNATIVEBYTES_REJECT_NEGATIVE : 0)); + if (unlikely(bytes_copied == -1)) { + } else if (unlikely(bytes_copied > (Py_ssize_t) sizeof(val))) { + goto raise_overflow; + } else { + ret = 0; + } +#elif PY_VERSION_HEX < 0x030d0000 && !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + ret = _PyLong_AsByteArray((PyLongObject *)x, + bytes, sizeof(val), + is_little, !is_unsigned); +#else + PyObject *v; + PyObject *stepval = NULL, *mask = NULL, *shift = NULL; + int bits, remaining_bits, is_negative = 0; + int chunk_size = (sizeof(long) < 8) ? 30 : 62; + if (likely(PyLong_CheckExact(x))) { + v = __Pyx_NewRef(x); + } else { + v = PyNumber_Long(x); + if (unlikely(!v)) return (int) -1; + assert(PyLong_CheckExact(v)); + } + { + int result = PyObject_RichCompareBool(v, Py_False, Py_LT); + if (unlikely(result < 0)) { + Py_DECREF(v); + return (int) -1; + } + is_negative = result == 1; + } + if (is_unsigned && unlikely(is_negative)) { + Py_DECREF(v); + goto raise_neg_overflow; + } else if (is_negative) { + stepval = PyNumber_Invert(v); + Py_DECREF(v); + if (unlikely(!stepval)) + return (int) -1; + } else { + stepval = v; + } + v = NULL; + val = (int) 0; + mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; + shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; + for (bits = 0; bits < (int) sizeof(int) * 8 - chunk_size; bits += chunk_size) { + PyObject *tmp, *digit; + long idigit; + digit = PyNumber_And(stepval, mask); + if (unlikely(!digit)) goto done; + idigit = PyLong_AsLong(digit); + Py_DECREF(digit); + if (unlikely(idigit < 0)) goto done; + val |= ((int) idigit) << bits; + tmp = PyNumber_Rshift(stepval, shift); + if (unlikely(!tmp)) goto done; + Py_DECREF(stepval); stepval = tmp; + } + Py_DECREF(shift); shift = NULL; + Py_DECREF(mask); mask = NULL; + { + long idigit = PyLong_AsLong(stepval); + if (unlikely(idigit < 0)) goto done; + remaining_bits = ((int) sizeof(int) * 8) - bits - (is_unsigned ? 0 : 1); + if (unlikely(idigit >= (1L << remaining_bits))) + goto raise_overflow; + val |= ((int) idigit) << bits; + } + if (!is_unsigned) { + if (unlikely(val & (((int) 1) << (sizeof(int) * 8 - 1)))) + goto raise_overflow; + if (is_negative) + val = ~val; + } + ret = 0; + done: + Py_XDECREF(shift); + Py_XDECREF(mask); + Py_XDECREF(stepval); +#endif + if (unlikely(ret)) + return (int) -1; + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyLong_From_int(int value) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const int neg_one = (int) -1, const_zero = (int) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyLong_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#if defined(HAVE_LONG_LONG) && !CYTHON_COMPILING_IN_PYPY + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyLong_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + unsigned char *bytes = (unsigned char *)&value; +#if !CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX >= 0x030d00A4 + if (is_unsigned) { + return PyLong_FromUnsignedNativeBytes(bytes, sizeof(value), -1); + } else { + return PyLong_FromNativeBytes(bytes, sizeof(value), -1); + } +#elif !CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030d0000 + int one = 1; int little = (int)*(unsigned char *)&one; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); +#else + int one = 1; int little = (int)*(unsigned char *)&one; + PyObject *from_bytes, *result = NULL, *kwds = NULL; + PyObject *py_bytes = NULL, *order_str = NULL; + from_bytes = PyObject_GetAttrString((PyObject*)&PyLong_Type, "from_bytes"); + if (!from_bytes) return NULL; + py_bytes = PyBytes_FromStringAndSize((char*)bytes, sizeof(int)); + if (!py_bytes) goto limited_bad; + order_str = PyUnicode_FromString(little ? "little" : "big"); + if (!order_str) goto limited_bad; + { + PyObject *args[3+(CYTHON_VECTORCALL ? 1 : 0)] = { NULL, py_bytes, order_str }; + if (!is_unsigned) { + kwds = __Pyx_MakeVectorcallBuilderKwds(1); + if (!kwds) goto limited_bad; + if (__Pyx_VectorcallBuilder_AddArgStr("signed", __Pyx_NewRef(Py_True), kwds, args+3, 0) < 0) goto limited_bad; + } + result = __Pyx_Object_Vectorcall_CallFromBuilder(from_bytes, args+1, 2 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET, kwds); + } + limited_bad: + Py_XDECREF(kwds); + Py_XDECREF(order_str); + Py_XDECREF(py_bytes); + Py_XDECREF(from_bytes); + return result; +#endif + } +} + +/* CIntFromPy */ + static CYTHON_INLINE char __Pyx_PyLong_As_char(PyObject *x) { +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wconversion" +#endif + const char neg_one = (char) -1, const_zero = (char) 0; +#ifdef __Pyx_HAS_GCC_DIAGNOSTIC +#pragma GCC diagnostic pop +#endif + const int is_unsigned = neg_one > const_zero; + if (unlikely(!PyLong_Check(x))) { + char val; + PyObject *tmp = __Pyx_PyNumber_Long(x); + if (!tmp) return (char) -1; + val = __Pyx_PyLong_As_char(tmp); + Py_DECREF(tmp); + return val; + } + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + if (unlikely(__Pyx_PyLong_IsNeg(x))) { + goto raise_neg_overflow; + } else if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_DigitCount(x)) { + case 2: + if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) >= 2 * PyLong_SHIFT)) { + return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 3: + if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) >= 3 * PyLong_SHIFT)) { + return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + case 4: + if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) >= 4 * PyLong_SHIFT)) { + return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); + } + } + break; + } + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (char) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if ((sizeof(char) <= sizeof(unsigned long))) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(char) <= sizeof(unsigned PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + if (__Pyx_PyLong_IsCompact(x)) { + __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) + } else { + const digit* digits = __Pyx_PyLong_Digits(x); + assert(__Pyx_PyLong_DigitCount(x) > 1); + switch (__Pyx_PyLong_SignedDigitCount(x)) { + case -2: + if ((8 * sizeof(char) - 1 > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { + return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 2: + if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { + return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -3: + if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { + return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 3: + if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { + return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case -4: + if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) { + return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + case 4: + if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) { + if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { + __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) { + return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); + } + } + break; + } + } +#endif + if ((sizeof(char) <= sizeof(long))) { + __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if ((sizeof(char) <= sizeof(PY_LONG_LONG))) { + __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { + char val; + int ret = -1; +#if PY_VERSION_HEX >= 0x030d00A6 && !CYTHON_COMPILING_IN_LIMITED_API + Py_ssize_t bytes_copied = PyLong_AsNativeBytes( + x, &val, sizeof(val), Py_ASNATIVEBYTES_NATIVE_ENDIAN | (is_unsigned ? Py_ASNATIVEBYTES_UNSIGNED_BUFFER | Py_ASNATIVEBYTES_REJECT_NEGATIVE : 0)); + if (unlikely(bytes_copied == -1)) { + } else if (unlikely(bytes_copied > (Py_ssize_t) sizeof(val))) { + goto raise_overflow; + } else { + ret = 0; + } +#elif PY_VERSION_HEX < 0x030d0000 && !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + ret = _PyLong_AsByteArray((PyLongObject *)x, + bytes, sizeof(val), + is_little, !is_unsigned); +#else + PyObject *v; + PyObject *stepval = NULL, *mask = NULL, *shift = NULL; + int bits, remaining_bits, is_negative = 0; + int chunk_size = (sizeof(long) < 8) ? 30 : 62; + if (likely(PyLong_CheckExact(x))) { + v = __Pyx_NewRef(x); + } else { + v = PyNumber_Long(x); + if (unlikely(!v)) return (char) -1; + assert(PyLong_CheckExact(v)); + } + { + int result = PyObject_RichCompareBool(v, Py_False, Py_LT); + if (unlikely(result < 0)) { + Py_DECREF(v); + return (char) -1; + } + is_negative = result == 1; + } + if (is_unsigned && unlikely(is_negative)) { + Py_DECREF(v); + goto raise_neg_overflow; + } else if (is_negative) { + stepval = PyNumber_Invert(v); + Py_DECREF(v); + if (unlikely(!stepval)) + return (char) -1; + } else { + stepval = v; + } + v = NULL; + val = (char) 0; + mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; + shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; + for (bits = 0; bits < (int) sizeof(char) * 8 - chunk_size; bits += chunk_size) { + PyObject *tmp, *digit; + long idigit; + digit = PyNumber_And(stepval, mask); + if (unlikely(!digit)) goto done; + idigit = PyLong_AsLong(digit); + Py_DECREF(digit); + if (unlikely(idigit < 0)) goto done; + val |= ((char) idigit) << bits; + tmp = PyNumber_Rshift(stepval, shift); + if (unlikely(!tmp)) goto done; + Py_DECREF(stepval); stepval = tmp; + } + Py_DECREF(shift); shift = NULL; + Py_DECREF(mask); mask = NULL; + { + long idigit = PyLong_AsLong(stepval); + if (unlikely(idigit < 0)) goto done; + remaining_bits = ((int) sizeof(char) * 8) - bits - (is_unsigned ? 0 : 1); + if (unlikely(idigit >= (1L << remaining_bits))) + goto raise_overflow; + val |= ((char) idigit) << bits; + } + if (!is_unsigned) { + if (unlikely(val & (((char) 1) << (sizeof(char) * 8 - 1)))) + goto raise_overflow; + if (is_negative) + val = ~val; + } + ret = 0; + done: + Py_XDECREF(shift); + Py_XDECREF(mask); + Py_XDECREF(stepval); +#endif + if (unlikely(ret)) + return (char) -1; + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to char"); + return (char) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to char"); + return (char) -1; +} + +/* FormatTypeName */ + #if CYTHON_COMPILING_IN_LIMITED_API && __PYX_LIMITED_VERSION_HEX < 0x030d0000 +static __Pyx_TypeName +__Pyx_PyType_GetFullyQualifiedName(PyTypeObject* tp) +{ + PyObject *module = NULL, *name = NULL, *result = NULL; + #if __PYX_LIMITED_VERSION_HEX < 0x030b0000 + name = __Pyx_PyObject_GetAttrStr((PyObject *)tp, + __pyx_mstate_global->__pyx_n_u_qualname); + #else + name = PyType_GetQualName(tp); + #endif + if (unlikely(name == NULL) || unlikely(!PyUnicode_Check(name))) goto bad; + module = __Pyx_PyObject_GetAttrStr((PyObject *)tp, + __pyx_mstate_global->__pyx_n_u_module); + if (unlikely(module == NULL) || unlikely(!PyUnicode_Check(module))) goto bad; + if (PyUnicode_CompareWithASCIIString(module, "builtins") == 0) { + result = name; + name = NULL; + goto done; + } + result = PyUnicode_FromFormat("%U.%U", module, name); + if (unlikely(result == NULL)) goto bad; + done: + Py_XDECREF(name); + Py_XDECREF(module); + return result; + bad: + PyErr_Clear(); + if (name) { + result = name; + name = NULL; + } else { + result = __Pyx_NewRef(__pyx_mstate_global->__pyx_kp_u__7); + } + goto done; +} +#endif + +/* GetRuntimeVersion */ + static unsigned long __Pyx_get_runtime_version(void) { +#if __PYX_LIMITED_VERSION_HEX >= 0x030b0000 + return Py_Version & ~0xFFUL; +#else + static unsigned long __Pyx_cached_runtime_version = 0; + if (__Pyx_cached_runtime_version == 0) { + const char* rt_version = Py_GetVersion(); + unsigned long version = 0; + unsigned long factor = 0x01000000UL; + unsigned int digit = 0; + int i = 0; + while (factor) { + while ('0' <= rt_version[i] && rt_version[i] <= '9') { + digit = digit * 10 + (unsigned int) (rt_version[i] - '0'); + ++i; + } + version += factor * digit; + if (rt_version[i] != '.') + break; + digit = 0; + factor >>= 8; + ++i; + } + __Pyx_cached_runtime_version = version; + } + return __Pyx_cached_runtime_version; +#endif +} + +/* CheckBinaryVersion */ + static int __Pyx_check_binary_version(unsigned long ct_version, unsigned long rt_version, int allow_newer) { + const unsigned long MAJOR_MINOR = 0xFFFF0000UL; + if ((rt_version & MAJOR_MINOR) == (ct_version & MAJOR_MINOR)) + return 0; + if (likely(allow_newer && (rt_version & MAJOR_MINOR) > (ct_version & MAJOR_MINOR))) + return 1; + { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compile time Python version %d.%d " + "of module '%.100s' " + "%s " + "runtime version %d.%d", + (int) (ct_version >> 24), (int) ((ct_version >> 16) & 0xFF), + __Pyx_MODULE_NAME, + (allow_newer) ? "was newer than" : "does not match", + (int) (rt_version >> 24), (int) ((rt_version >> 16) & 0xFF) + ); + return PyErr_WarnEx(NULL, message, 1); + } +} + +/* NewCodeObj */ + #if CYTHON_COMPILING_IN_LIMITED_API + static PyObject* __Pyx__PyCode_New(int a, int p, int k, int l, int s, int f, + PyObject *code, PyObject *c, PyObject* n, PyObject *v, + PyObject *fv, PyObject *cell, PyObject* fn, + PyObject *name, int fline, PyObject *lnos) { + PyObject *exception_table = NULL; + PyObject *types_module=NULL, *code_type=NULL, *result=NULL; + #if __PYX_LIMITED_VERSION_HEX < 0x030b0000 + PyObject *version_info; + PyObject *py_minor_version = NULL; + #endif + long minor_version = 0; + PyObject *type, *value, *traceback; + PyErr_Fetch(&type, &value, &traceback); + #if __PYX_LIMITED_VERSION_HEX >= 0x030b0000 + minor_version = 11; + #else + if (!(version_info = PySys_GetObject("version_info"))) goto end; + if (!(py_minor_version = PySequence_GetItem(version_info, 1))) goto end; + minor_version = PyLong_AsLong(py_minor_version); + Py_DECREF(py_minor_version); + if (minor_version == -1 && PyErr_Occurred()) goto end; + #endif + if (!(types_module = PyImport_ImportModule("types"))) goto end; + if (!(code_type = PyObject_GetAttrString(types_module, "CodeType"))) goto end; + if (minor_version <= 7) { + (void)p; + result = PyObject_CallFunction(code_type, "iiiiiOOOOOOiOOO", a, k, l, s, f, code, + c, n, v, fn, name, fline, lnos, fv, cell); + } else if (minor_version <= 10) { + result = PyObject_CallFunction(code_type, "iiiiiiOOOOOOiOOO", a,p, k, l, s, f, code, + c, n, v, fn, name, fline, lnos, fv, cell); + } else { + if (!(exception_table = PyBytes_FromStringAndSize(NULL, 0))) goto end; + result = PyObject_CallFunction(code_type, "iiiiiiOOOOOOOiOOOO", a,p, k, l, s, f, code, + c, n, v, fn, name, name, fline, lnos, exception_table, fv, cell); + } + end: + Py_XDECREF(code_type); + Py_XDECREF(exception_table); + Py_XDECREF(types_module); + if (type) { + PyErr_Restore(type, value, traceback); + } + return result; + } +#elif PY_VERSION_HEX >= 0x030B0000 + static PyCodeObject* __Pyx__PyCode_New(int a, int p, int k, int l, int s, int f, + PyObject *code, PyObject *c, PyObject* n, PyObject *v, + PyObject *fv, PyObject *cell, PyObject* fn, + PyObject *name, int fline, PyObject *lnos) { + PyCodeObject *result; + result = + #if PY_VERSION_HEX >= 0x030C0000 + PyUnstable_Code_NewWithPosOnlyArgs + #else + PyCode_NewWithPosOnlyArgs + #endif + (a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, name, fline, lnos, __pyx_mstate_global->__pyx_empty_bytes); + #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030c00A1 + if (likely(result)) + result->_co_firsttraceable = 0; + #endif + return result; + } +#elif PY_VERSION_HEX >= 0x030800B2 && !CYTHON_COMPILING_IN_PYPY + #define __Pyx__PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_NewWithPosOnlyArgs(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#else + #define __Pyx__PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) +#endif +static PyObject* __Pyx_PyCode_New( + const __Pyx_PyCode_New_function_description descr, + PyObject * const *varnames, + PyObject *filename, + PyObject *funcname, + const char *line_table, + PyObject *tuple_dedup_map +) { + PyObject *code_obj = NULL, *varnames_tuple_dedup = NULL, *code_bytes = NULL, *line_table_bytes = NULL; + Py_ssize_t var_count = (Py_ssize_t) descr.nlocals; + PyObject *varnames_tuple = PyTuple_New(var_count); + if (unlikely(!varnames_tuple)) return NULL; + for (Py_ssize_t i=0; i < var_count; i++) { + Py_INCREF(varnames[i]); + if (__Pyx_PyTuple_SET_ITEM(varnames_tuple, i, varnames[i]) != (0)) goto done; + } + #if CYTHON_COMPILING_IN_LIMITED_API + varnames_tuple_dedup = PyDict_GetItem(tuple_dedup_map, varnames_tuple); + if (!varnames_tuple_dedup) { + if (unlikely(PyDict_SetItem(tuple_dedup_map, varnames_tuple, varnames_tuple) < 0)) goto done; + varnames_tuple_dedup = varnames_tuple; + } + #else + varnames_tuple_dedup = PyDict_SetDefault(tuple_dedup_map, varnames_tuple, varnames_tuple); + if (unlikely(!varnames_tuple_dedup)) goto done; + #endif + #if CYTHON_AVOID_BORROWED_REFS + Py_INCREF(varnames_tuple_dedup); + #endif + if (__PYX_LIMITED_VERSION_HEX >= (0x030b0000) && line_table != NULL + && !CYTHON_COMPILING_IN_GRAAL) { + line_table_bytes = PyBytes_FromStringAndSize(line_table, descr.line_table_length); + if (unlikely(!line_table_bytes)) goto done; + Py_ssize_t code_len = (descr.line_table_length * 2 + 4) & ~3; + code_bytes = PyBytes_FromStringAndSize(NULL, code_len); + if (unlikely(!code_bytes)) goto done; + char* c_code_bytes = PyBytes_AsString(code_bytes); + if (unlikely(!c_code_bytes)) goto done; + memset(c_code_bytes, 0, (size_t) code_len); + } + code_obj = (PyObject*) __Pyx__PyCode_New( + (int) descr.argcount, + (int) descr.num_posonly_args, + (int) descr.num_kwonly_args, + (int) descr.nlocals, + 0, + (int) descr.flags, + code_bytes ? code_bytes : __pyx_mstate_global->__pyx_empty_bytes, + __pyx_mstate_global->__pyx_empty_tuple, + __pyx_mstate_global->__pyx_empty_tuple, + varnames_tuple_dedup, + __pyx_mstate_global->__pyx_empty_tuple, + __pyx_mstate_global->__pyx_empty_tuple, + filename, + funcname, + (int) descr.first_line, + (__PYX_LIMITED_VERSION_HEX >= (0x030b0000) && line_table_bytes) ? line_table_bytes : __pyx_mstate_global->__pyx_empty_bytes + ); +done: + Py_XDECREF(code_bytes); + Py_XDECREF(line_table_bytes); + #if CYTHON_AVOID_BORROWED_REFS + Py_XDECREF(varnames_tuple_dedup); + #endif + Py_DECREF(varnames_tuple); + return code_obj; +} + +/* InitStrings */ + static int __Pyx_InitStrings(__Pyx_StringTabEntry const *t, PyObject **target, const char* const* encoding_names) { + while (t->s) { + PyObject *str; + if (t->is_unicode) { + if (t->intern) { + str = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + str = PyUnicode_Decode(t->s, t->n - 1, encoding_names[t->encoding], NULL); + } else { + str = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + str = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + if (!str) + return -1; + *target = str; + if (PyObject_Hash(str) == -1) + return -1; + ++t; + ++target; + } + return 0; +} + +#include +static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s) { + size_t len = strlen(s); + if (unlikely(len > (size_t) PY_SSIZE_T_MAX)) { + PyErr_SetString(PyExc_OverflowError, "byte string is too long"); + return -1; + } + return (Py_ssize_t) len; +} +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + Py_ssize_t len = __Pyx_ssize_strlen(c_str); + if (unlikely(len < 0)) return NULL; + return __Pyx_PyUnicode_FromStringAndSize(c_str, len); +} +static CYTHON_INLINE PyObject* __Pyx_PyByteArray_FromString(const char* c_str) { + Py_ssize_t len = __Pyx_ssize_strlen(c_str); + if (unlikely(len < 0)) return NULL; + return PyByteArray_FromStringAndSize(c_str, len); +} +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 +static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; +#if CYTHON_COMPILING_IN_LIMITED_API + { + const char* result; + Py_ssize_t unicode_length; + CYTHON_MAYBE_UNUSED_VAR(unicode_length); // only for __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + #if __PYX_LIMITED_VERSION_HEX < 0x030A0000 + if (unlikely(PyArg_Parse(o, "s#", &result, length) < 0)) return NULL; + #else + result = PyUnicode_AsUTF8AndSize(o, length); + #endif + #if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + unicode_length = PyUnicode_GetLength(o); + if (unlikely(unicode_length < 0)) return NULL; + if (unlikely(unicode_length != *length)) { + PyUnicode_AsASCIIString(o); + return NULL; + } + #endif + return result; + } +#else +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (likely(PyUnicode_IS_ASCII(o))) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +#endif +} +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 + if (PyUnicode_Check(o)) { + return __Pyx_PyUnicode_AsStringAndSize(o, length); + } else +#endif + if (PyByteArray_Check(o)) { +#if (CYTHON_ASSUME_SAFE_SIZE && CYTHON_ASSUME_SAFE_MACROS) || (CYTHON_COMPILING_IN_PYPY && (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))) + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); +#else + *length = PyByteArray_Size(o); + if (*length == -1) return NULL; + return PyByteArray_AsString(o); +#endif + } else + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { + int retval; + if (unlikely(!x)) return -1; + retval = __Pyx_PyObject_IsTrue(x); + Py_DECREF(x); + return retval; +} +static PyObject* __Pyx_PyNumber_LongWrongResultType(PyObject* result) { + __Pyx_TypeName result_type_name = __Pyx_PyType_GetFullyQualifiedName(Py_TYPE(result)); + if (PyLong_Check(result)) { + if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, + "__int__ returned non-int (type " __Pyx_FMT_TYPENAME "). " + "The ability to return an instance of a strict subclass of int is deprecated, " + "and may be removed in a future version of Python.", + result_type_name)) { + __Pyx_DECREF_TypeName(result_type_name); + Py_DECREF(result); + return NULL; + } + __Pyx_DECREF_TypeName(result_type_name); + return result; + } + PyErr_Format(PyExc_TypeError, + "__int__ returned non-int (type " __Pyx_FMT_TYPENAME ")", + result_type_name); + __Pyx_DECREF_TypeName(result_type_name); + Py_DECREF(result); + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Long(PyObject* x) { +#if CYTHON_USE_TYPE_SLOTS + PyNumberMethods *m; +#endif + PyObject *res = NULL; + if (likely(PyLong_Check(x))) + return __Pyx_NewRef(x); +#if CYTHON_USE_TYPE_SLOTS + m = Py_TYPE(x)->tp_as_number; + if (likely(m && m->nb_int)) { + res = m->nb_int(x); + } +#else + if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { + res = PyNumber_Long(x); + } +#endif + if (likely(res)) { + if (unlikely(!PyLong_CheckExact(res))) { + return __Pyx_PyNumber_LongWrongResultType(res); + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + if (likely(__Pyx_PyLong_IsCompact(b))) { + return __Pyx_PyLong_CompactValue(b); + } else { + const digit* digits = __Pyx_PyLong_Digits(b); + const Py_ssize_t size = __Pyx_PyLong_SignedDigitCount(b); + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyLong_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject* o) { + if (sizeof(Py_hash_t) == sizeof(Py_ssize_t)) { + return (Py_hash_t) __Pyx_PyIndex_AsSsize_t(o); + } else { + Py_ssize_t ival; + PyObject *x; + x = PyNumber_Index(o); + if (!x) return -1; + ival = PyLong_AsLong(x); + Py_DECREF(x); + return ival; + } +} +static CYTHON_INLINE PyObject *__Pyx_Owned_Py_None(int b) { + CYTHON_UNUSED_VAR(b); + return __Pyx_NewRef(Py_None); +} +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { + return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); +} +static CYTHON_INLINE PyObject * __Pyx_PyLong_FromSize_t(size_t ival) { + return PyLong_FromSize_t(ival); +} +#if CYTHON_USE_PYLONG_INTERNALS +static CYTHON_INLINE int __Pyx_PyLong_CompactAsLong(PyObject *x, long *return_value) { + if (unlikely(!__Pyx_PyLong_IsCompact(x))) + return 0; + Py_ssize_t value = __Pyx_PyLong_CompactValue(x); + if ((sizeof(long) < sizeof(Py_ssize_t)) && unlikely(value != (long) value)) + return 0; + *return_value = (long) value; + return 1; +} +#endif + + + /* MultiPhaseInitModuleState */ + #if CYTHON_PEP489_MULTI_PHASE_INIT && CYTHON_USE_MODULE_STATE +#ifndef CYTHON_MODULE_STATE_LOOKUP_THREAD_SAFE +#if (CYTHON_COMPILING_IN_LIMITED_API || PY_VERSION_HEX >= 0x030C0000) + #define CYTHON_MODULE_STATE_LOOKUP_THREAD_SAFE 1 +#else + #define CYTHON_MODULE_STATE_LOOKUP_THREAD_SAFE 0 +#endif +#endif +#if CYTHON_MODULE_STATE_LOOKUP_THREAD_SAFE && !CYTHON_ATOMICS +#error "Module state with PEP489 requires atomics. Currently that's one of\ + C11, C++11, gcc atomic intrinsics or MSVC atomic intrinsics" +#endif +#if !CYTHON_MODULE_STATE_LOOKUP_THREAD_SAFE +#define __Pyx_ModuleStateLookup_Lock() +#define __Pyx_ModuleStateLookup_Unlock() +#elif !CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX >= 0x030d0000 +static PyMutex __Pyx_ModuleStateLookup_mutex = {0}; +#define __Pyx_ModuleStateLookup_Lock() PyMutex_Lock(&__Pyx_ModuleStateLookup_mutex) +#define __Pyx_ModuleStateLookup_Unlock() PyMutex_Unlock(&__Pyx_ModuleStateLookup_mutex) +#elif defined(__cplusplus) && __cplusplus >= 201103L +#include +static std::mutex __Pyx_ModuleStateLookup_mutex; +#define __Pyx_ModuleStateLookup_Lock() __Pyx_ModuleStateLookup_mutex.lock() +#define __Pyx_ModuleStateLookup_Unlock() __Pyx_ModuleStateLookup_mutex.unlock() +#elif defined(__STDC_VERSION__) && (__STDC_VERSION__ > 201112L) && !defined(__STDC_NO_THREADS__) +#include +static mtx_t __Pyx_ModuleStateLookup_mutex; +static once_flag __Pyx_ModuleStateLookup_mutex_once_flag = ONCE_FLAG_INIT; +static void __Pyx_ModuleStateLookup_initialize_mutex(void) { + mtx_init(&__Pyx_ModuleStateLookup_mutex, mtx_plain); +} +#define __Pyx_ModuleStateLookup_Lock()\ + call_once(&__Pyx_ModuleStateLookup_mutex_once_flag, __Pyx_ModuleStateLookup_initialize_mutex);\ + mtx_lock(&__Pyx_ModuleStateLookup_mutex) +#define __Pyx_ModuleStateLookup_Unlock() mtx_unlock(&__Pyx_ModuleStateLookup_mutex) +#elif defined(HAVE_PTHREAD_H) +#include +static pthread_mutex_t __Pyx_ModuleStateLookup_mutex = PTHREAD_MUTEX_INITIALIZER; +#define __Pyx_ModuleStateLookup_Lock() pthread_mutex_lock(&__Pyx_ModuleStateLookup_mutex) +#define __Pyx_ModuleStateLookup_Unlock() pthread_mutex_unlock(&__Pyx_ModuleStateLookup_mutex) +#elif defined(_WIN32) +#include // synchapi.h on its own doesn't work +static SRWLOCK __Pyx_ModuleStateLookup_mutex = SRWLOCK_INIT; +#define __Pyx_ModuleStateLookup_Lock() AcquireSRWLockExclusive(&__Pyx_ModuleStateLookup_mutex) +#define __Pyx_ModuleStateLookup_Unlock() ReleaseSRWLockExclusive(&__Pyx_ModuleStateLookup_mutex) +#else +#error "No suitable lock available for CYTHON_MODULE_STATE_LOOKUP_THREAD_SAFE.\ + Requires C standard >= C11, or C++ standard >= C++11,\ + or pthreads, or the Windows 32 API, or Python >= 3.13." +#endif +typedef struct { + int64_t id; + PyObject *module; +} __Pyx_InterpreterIdAndModule; +typedef struct { + char interpreter_id_as_index; + Py_ssize_t count; + Py_ssize_t allocated; + __Pyx_InterpreterIdAndModule table[1]; +} __Pyx_ModuleStateLookupData; +#define __PYX_MODULE_STATE_LOOKUP_SMALL_SIZE 32 +#if CYTHON_MODULE_STATE_LOOKUP_THREAD_SAFE +static __pyx_atomic_int_type __Pyx_ModuleStateLookup_read_counter = 0; +#endif +#if CYTHON_MODULE_STATE_LOOKUP_THREAD_SAFE +static __pyx_atomic_ptr_type __Pyx_ModuleStateLookup_data = 0; +#else +static __Pyx_ModuleStateLookupData* __Pyx_ModuleStateLookup_data = NULL; +#endif +static __Pyx_InterpreterIdAndModule* __Pyx_State_FindModuleStateLookupTableLowerBound( + __Pyx_InterpreterIdAndModule* table, + Py_ssize_t count, + int64_t interpreterId) { + __Pyx_InterpreterIdAndModule* begin = table; + __Pyx_InterpreterIdAndModule* end = begin + count; + if (begin->id == interpreterId) { + return begin; + } + while ((end - begin) > __PYX_MODULE_STATE_LOOKUP_SMALL_SIZE) { + __Pyx_InterpreterIdAndModule* halfway = begin + (end - begin)/2; + if (halfway->id == interpreterId) { + return halfway; + } + if (halfway->id < interpreterId) { + begin = halfway; + } else { + end = halfway; + } + } + for (; begin < end; ++begin) { + if (begin->id >= interpreterId) return begin; + } + return begin; +} +static PyObject *__Pyx_State_FindModule(CYTHON_UNUSED void* dummy) { + int64_t interpreter_id = PyInterpreterState_GetID(__Pyx_PyInterpreterState_Get()); + if (interpreter_id == -1) return NULL; +#if CYTHON_MODULE_STATE_LOOKUP_THREAD_SAFE + __Pyx_ModuleStateLookupData* data = (__Pyx_ModuleStateLookupData*)__pyx_atomic_pointer_load_relaxed(&__Pyx_ModuleStateLookup_data); + { + __pyx_atomic_incr_acq_rel(&__Pyx_ModuleStateLookup_read_counter); + if (likely(data)) { + __Pyx_ModuleStateLookupData* new_data = (__Pyx_ModuleStateLookupData*)__pyx_atomic_pointer_load_acquire(&__Pyx_ModuleStateLookup_data); + if (likely(data == new_data)) { + goto read_finished; + } + } + __pyx_atomic_decr_acq_rel(&__Pyx_ModuleStateLookup_read_counter); + __Pyx_ModuleStateLookup_Lock(); + __pyx_atomic_incr_relaxed(&__Pyx_ModuleStateLookup_read_counter); + data = (__Pyx_ModuleStateLookupData*)__pyx_atomic_pointer_load_relaxed(&__Pyx_ModuleStateLookup_data); + __Pyx_ModuleStateLookup_Unlock(); + } + read_finished:; +#else + __Pyx_ModuleStateLookupData* data = __Pyx_ModuleStateLookup_data; +#endif + __Pyx_InterpreterIdAndModule* found = NULL; + if (unlikely(!data)) goto end; + if (data->interpreter_id_as_index) { + if (interpreter_id < data->count) { + found = data->table+interpreter_id; + } + } else { + found = __Pyx_State_FindModuleStateLookupTableLowerBound( + data->table, data->count, interpreter_id); + } + end: + { + PyObject *result=NULL; + if (found && found->id == interpreter_id) { + result = found->module; + } +#if CYTHON_MODULE_STATE_LOOKUP_THREAD_SAFE + __pyx_atomic_decr_acq_rel(&__Pyx_ModuleStateLookup_read_counter); +#endif + return result; + } +} +#if CYTHON_MODULE_STATE_LOOKUP_THREAD_SAFE +static void __Pyx_ModuleStateLookup_wait_until_no_readers(void) { + while (__pyx_atomic_load(&__Pyx_ModuleStateLookup_read_counter) != 0); +} +#else +#define __Pyx_ModuleStateLookup_wait_until_no_readers() +#endif +static int __Pyx_State_AddModuleInterpIdAsIndex(__Pyx_ModuleStateLookupData **old_data, PyObject* module, int64_t interpreter_id) { + Py_ssize_t to_allocate = (*old_data)->allocated; + while (to_allocate <= interpreter_id) { + if (to_allocate == 0) to_allocate = 1; + else to_allocate *= 2; + } + __Pyx_ModuleStateLookupData *new_data = *old_data; + if (to_allocate != (*old_data)->allocated) { + new_data = (__Pyx_ModuleStateLookupData *)realloc( + *old_data, + sizeof(__Pyx_ModuleStateLookupData)+(to_allocate-1)*sizeof(__Pyx_InterpreterIdAndModule)); + if (!new_data) { + PyErr_NoMemory(); + return -1; + } + for (Py_ssize_t i = new_data->allocated; i < to_allocate; ++i) { + new_data->table[i].id = i; + new_data->table[i].module = NULL; + } + new_data->allocated = to_allocate; + } + new_data->table[interpreter_id].module = module; + if (new_data->count < interpreter_id+1) { + new_data->count = interpreter_id+1; + } + *old_data = new_data; + return 0; +} +static void __Pyx_State_ConvertFromInterpIdAsIndex(__Pyx_ModuleStateLookupData *data) { + __Pyx_InterpreterIdAndModule *read = data->table; + __Pyx_InterpreterIdAndModule *write = data->table; + __Pyx_InterpreterIdAndModule *end = read + data->count; + for (; readmodule) { + write->id = read->id; + write->module = read->module; + ++write; + } + } + data->count = write - data->table; + for (; writeid = 0; + write->module = NULL; + } + data->interpreter_id_as_index = 0; +} +static int __Pyx_State_AddModule(PyObject* module, CYTHON_UNUSED void* dummy) { + int64_t interpreter_id = PyInterpreterState_GetID(__Pyx_PyInterpreterState_Get()); + if (interpreter_id == -1) return -1; + int result = 0; + __Pyx_ModuleStateLookup_Lock(); +#if CYTHON_MODULE_STATE_LOOKUP_THREAD_SAFE + __Pyx_ModuleStateLookupData *old_data = (__Pyx_ModuleStateLookupData *) + __pyx_atomic_pointer_exchange(&__Pyx_ModuleStateLookup_data, 0); +#else + __Pyx_ModuleStateLookupData *old_data = __Pyx_ModuleStateLookup_data; +#endif + __Pyx_ModuleStateLookupData *new_data = old_data; + if (!new_data) { + new_data = (__Pyx_ModuleStateLookupData *)calloc(1, sizeof(__Pyx_ModuleStateLookupData)); + if (!new_data) { + result = -1; + PyErr_NoMemory(); + goto end; + } + new_data->allocated = 1; + new_data->interpreter_id_as_index = 1; + } + __Pyx_ModuleStateLookup_wait_until_no_readers(); + if (new_data->interpreter_id_as_index) { + if (interpreter_id < __PYX_MODULE_STATE_LOOKUP_SMALL_SIZE) { + result = __Pyx_State_AddModuleInterpIdAsIndex(&new_data, module, interpreter_id); + goto end; + } + __Pyx_State_ConvertFromInterpIdAsIndex(new_data); + } + { + Py_ssize_t insert_at = 0; + { + __Pyx_InterpreterIdAndModule* lower_bound = __Pyx_State_FindModuleStateLookupTableLowerBound( + new_data->table, new_data->count, interpreter_id); + assert(lower_bound); + insert_at = lower_bound - new_data->table; + if (unlikely(insert_at < new_data->count && lower_bound->id == interpreter_id)) { + lower_bound->module = module; + goto end; // already in table, nothing more to do + } + } + if (new_data->count+1 >= new_data->allocated) { + Py_ssize_t to_allocate = (new_data->count+1)*2; + new_data = + (__Pyx_ModuleStateLookupData*)realloc( + new_data, + sizeof(__Pyx_ModuleStateLookupData) + + (to_allocate-1)*sizeof(__Pyx_InterpreterIdAndModule)); + if (!new_data) { + result = -1; + new_data = old_data; + PyErr_NoMemory(); + goto end; + } + new_data->allocated = to_allocate; + } + ++new_data->count; + int64_t last_id = interpreter_id; + PyObject *last_module = module; + for (Py_ssize_t i=insert_at; icount; ++i) { + int64_t current_id = new_data->table[i].id; + new_data->table[i].id = last_id; + last_id = current_id; + PyObject *current_module = new_data->table[i].module; + new_data->table[i].module = last_module; + last_module = current_module; + } + } + end: +#if CYTHON_MODULE_STATE_LOOKUP_THREAD_SAFE + __pyx_atomic_pointer_exchange(&__Pyx_ModuleStateLookup_data, new_data); +#else + __Pyx_ModuleStateLookup_data = new_data; +#endif + __Pyx_ModuleStateLookup_Unlock(); + return result; +} +static int __Pyx_State_RemoveModule(CYTHON_UNUSED void* dummy) { + int64_t interpreter_id = PyInterpreterState_GetID(__Pyx_PyInterpreterState_Get()); + if (interpreter_id == -1) return -1; + __Pyx_ModuleStateLookup_Lock(); +#if CYTHON_MODULE_STATE_LOOKUP_THREAD_SAFE + __Pyx_ModuleStateLookupData *data = (__Pyx_ModuleStateLookupData *) + __pyx_atomic_pointer_exchange(&__Pyx_ModuleStateLookup_data, 0); +#else + __Pyx_ModuleStateLookupData *data = __Pyx_ModuleStateLookup_data; +#endif + if (data->interpreter_id_as_index) { + if (interpreter_id < data->count) { + data->table[interpreter_id].module = NULL; + } + goto done; + } + { + __Pyx_ModuleStateLookup_wait_until_no_readers(); + __Pyx_InterpreterIdAndModule* lower_bound = __Pyx_State_FindModuleStateLookupTableLowerBound( + data->table, data->count, interpreter_id); + if (!lower_bound) goto done; + if (lower_bound->id != interpreter_id) goto done; + __Pyx_InterpreterIdAndModule *end = data->table+data->count; + for (;lower_boundid = (lower_bound+1)->id; + lower_bound->module = (lower_bound+1)->module; + } + } + --data->count; + if (data->count == 0) { + free(data); + data = NULL; + } + done: +#if CYTHON_MODULE_STATE_LOOKUP_THREAD_SAFE + __pyx_atomic_pointer_exchange(&__Pyx_ModuleStateLookup_data, data); +#else + __Pyx_ModuleStateLookup_data = data; +#endif + __Pyx_ModuleStateLookup_Unlock(); + return 0; +} +#endif + +/* #### Code section: utility_code_pragmas_end ### */ +#ifdef _MSC_VER +#pragma warning( pop ) +#endif + + + +/* #### Code section: end ### */ +#endif /* Py_PYTHON_H */ diff --git a/models/matris/matris/graph/cygraph.pyx b/models/matris/matris/graph/cygraph.pyx new file mode 100644 index 0000000000000000000000000000000000000000..ff11a69ae4c04dea2ffd4bb0f0751b7ffd96bba7 --- /dev/null +++ b/models/matris/matris/graph/cygraph.pyx @@ -0,0 +1,174 @@ +""" + This code is referenced from: https://github.com/CederGroupHub/chgnet/blob/main/chgnet/graph/converter.py + The original implementation can be found at the link above. +""" + +# cython: language_level=3 +# cython: initializedcheck=False +# cython: nonecheck=False +# cython: boundscheck=False +# cython: wraparound=False +# cython: cdivision=True +# cython: profile=False +# distutils: language = c + + +from . import radiusgraph +import numpy as np +from libc.stdlib cimport free + +cdef extern from 'fast_converter_libraries/create_graph.c': + ctypedef struct Node: + long index + LongToDirectedEdgeList* neighbors + long num_neighbors + + ctypedef struct NodeIndexPair: + long center + long neighbor + + ctypedef struct UndirectedEdge: + NodeIndexPair nodes + long index + long* directed_edge_indices + long num_directed_edges + double distance + + ctypedef struct DirectedEdge: + NodeIndexPair nodes + long index + const long* image + long undirected_edge_index + double distance + + ctypedef struct LongToDirectedEdgeList: + long key + DirectedEdge** directed_edges_list + int num_directed_edges_in_group + + ctypedef struct ReturnElems2: + long num_nodes + long num_directed_edges + long num_undirected_edges + Node* nodes + UndirectedEdge** undirected_edges_list + DirectedEdge** directed_edges_list + + ReturnElems2* create_graph( + long* center_index, + long n_e, + long* neighbor_index, + long* image, + double* distance, + long num_atoms) + + void free_LongToDirectedEdgeList_in_nodes(Node* nodes, long num_nodes) + + + LongToDirectedEdgeList** get_neighbors(Node* node) + +def make_graph( + const long[::1] center_index, + const long n_e, + const long[::1] neighbor_index, + const long[:, ::1] image, + const double[::1] distance, + const long num_atoms + ): + cdef ReturnElems2* returned + returned = create_graph( ¢er_index[0], n_e, &neighbor_index[0], &image[0][0], &distance[0], num_atoms) + + chg_DirectedEdge = radiusgraph.DirectedEdge + chg_Node = radiusgraph.Node + chg_UndirectedEdge = radiusgraph.UndirectedEdge + + image_np = np.asarray(image) + + cdef LongToDirectedEdgeList** node_neighbors + cdef Node this_node + cdef LongToDirectedEdgeList this_entry + py_nodes = [] + cdef DirectedEdge* this_DE + + # Handling nodes + directed edges + for idx in range(returned[0].num_nodes): + this_node = returned[0].nodes[idx] + this_py_node = chg_Node(index=idx) + + node_neighbors = get_neighbors(&this_node) + + # Iterate through all neighbors and populate our py_node.neighbors dict + for j in range(this_node.num_neighbors): + this_entry = node_neighbors[j][0] + directed_edges = [] + + for k in range(this_entry.num_directed_edges_in_group): + this_DE = this_entry.directed_edges_list[k] + directed_edges.append(this_DE[0].index) + + this_py_node.neighbors[this_entry.key] = directed_edges + + py_nodes.append(this_py_node) + + free(node_neighbors) + + # Handling directed edges + py_directed_edges_list = [] + + for idx in range(returned[0].num_directed_edges): + this_DE = returned[0].directed_edges_list[idx] + py_DE = chg_DirectedEdge(nodes = [this_DE[0].nodes.center, this_DE[0].nodes.neighbor], index=this_DE[0].index, info = {"distance": this_DE[0].distance, "image": image_np[this_DE[0].index], "undirected_edge_index": this_DE[0].undirected_edge_index}) + + py_directed_edges_list.append(py_DE) + + + # Handling undirected edges + py_undirected_edges_list = [] + cdef UndirectedEdge* UDE + + for idx in range(returned[0].num_undirected_edges): + UDE = returned[0].undirected_edges_list[idx] + py_undirected_edge = chg_UndirectedEdge([UDE[0].nodes.center, UDE[0].nodes.neighbor], index = UDE[0].index, info = {"distance": UDE[0].distance, "directed_edge_index": []}) + + for j in range(UDE[0].num_directed_edges): + py_undirected_edge.info["directed_edge_index"].append(UDE[0].directed_edge_indices[j]) + + py_undirected_edges_list.append(py_undirected_edge) + + + # Create Undirected_Edges hashmap + py_undirected_edges = {} + for undirected_edge in py_undirected_edges_list: + this_set = frozenset(undirected_edge.nodes) + if this_set not in py_undirected_edges: + py_undirected_edges[this_set] = [undirected_edge] + else: + py_undirected_edges[this_set].append(undirected_edge) + + # # Update the nodes list to have pointers to DirectedEdges instead of indices + for node_index in range(returned[0].num_nodes): + this_neighbors = py_nodes[node_index].neighbors + for this_neighbor_index in this_neighbors: + replacement = [py_directed_edges_list[edge_index] for edge_index in this_neighbors[this_neighbor_index]] + this_neighbors[this_neighbor_index] = replacement + + + # Free everything unneeded + for idx in range(returned[0].num_directed_edges): + free(returned[0].directed_edges_list[idx]) + + for idx in range(returned[0].num_undirected_edges): + free(returned[0].undirected_edges_list[idx].directed_edge_indices) + free(returned[0].undirected_edges_list[idx]) + + + # Free node LongToDirectedEdgeList + free_LongToDirectedEdgeList_in_nodes(returned[0].nodes, returned[0].num_nodes) + + free(returned[0].directed_edges_list) + free(returned[0].undirected_edges_list) + free(returned[0].nodes) + + free(returned) + + return py_nodes, py_directed_edges_list, py_undirected_edges_list, py_undirected_edges diff --git a/models/matris/matris/graph/fast_converter_libraries/create_graph.c b/models/matris/matris/graph/fast_converter_libraries/create_graph.c new file mode 100644 index 0000000000000000000000000000000000000000..7a6ee0e374e8a122b575a9181f62069d1f37edba --- /dev/null +++ b/models/matris/matris/graph/fast_converter_libraries/create_graph.c @@ -0,0 +1,423 @@ +#include "uthash.h" +#include + +typedef struct _UndirectedEdge UndirectedEdge; +typedef struct _DirectedEdge DirectedEdge; +typedef struct _Node Node; +typedef struct _NodeIndexPair NodeIndexPair; +typedef struct _LongToDirectedEdgeList LongToDirectedEdgeList; +typedef struct _ReturnElems2 ReturnElems2; + +// NOTE: This code was mainly written to replicate the original add_edges method +// in the graph class in chgnet.graph.graph such that anyone familiar with that code should be able to pick up this +// code pretty easily. + +long MEM_ERR = 100; + +typedef struct _Node { + long index; + LongToDirectedEdgeList* neighbors; // Assuming neighbors can only be directed edge. Key is dest_node, value is DirectedEdge struct + long num_neighbors; +} Node; + +typedef struct _NodeIndexPair { + long center; + long neighbor; +} NodeIndexPair; + +typedef struct _UndirectedEdge { + NodeIndexPair nodes; + long index; + long* directed_edge_indices; + long num_directed_edges; + double distance; +} UndirectedEdge; + +typedef struct _DirectedEdge { + NodeIndexPair nodes; + long index; + const long* image; // Only access the first 3, never edit + long undirected_edge_index; + double distance; +} DirectedEdge; + +typedef struct _StructToUndirectedEdgeList { + NodeIndexPair key; + UndirectedEdge** undirected_edges_list; + int num_undirected_edges_in_group; + UT_hash_handle hh; +} StructToUndirectedEdgeList; + +typedef struct _LongToDirectedEdgeList { + long key; + DirectedEdge** directed_edges_list; + int num_directed_edges_in_group; + UT_hash_handle hh; +} LongToDirectedEdgeList; + + +typedef struct _ReturnElems2 { + long num_nodes; + long num_directed_edges; + long num_undirected_edges; + Node* nodes; + UndirectedEdge** undirected_edges_list; + DirectedEdge** directed_edges_list; +} ReturnElems2; + +bool find_in_undirected(NodeIndexPair* tmp, StructToUndirectedEdgeList** undirected_edges, StructToUndirectedEdgeList** found_entry); +void directed_to_undirected(DirectedEdge* directed, UndirectedEdge* undirected, long undirected_index); +void create_new_undirected_edges_entry(StructToUndirectedEdgeList** undirected_edges, NodeIndexPair* tmp, UndirectedEdge* new_undirected_edge); +void append_to_undirected_edges_tmp(UndirectedEdge* undirected, StructToUndirectedEdgeList** undirected_edges, NodeIndexPair* tmp); +void append_to_undirected_edges_list(UndirectedEdge** undirected_edges_list, UndirectedEdge* to_add, long* num_undirected_edges); +void append_to_directed_edges_list(DirectedEdge** directed_edges_list, DirectedEdge* to_add, long* num_directed_edges); +void add_neighbors_to_node(Node* node, long neighbor_index, DirectedEdge* directed_edge); +void print_neighbors(Node* node); +void append_to_directed_edge_indices(UndirectedEdge* undirected_edge, long directed_edge_index); +bool is_reversed_directed_edge(DirectedEdge* directed_edge1, DirectedEdge* directed_edge2); +void free_undirected_edges(StructToUndirectedEdgeList** undirected_edges); +void free_LongToDirectedEdgeList_in_nodes(Node* nodes, long num_nodes); + + +Node* create_nodes(long num_nodes) { + Node* Nodes = (Node*) malloc(sizeof(Node) * num_nodes); + + if (Nodes == NULL) { + return NULL; + } + + for (long i = 0; i < num_nodes; i++) { + Nodes[i].index = i; + Nodes[i].num_neighbors = 0; + + // Initialize the uthash + Nodes[i].neighbors = NULL; + } + + return Nodes; +} + +ReturnElems2* create_graph( + long* center_indices, + long num_edges, + long* neighbor_indices, + long* images, // contiguous memory (row-major) of image elements (total of n_e * 3 integers) + double* distances, + long num_atoms + ) { + // Initialize pertinent data structures --------------------- + Node* nodes = create_nodes(num_atoms); + + DirectedEdge** directed_edges_list = calloc(num_edges, sizeof(DirectedEdge)); + long num_directed_edges = 0; + + // There will never be more undirected edges than directed edges + UndirectedEdge** undirected_edges_list = calloc(num_edges, sizeof(UndirectedEdge)); + long num_undirected_edges = 0; + StructToUndirectedEdgeList* undirected_edges = NULL; + + // Pointer to beginning of list of UndirectedEdges corresponding to tmp of current iteration + StructToUndirectedEdgeList* corr_undirected_edges_item = NULL; + + // Pointer to NodeIndexPair storing tmp + NodeIndexPair* tmp = malloc(sizeof(NodeIndexPair)); + + // Flag for whether or not the value was found + bool found = false; + + // Flag used to show if we've already processed the current undirected edge + bool processed_edge = false; + + // Pointer used to store the previously added directed edge between two nodes + DirectedEdge* added_DE; + DirectedEdge* this_directed_edge; + + // Add all edges to graph information + for (long i = 0; i < num_edges; i++) { + // Haven't processed the edge yet + processed_edge = false; + // Create the current directed edge ------------------- + this_directed_edge = calloc(1, sizeof(DirectedEdge)); + this_directed_edge->nodes.center = center_indices[i]; + this_directed_edge->nodes.neighbor = neighbor_indices[i]; + this_directed_edge->distance = distances[i]; + this_directed_edge->index = num_directed_edges; + this_directed_edge->image = images + (3 * i); + + // Load tmp + memset(tmp, 0, sizeof(NodeIndexPair)); + tmp->center = center_indices[i]; + tmp->neighbor = neighbor_indices[i]; + + // See if tmp is in undirected + corr_undirected_edges_item = NULL; + found = find_in_undirected(tmp, &undirected_edges, &corr_undirected_edges_item); + + if (!found) { + // Never seen this edge combination before + this_directed_edge->undirected_edge_index = num_undirected_edges; + + // Create new undirected edge + UndirectedEdge* this_undirected_edge = malloc(sizeof(UndirectedEdge)); + + directed_to_undirected(this_directed_edge, this_undirected_edge, num_undirected_edges); + + // Add this new edge information to various data structures + create_new_undirected_edges_entry(&undirected_edges, tmp, this_undirected_edge); + append_to_undirected_edges_list(undirected_edges_list, this_undirected_edge, &num_undirected_edges); + add_neighbors_to_node(&nodes[center_indices[i]], neighbor_indices[i], this_directed_edge); + append_to_directed_edges_list(directed_edges_list, this_directed_edge, &num_directed_edges); + } else { + // This pair of nodes has been added before. We have to check if it's the other directed edge (but pointed in + // the different direction) OR it's another totally different undirected edge that has different image and distance + + // if found is true, then corr_undirected_edges_item points to self.undirected_edges[tmp] + // iterate through all previously scanned undirected edges that have the same endpoints as this edge + // if there exists an undirected edge with the same inverted image as this_undirected_edge, then add this new directed edge + // and associate it with this undirected edge + for (int j = 0; j < corr_undirected_edges_item->num_undirected_edges_in_group; j++) { + // Grab the 0th directed edge associated with this undirected edge + added_DE = directed_edges_list[((corr_undirected_edges_item->undirected_edges_list)[j]->directed_edge_indices)[0]]; + + if (is_reversed_directed_edge(added_DE, this_directed_edge)) { + this_directed_edge->undirected_edge_index = added_DE->undirected_edge_index; + add_neighbors_to_node(&nodes[center_indices[i]], neighbor_indices[i], this_directed_edge); + append_to_directed_edges_list(directed_edges_list, this_directed_edge, &num_directed_edges); + append_to_directed_edge_indices((corr_undirected_edges_item->undirected_edges_list)[j], this_directed_edge->index); + processed_edge = true; + break; + } + } + // There wasn't a pre-existing undirected edge that corresponds to this directed edge + // Create a new undirected edge and process + if (!processed_edge) { + this_directed_edge->undirected_edge_index = num_undirected_edges; + // Create a new undirected edge + UndirectedEdge* this_undirected_edge = malloc(sizeof(UndirectedEdge)); + directed_to_undirected(this_directed_edge, this_undirected_edge, num_undirected_edges); + append_to_undirected_edges_tmp(this_undirected_edge, &undirected_edges, tmp); + append_to_undirected_edges_list(undirected_edges_list, this_undirected_edge, &num_undirected_edges); + add_neighbors_to_node(&nodes[center_indices[i]], neighbor_indices[i], this_directed_edge); + append_to_directed_edges_list(directed_edges_list, this_directed_edge, &num_directed_edges); + } + } + } + + ReturnElems2* returned2 = malloc(sizeof(ReturnElems2)); + returned2->num_nodes = num_atoms; + returned2->num_undirected_edges = num_undirected_edges; + returned2->num_directed_edges = num_directed_edges; + + returned2->nodes = nodes; + returned2->directed_edges_list = directed_edges_list; + returned2->undirected_edges_list = undirected_edges_list; + + free(tmp); + free_undirected_edges(&undirected_edges); + + return returned2; +} + +void print_neighbors(Node* node) { + LongToDirectedEdgeList *tmp, *neighbor; + HASH_ITER(hh, node->neighbors, neighbor, tmp) { + printf("C:neighboring atom: %lu\n", neighbor->key); + } +} + +void free_undirected_edges(StructToUndirectedEdgeList** undirected_edges) { + StructToUndirectedEdgeList* current; + StructToUndirectedEdgeList* tmp; + + HASH_ITER(hh, *undirected_edges, current, tmp) { + HASH_DEL(*undirected_edges, current); + free(current->undirected_edges_list); + free(current); + } +} + +void free_LongToDirectedEdgeList_in_nodes(Node* nodes, long num_nodes) { + LongToDirectedEdgeList* current; + LongToDirectedEdgeList* tmp; + + for (long node_i = 0; node_i < num_nodes; node_i++) { + HASH_ITER(hh, nodes[node_i].neighbors, current, tmp) { + HASH_DEL(nodes[node_i].neighbors, current); + free(current->directed_edges_list); + free(current); + } + } +} + +// Returns true if the two directed edges have images that are inverted +// NOTE: assumes that directed_edge1->center = directed_edge2->neighbor and directed_edge1->neighbor = directed_edge2->center +bool is_reversed_directed_edge(DirectedEdge* directed_edge1, DirectedEdge* directed_edge2) { + for (int i = 0; i < 3; i++) { + if (directed_edge1->image[i] != -1 * directed_edge2->image[i]) { + return false; + } + } + + // The two directed edges should have opposing center/neighbor nodes (i.e. center-neighbor for DE1 is [0, 1] and for DE2 is [1, 0]) + // We check for that condition here + if (directed_edge1->nodes.center != directed_edge2->nodes.neighbor) { + return false; + } + if (directed_edge1->nodes.neighbor != directed_edge2->nodes.center) { + return false; + } + return true; +} + +// If tmp or the reverse of tmp is found in undirected_edges, True is returned and the corresponding StructToUndirectedEdgeList pointer is placed +// into found_entry. Otherwise, False is returned. +// NOTE: does not edit the *tmp +// Assumes *tmp bits have already been 0'd at padding within a struct +bool find_in_undirected(NodeIndexPair* tmp, StructToUndirectedEdgeList** undirected_edges, StructToUndirectedEdgeList** found_entry) { + StructToUndirectedEdgeList* out_list; + // Check tmp + HASH_FIND(hh, *undirected_edges, tmp, sizeof(NodeIndexPair), out_list); + + if (out_list) { + *found_entry = out_list; + return true; + } + + // Check tmp_rev + NodeIndexPair tmp_rev; + tmp_rev.center = tmp->neighbor; + tmp_rev.neighbor = tmp->center; + + HASH_FIND(hh, *undirected_edges, &tmp_rev, sizeof(NodeIndexPair), out_list); + + if (out_list) { + *found_entry = out_list; + return true; + } + + return false; +} + + +// Creates new entry in undirected_edges and initializes necessary arrays +void create_new_undirected_edges_entry(StructToUndirectedEdgeList** undirected_edges, NodeIndexPair* tmp, UndirectedEdge* new_undirected_edge) { + StructToUndirectedEdgeList* new_entry = malloc(sizeof(StructToUndirectedEdgeList)); + memset(new_entry, 0, sizeof(StructToUndirectedEdgeList)); + + // Set up fields within the new entry in the hashmap + new_entry->key.center = tmp->center; + new_entry->key.neighbor = tmp->neighbor; + + new_entry->num_undirected_edges_in_group = 1; + new_entry->undirected_edges_list = malloc(sizeof(UndirectedEdge*)); + new_entry->undirected_edges_list[0] = new_undirected_edge; + + HASH_ADD(hh, *undirected_edges, key, sizeof(NodeIndexPair), new_entry); + +} + +// Appends undirected into the StructToUndirectedEdgeList entry that corresponds to tmp +// This function will first look up tmp +void append_to_undirected_edges_tmp(UndirectedEdge* undirected, StructToUndirectedEdgeList** undirected_edges, NodeIndexPair* tmp) { + + StructToUndirectedEdgeList* this_undirected_edges_item; + find_in_undirected(tmp, undirected_edges, &this_undirected_edges_item); + + long num_undirected_edges = this_undirected_edges_item->num_undirected_edges_in_group; + + // No need to worry about originally malloc'ing memory for this_undirected_edges_item->undirected_edges_list + // this is because, we first call create_new_undirected_edges_entry for all entries. This function already mallocs for us. + + // Realloc the space to fit a new pointer to an undirected edge + UndirectedEdge** new_list = realloc(this_undirected_edges_item->undirected_edges_list, sizeof(UndirectedEdge*) * (num_undirected_edges + 1)); + this_undirected_edges_item->undirected_edges_list = new_list; + + // Insert the undirected pointer into the newly allocated slot + this_undirected_edges_item->undirected_edges_list[num_undirected_edges] = undirected; + + // Increase the counter for # of undirected edges + this_undirected_edges_item->num_undirected_edges_in_group = num_undirected_edges + 1; +} + + +void directed_to_undirected(DirectedEdge* directed, UndirectedEdge* undirected, long undirected_index) { + // Copy over image and distance + undirected->distance = directed->distance; + undirected->nodes = directed->nodes; + undirected->index = undirected_index; + + // Add a new directed_edge_index to the directed_edge_indices pointer. This should be the first + undirected->num_directed_edges = 1; + undirected->directed_edge_indices = malloc(sizeof(long)); + undirected->directed_edge_indices[0] = directed->index; +} + + +void append_to_undirected_edges_list(UndirectedEdge** undirected_edges_list, UndirectedEdge* to_add, long* num_undirected_edges) { + // No need to realloc for space since our original alloc should cover everything + + // Assign value to next available position + undirected_edges_list[*num_undirected_edges] = to_add; + *num_undirected_edges += 1; +} + +void append_to_directed_edges_list(DirectedEdge** directed_edges_list, DirectedEdge* to_add, long* num_directed_edges) { + // No need to realloc for space since our original alloc should cover everything + + // Assign value to next available position + directed_edges_list[*num_directed_edges] = to_add; + *num_directed_edges += 1; +} + +void append_to_directed_edge_indices(UndirectedEdge* undirected_edge, long directed_edge_index) { + // TODO: don't need to realloc if we always know that there will be 2 directed edges per undirected edge. Update this later for performance boosts. + // TODO: other random performance boost: don't pass longs into function parameters, pass long* instead + undirected_edge->directed_edge_indices = realloc(undirected_edge->directed_edge_indices, sizeof(long) * (undirected_edge->num_directed_edges + 1)); + undirected_edge->directed_edge_indices[undirected_edge->num_directed_edges] = directed_edge_index; + undirected_edge->num_directed_edges += 1; +} + +// If there already exists neighbor_index within the Node node, then adds directed_edge to the list of directed edges. +// If there doesn't already exist neighbor_index within the Node node, then create a new entry into the node's neighbors hashmap and add the entry +void add_neighbors_to_node(Node* node, long neighbor_index, DirectedEdge* directed_edge) { + LongToDirectedEdgeList* entry = NULL; + + // Search for the neighbor_index in our hashmap + HASH_FIND(hh, node->neighbors, &neighbor_index, sizeof(long), entry); + + if (entry) { + // We found something, update the list within this pointer + entry->directed_edges_list = realloc(entry->directed_edges_list, sizeof(DirectedEdge*) * (entry->num_directed_edges_in_group + 1)); + entry->directed_edges_list[entry->num_directed_edges_in_group] = directed_edge; + + entry->num_directed_edges_in_group += 1; + } else { + // allocate memory for entry + entry = malloc(sizeof(LongToDirectedEdgeList)); + + // The entry doesn't exist, initialize the entry and enter it into our hashmap + entry->directed_edges_list = malloc(sizeof(DirectedEdge*)); + entry->directed_edges_list[0] = directed_edge; + entry->key = neighbor_index; + + entry->num_directed_edges_in_group = 1; + HASH_ADD(hh, node->neighbors, key, sizeof(long), entry); + + node->num_neighbors += 1; + } +} + +// Returns a list of LongToDirectedEdgeList pointers which are entries for the neighbors of the inputted node +LongToDirectedEdgeList** get_neighbors(Node* node) { + long num_neighbors = HASH_COUNT(node->neighbors); + LongToDirectedEdgeList** entries = malloc(sizeof(LongToDirectedEdgeList*) * num_neighbors); + + LongToDirectedEdgeList* entry; + long counter = 0; + for (entry = node->neighbors; entry != NULL; entry = entry->hh.next) { + entries[counter] = entry; + counter += 1; + } + + return entries; +} diff --git a/models/matris/matris/graph/fast_converter_libraries/uthash.h b/models/matris/matris/graph/fast_converter_libraries/uthash.h new file mode 100644 index 0000000000000000000000000000000000000000..68693bf39603d91a529ca8f8c46ad720aa4f094a --- /dev/null +++ b/models/matris/matris/graph/fast_converter_libraries/uthash.h @@ -0,0 +1,1140 @@ +/* +Copyright (c) 2003-2022, Troy D. Hanson https://troydhanson.github.io/uthash/ +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS +IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED +TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A +PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER +OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef UTHASH_H +#define UTHASH_H + +#define UTHASH_VERSION 2.3.0 + +#include /* memcmp, memset, strlen */ +#include /* ptrdiff_t */ +#include /* exit */ + +#if defined(HASH_DEFINE_OWN_STDINT) && HASH_DEFINE_OWN_STDINT +/* This codepath is provided for backward compatibility, but I plan to remove it. */ +#warning "HASH_DEFINE_OWN_STDINT is deprecated; please use HASH_NO_STDINT instead" +typedef unsigned int uint32_t; +typedef unsigned char uint8_t; +#elif defined(HASH_NO_STDINT) && HASH_NO_STDINT +#else +#include /* uint8_t, uint32_t */ +#endif + +/* These macros use decltype or the earlier __typeof GNU extension. + As decltype is only available in newer compilers (VS2010 or gcc 4.3+ + when compiling c++ source) this code uses whatever method is needed + or, for VS2008 where neither is available, uses casting workarounds. */ +#if !defined(DECLTYPE) && !defined(NO_DECLTYPE) +#if defined(_MSC_VER) /* MS compiler */ +#if _MSC_VER >= 1600 && defined(__cplusplus) /* VS2010 or newer in C++ mode */ +#define DECLTYPE(x) (decltype(x)) +#else /* VS2008 or older (or VS2010 in C mode) */ +#define NO_DECLTYPE +#endif +#elif defined(__MCST__) /* Elbrus C Compiler */ +#define DECLTYPE(x) (__typeof(x)) +#elif defined(__BORLANDC__) || defined(__ICCARM__) || defined(__LCC__) || defined(__WATCOMC__) +#define NO_DECLTYPE +#else /* GNU, Sun and other compilers */ +#define DECLTYPE(x) (__typeof(x)) +#endif +#endif + +#ifdef NO_DECLTYPE +#define DECLTYPE(x) +#define DECLTYPE_ASSIGN(dst,src) \ +do { \ + char **_da_dst = (char**)(&(dst)); \ + *_da_dst = (char*)(src); \ +} while (0) +#else +#define DECLTYPE_ASSIGN(dst,src) \ +do { \ + (dst) = DECLTYPE(dst)(src); \ +} while (0) +#endif + +#ifndef uthash_malloc +#define uthash_malloc(sz) malloc(sz) /* malloc fcn */ +#endif +#ifndef uthash_free +#define uthash_free(ptr,sz) free(ptr) /* free fcn */ +#endif +#ifndef uthash_bzero +#define uthash_bzero(a,n) memset(a,'\0',n) +#endif +#ifndef uthash_strlen +#define uthash_strlen(s) strlen(s) +#endif + +#ifndef HASH_FUNCTION +#define HASH_FUNCTION(keyptr,keylen,hashv) HASH_JEN(keyptr, keylen, hashv) +#endif + +#ifndef HASH_KEYCMP +#define HASH_KEYCMP(a,b,n) memcmp(a,b,n) +#endif + +#ifndef uthash_noexpand_fyi +#define uthash_noexpand_fyi(tbl) /* can be defined to log noexpand */ +#endif +#ifndef uthash_expand_fyi +#define uthash_expand_fyi(tbl) /* can be defined to log expands */ +#endif + +#ifndef HASH_NONFATAL_OOM +#define HASH_NONFATAL_OOM 0 +#endif + +#if HASH_NONFATAL_OOM +/* malloc failures can be recovered from */ + +#ifndef uthash_nonfatal_oom +#define uthash_nonfatal_oom(obj) do {} while (0) /* non-fatal OOM error */ +#endif + +#define HASH_RECORD_OOM(oomed) do { (oomed) = 1; } while (0) +#define IF_HASH_NONFATAL_OOM(x) x + +#else +/* malloc failures result in lost memory, hash tables are unusable */ + +#ifndef uthash_fatal +#define uthash_fatal(msg) exit(-1) /* fatal OOM error */ +#endif + +#define HASH_RECORD_OOM(oomed) uthash_fatal("out of memory") +#define IF_HASH_NONFATAL_OOM(x) + +#endif + +/* initial number of buckets */ +#define HASH_INITIAL_NUM_BUCKETS 32U /* initial number of buckets */ +#define HASH_INITIAL_NUM_BUCKETS_LOG2 5U /* lg2 of initial number of buckets */ +#define HASH_BKT_CAPACITY_THRESH 10U /* expand when bucket count reaches */ + +/* calculate the element whose hash handle address is hhp */ +#define ELMT_FROM_HH(tbl,hhp) ((void*)(((char*)(hhp)) - ((tbl)->hho))) +/* calculate the hash handle from element address elp */ +#define HH_FROM_ELMT(tbl,elp) ((UT_hash_handle*)(void*)(((char*)(elp)) + ((tbl)->hho))) + +#define HASH_ROLLBACK_BKT(hh, head, itemptrhh) \ +do { \ + struct UT_hash_handle *_hd_hh_item = (itemptrhh); \ + unsigned _hd_bkt; \ + HASH_TO_BKT(_hd_hh_item->hashv, (head)->hh.tbl->num_buckets, _hd_bkt); \ + (head)->hh.tbl->buckets[_hd_bkt].count++; \ + _hd_hh_item->hh_next = NULL; \ + _hd_hh_item->hh_prev = NULL; \ +} while (0) + +#define HASH_VALUE(keyptr,keylen,hashv) \ +do { \ + HASH_FUNCTION(keyptr, keylen, hashv); \ +} while (0) + +#define HASH_FIND_BYHASHVALUE(hh,head,keyptr,keylen,hashval,out) \ +do { \ + (out) = NULL; \ + if (head) { \ + unsigned _hf_bkt; \ + HASH_TO_BKT(hashval, (head)->hh.tbl->num_buckets, _hf_bkt); \ + if (HASH_BLOOM_TEST((head)->hh.tbl, hashval) != 0) { \ + HASH_FIND_IN_BKT((head)->hh.tbl, hh, (head)->hh.tbl->buckets[ _hf_bkt ], keyptr, keylen, hashval, out); \ + } \ + } \ +} while (0) + +#define HASH_FIND(hh,head,keyptr,keylen,out) \ +do { \ + (out) = NULL; \ + if (head) { \ + unsigned _hf_hashv; \ + HASH_VALUE(keyptr, keylen, _hf_hashv); \ + HASH_FIND_BYHASHVALUE(hh, head, keyptr, keylen, _hf_hashv, out); \ + } \ +} while (0) + +#ifdef HASH_BLOOM +#define HASH_BLOOM_BITLEN (1UL << HASH_BLOOM) +#define HASH_BLOOM_BYTELEN (HASH_BLOOM_BITLEN/8UL) + (((HASH_BLOOM_BITLEN%8UL)!=0UL) ? 1UL : 0UL) +#define HASH_BLOOM_MAKE(tbl,oomed) \ +do { \ + (tbl)->bloom_nbits = HASH_BLOOM; \ + (tbl)->bloom_bv = (uint8_t*)uthash_malloc(HASH_BLOOM_BYTELEN); \ + if (!(tbl)->bloom_bv) { \ + HASH_RECORD_OOM(oomed); \ + } else { \ + uthash_bzero((tbl)->bloom_bv, HASH_BLOOM_BYTELEN); \ + (tbl)->bloom_sig = HASH_BLOOM_SIGNATURE; \ + } \ +} while (0) + +#define HASH_BLOOM_FREE(tbl) \ +do { \ + uthash_free((tbl)->bloom_bv, HASH_BLOOM_BYTELEN); \ +} while (0) + +#define HASH_BLOOM_BITSET(bv,idx) (bv[(idx)/8U] |= (1U << ((idx)%8U))) +#define HASH_BLOOM_BITTEST(bv,idx) (bv[(idx)/8U] & (1U << ((idx)%8U))) + +#define HASH_BLOOM_ADD(tbl,hashv) \ + HASH_BLOOM_BITSET((tbl)->bloom_bv, ((hashv) & (uint32_t)((1UL << (tbl)->bloom_nbits) - 1U))) + +#define HASH_BLOOM_TEST(tbl,hashv) \ + HASH_BLOOM_BITTEST((tbl)->bloom_bv, ((hashv) & (uint32_t)((1UL << (tbl)->bloom_nbits) - 1U))) + +#else +#define HASH_BLOOM_MAKE(tbl,oomed) +#define HASH_BLOOM_FREE(tbl) +#define HASH_BLOOM_ADD(tbl,hashv) +#define HASH_BLOOM_TEST(tbl,hashv) (1) +#define HASH_BLOOM_BYTELEN 0U +#endif + +#define HASH_MAKE_TABLE(hh,head,oomed) \ +do { \ + (head)->hh.tbl = (UT_hash_table*)uthash_malloc(sizeof(UT_hash_table)); \ + if (!(head)->hh.tbl) { \ + HASH_RECORD_OOM(oomed); \ + } else { \ + uthash_bzero((head)->hh.tbl, sizeof(UT_hash_table)); \ + (head)->hh.tbl->tail = &((head)->hh); \ + (head)->hh.tbl->num_buckets = HASH_INITIAL_NUM_BUCKETS; \ + (head)->hh.tbl->log2_num_buckets = HASH_INITIAL_NUM_BUCKETS_LOG2; \ + (head)->hh.tbl->hho = (char*)(&(head)->hh) - (char*)(head); \ + (head)->hh.tbl->buckets = (UT_hash_bucket*)uthash_malloc( \ + HASH_INITIAL_NUM_BUCKETS * sizeof(struct UT_hash_bucket)); \ + (head)->hh.tbl->signature = HASH_SIGNATURE; \ + if (!(head)->hh.tbl->buckets) { \ + HASH_RECORD_OOM(oomed); \ + uthash_free((head)->hh.tbl, sizeof(UT_hash_table)); \ + } else { \ + uthash_bzero((head)->hh.tbl->buckets, \ + HASH_INITIAL_NUM_BUCKETS * sizeof(struct UT_hash_bucket)); \ + HASH_BLOOM_MAKE((head)->hh.tbl, oomed); \ + IF_HASH_NONFATAL_OOM( \ + if (oomed) { \ + uthash_free((head)->hh.tbl->buckets, \ + HASH_INITIAL_NUM_BUCKETS*sizeof(struct UT_hash_bucket)); \ + uthash_free((head)->hh.tbl, sizeof(UT_hash_table)); \ + } \ + ) \ + } \ + } \ +} while (0) + +#define HASH_REPLACE_BYHASHVALUE_INORDER(hh,head,fieldname,keylen_in,hashval,add,replaced,cmpfcn) \ +do { \ + (replaced) = NULL; \ + HASH_FIND_BYHASHVALUE(hh, head, &((add)->fieldname), keylen_in, hashval, replaced); \ + if (replaced) { \ + HASH_DELETE(hh, head, replaced); \ + } \ + HASH_ADD_KEYPTR_BYHASHVALUE_INORDER(hh, head, &((add)->fieldname), keylen_in, hashval, add, cmpfcn); \ +} while (0) + +#define HASH_REPLACE_BYHASHVALUE(hh,head,fieldname,keylen_in,hashval,add,replaced) \ +do { \ + (replaced) = NULL; \ + HASH_FIND_BYHASHVALUE(hh, head, &((add)->fieldname), keylen_in, hashval, replaced); \ + if (replaced) { \ + HASH_DELETE(hh, head, replaced); \ + } \ + HASH_ADD_KEYPTR_BYHASHVALUE(hh, head, &((add)->fieldname), keylen_in, hashval, add); \ +} while (0) + +#define HASH_REPLACE(hh,head,fieldname,keylen_in,add,replaced) \ +do { \ + unsigned _hr_hashv; \ + HASH_VALUE(&((add)->fieldname), keylen_in, _hr_hashv); \ + HASH_REPLACE_BYHASHVALUE(hh, head, fieldname, keylen_in, _hr_hashv, add, replaced); \ +} while (0) + +#define HASH_REPLACE_INORDER(hh,head,fieldname,keylen_in,add,replaced,cmpfcn) \ +do { \ + unsigned _hr_hashv; \ + HASH_VALUE(&((add)->fieldname), keylen_in, _hr_hashv); \ + HASH_REPLACE_BYHASHVALUE_INORDER(hh, head, fieldname, keylen_in, _hr_hashv, add, replaced, cmpfcn); \ +} while (0) + +#define HASH_APPEND_LIST(hh, head, add) \ +do { \ + (add)->hh.next = NULL; \ + (add)->hh.prev = ELMT_FROM_HH((head)->hh.tbl, (head)->hh.tbl->tail); \ + (head)->hh.tbl->tail->next = (add); \ + (head)->hh.tbl->tail = &((add)->hh); \ +} while (0) + +#define HASH_AKBI_INNER_LOOP(hh,head,add,cmpfcn) \ +do { \ + do { \ + if (cmpfcn(DECLTYPE(head)(_hs_iter), add) > 0) { \ + break; \ + } \ + } while ((_hs_iter = HH_FROM_ELMT((head)->hh.tbl, _hs_iter)->next)); \ +} while (0) + +#ifdef NO_DECLTYPE +#undef HASH_AKBI_INNER_LOOP +#define HASH_AKBI_INNER_LOOP(hh,head,add,cmpfcn) \ +do { \ + char *_hs_saved_head = (char*)(head); \ + do { \ + DECLTYPE_ASSIGN(head, _hs_iter); \ + if (cmpfcn(head, add) > 0) { \ + DECLTYPE_ASSIGN(head, _hs_saved_head); \ + break; \ + } \ + DECLTYPE_ASSIGN(head, _hs_saved_head); \ + } while ((_hs_iter = HH_FROM_ELMT((head)->hh.tbl, _hs_iter)->next)); \ +} while (0) +#endif + +#if HASH_NONFATAL_OOM + +#define HASH_ADD_TO_TABLE(hh,head,keyptr,keylen_in,hashval,add,oomed) \ +do { \ + if (!(oomed)) { \ + unsigned _ha_bkt; \ + (head)->hh.tbl->num_items++; \ + HASH_TO_BKT(hashval, (head)->hh.tbl->num_buckets, _ha_bkt); \ + HASH_ADD_TO_BKT((head)->hh.tbl->buckets[_ha_bkt], hh, &(add)->hh, oomed); \ + if (oomed) { \ + HASH_ROLLBACK_BKT(hh, head, &(add)->hh); \ + HASH_DELETE_HH(hh, head, &(add)->hh); \ + (add)->hh.tbl = NULL; \ + uthash_nonfatal_oom(add); \ + } else { \ + HASH_BLOOM_ADD((head)->hh.tbl, hashval); \ + HASH_EMIT_KEY(hh, head, keyptr, keylen_in); \ + } \ + } else { \ + (add)->hh.tbl = NULL; \ + uthash_nonfatal_oom(add); \ + } \ +} while (0) + +#else + +#define HASH_ADD_TO_TABLE(hh,head,keyptr,keylen_in,hashval,add,oomed) \ +do { \ + unsigned _ha_bkt; \ + (head)->hh.tbl->num_items++; \ + HASH_TO_BKT(hashval, (head)->hh.tbl->num_buckets, _ha_bkt); \ + HASH_ADD_TO_BKT((head)->hh.tbl->buckets[_ha_bkt], hh, &(add)->hh, oomed); \ + HASH_BLOOM_ADD((head)->hh.tbl, hashval); \ + HASH_EMIT_KEY(hh, head, keyptr, keylen_in); \ +} while (0) + +#endif + + +#define HASH_ADD_KEYPTR_BYHASHVALUE_INORDER(hh,head,keyptr,keylen_in,hashval,add,cmpfcn) \ +do { \ + IF_HASH_NONFATAL_OOM( int _ha_oomed = 0; ) \ + (add)->hh.hashv = (hashval); \ + (add)->hh.key = (char*) (keyptr); \ + (add)->hh.keylen = (unsigned) (keylen_in); \ + if (!(head)) { \ + (add)->hh.next = NULL; \ + (add)->hh.prev = NULL; \ + HASH_MAKE_TABLE(hh, add, _ha_oomed); \ + IF_HASH_NONFATAL_OOM( if (!_ha_oomed) { ) \ + (head) = (add); \ + IF_HASH_NONFATAL_OOM( } ) \ + } else { \ + void *_hs_iter = (head); \ + (add)->hh.tbl = (head)->hh.tbl; \ + HASH_AKBI_INNER_LOOP(hh, head, add, cmpfcn); \ + if (_hs_iter) { \ + (add)->hh.next = _hs_iter; \ + if (((add)->hh.prev = HH_FROM_ELMT((head)->hh.tbl, _hs_iter)->prev)) { \ + HH_FROM_ELMT((head)->hh.tbl, (add)->hh.prev)->next = (add); \ + } else { \ + (head) = (add); \ + } \ + HH_FROM_ELMT((head)->hh.tbl, _hs_iter)->prev = (add); \ + } else { \ + HASH_APPEND_LIST(hh, head, add); \ + } \ + } \ + HASH_ADD_TO_TABLE(hh, head, keyptr, keylen_in, hashval, add, _ha_oomed); \ + HASH_FSCK(hh, head, "HASH_ADD_KEYPTR_BYHASHVALUE_INORDER"); \ +} while (0) + +#define HASH_ADD_KEYPTR_INORDER(hh,head,keyptr,keylen_in,add,cmpfcn) \ +do { \ + unsigned _hs_hashv; \ + HASH_VALUE(keyptr, keylen_in, _hs_hashv); \ + HASH_ADD_KEYPTR_BYHASHVALUE_INORDER(hh, head, keyptr, keylen_in, _hs_hashv, add, cmpfcn); \ +} while (0) + +#define HASH_ADD_BYHASHVALUE_INORDER(hh,head,fieldname,keylen_in,hashval,add,cmpfcn) \ + HASH_ADD_KEYPTR_BYHASHVALUE_INORDER(hh, head, &((add)->fieldname), keylen_in, hashval, add, cmpfcn) + +#define HASH_ADD_INORDER(hh,head,fieldname,keylen_in,add,cmpfcn) \ + HASH_ADD_KEYPTR_INORDER(hh, head, &((add)->fieldname), keylen_in, add, cmpfcn) + +#define HASH_ADD_KEYPTR_BYHASHVALUE(hh,head,keyptr,keylen_in,hashval,add) \ +do { \ + IF_HASH_NONFATAL_OOM( int _ha_oomed = 0; ) \ + (add)->hh.hashv = (hashval); \ + (add)->hh.key = (const void*) (keyptr); \ + (add)->hh.keylen = (unsigned) (keylen_in); \ + if (!(head)) { \ + (add)->hh.next = NULL; \ + (add)->hh.prev = NULL; \ + HASH_MAKE_TABLE(hh, add, _ha_oomed); \ + IF_HASH_NONFATAL_OOM( if (!_ha_oomed) { ) \ + (head) = (add); \ + IF_HASH_NONFATAL_OOM( } ) \ + } else { \ + (add)->hh.tbl = (head)->hh.tbl; \ + HASH_APPEND_LIST(hh, head, add); \ + } \ + HASH_ADD_TO_TABLE(hh, head, keyptr, keylen_in, hashval, add, _ha_oomed); \ + HASH_FSCK(hh, head, "HASH_ADD_KEYPTR_BYHASHVALUE"); \ +} while (0) + +#define HASH_ADD_KEYPTR(hh,head,keyptr,keylen_in,add) \ +do { \ + unsigned _ha_hashv; \ + HASH_VALUE(keyptr, keylen_in, _ha_hashv); \ + HASH_ADD_KEYPTR_BYHASHVALUE(hh, head, keyptr, keylen_in, _ha_hashv, add); \ +} while (0) + +#define HASH_ADD_BYHASHVALUE(hh,head,fieldname,keylen_in,hashval,add) \ + HASH_ADD_KEYPTR_BYHASHVALUE(hh, head, &((add)->fieldname), keylen_in, hashval, add) + +#define HASH_ADD(hh,head,fieldname,keylen_in,add) \ + HASH_ADD_KEYPTR(hh, head, &((add)->fieldname), keylen_in, add) + +#define HASH_TO_BKT(hashv,num_bkts,bkt) \ +do { \ + bkt = ((hashv) & ((num_bkts) - 1U)); \ +} while (0) + +/* delete "delptr" from the hash table. + * "the usual" patch-up process for the app-order doubly-linked-list. + * The use of _hd_hh_del below deserves special explanation. + * These used to be expressed using (delptr) but that led to a bug + * if someone used the same symbol for the head and deletee, like + * HASH_DELETE(hh,users,users); + * We want that to work, but by changing the head (users) below + * we were forfeiting our ability to further refer to the deletee (users) + * in the patch-up process. Solution: use scratch space to + * copy the deletee pointer, then the latter references are via that + * scratch pointer rather than through the repointed (users) symbol. + */ +#define HASH_DELETE(hh,head,delptr) \ + HASH_DELETE_HH(hh, head, &(delptr)->hh) + +#define HASH_DELETE_HH(hh,head,delptrhh) \ +do { \ + const struct UT_hash_handle *_hd_hh_del = (delptrhh); \ + if ((_hd_hh_del->prev == NULL) && (_hd_hh_del->next == NULL)) { \ + HASH_BLOOM_FREE((head)->hh.tbl); \ + uthash_free((head)->hh.tbl->buckets, \ + (head)->hh.tbl->num_buckets * sizeof(struct UT_hash_bucket)); \ + uthash_free((head)->hh.tbl, sizeof(UT_hash_table)); \ + (head) = NULL; \ + } else { \ + unsigned _hd_bkt; \ + if (_hd_hh_del == (head)->hh.tbl->tail) { \ + (head)->hh.tbl->tail = HH_FROM_ELMT((head)->hh.tbl, _hd_hh_del->prev); \ + } \ + if (_hd_hh_del->prev != NULL) { \ + HH_FROM_ELMT((head)->hh.tbl, _hd_hh_del->prev)->next = _hd_hh_del->next; \ + } else { \ + DECLTYPE_ASSIGN(head, _hd_hh_del->next); \ + } \ + if (_hd_hh_del->next != NULL) { \ + HH_FROM_ELMT((head)->hh.tbl, _hd_hh_del->next)->prev = _hd_hh_del->prev; \ + } \ + HASH_TO_BKT(_hd_hh_del->hashv, (head)->hh.tbl->num_buckets, _hd_bkt); \ + HASH_DEL_IN_BKT((head)->hh.tbl->buckets[_hd_bkt], _hd_hh_del); \ + (head)->hh.tbl->num_items--; \ + } \ + HASH_FSCK(hh, head, "HASH_DELETE_HH"); \ +} while (0) + +/* convenience forms of HASH_FIND/HASH_ADD/HASH_DEL */ +#define HASH_FIND_STR(head,findstr,out) \ +do { \ + unsigned _uthash_hfstr_keylen = (unsigned)uthash_strlen(findstr); \ + HASH_FIND(hh, head, findstr, _uthash_hfstr_keylen, out); \ +} while (0) +#define HASH_ADD_STR(head,strfield,add) \ +do { \ + unsigned _uthash_hastr_keylen = (unsigned)uthash_strlen((add)->strfield); \ + HASH_ADD(hh, head, strfield[0], _uthash_hastr_keylen, add); \ +} while (0) +#define HASH_REPLACE_STR(head,strfield,add,replaced) \ +do { \ + unsigned _uthash_hrstr_keylen = (unsigned)uthash_strlen((add)->strfield); \ + HASH_REPLACE(hh, head, strfield[0], _uthash_hrstr_keylen, add, replaced); \ +} while (0) +#define HASH_FIND_INT(head,findint,out) \ + HASH_FIND(hh,head,findint,sizeof(int),out) +#define HASH_ADD_INT(head,intfield,add) \ + HASH_ADD(hh,head,intfield,sizeof(int),add) +#define HASH_REPLACE_INT(head,intfield,add,replaced) \ + HASH_REPLACE(hh,head,intfield,sizeof(int),add,replaced) +#define HASH_FIND_PTR(head,findptr,out) \ + HASH_FIND(hh,head,findptr,sizeof(void *),out) +#define HASH_ADD_PTR(head,ptrfield,add) \ + HASH_ADD(hh,head,ptrfield,sizeof(void *),add) +#define HASH_REPLACE_PTR(head,ptrfield,add,replaced) \ + HASH_REPLACE(hh,head,ptrfield,sizeof(void *),add,replaced) +#define HASH_DEL(head,delptr) \ + HASH_DELETE(hh,head,delptr) + +/* HASH_FSCK checks hash integrity on every add/delete when HASH_DEBUG is defined. + * This is for uthash developer only; it compiles away if HASH_DEBUG isn't defined. + */ +#ifdef HASH_DEBUG +#include /* fprintf, stderr */ +#define HASH_OOPS(...) do { fprintf(stderr, __VA_ARGS__); exit(-1); } while (0) +#define HASH_FSCK(hh,head,where) \ +do { \ + struct UT_hash_handle *_thh; \ + if (head) { \ + unsigned _bkt_i; \ + unsigned _count = 0; \ + char *_prev; \ + for (_bkt_i = 0; _bkt_i < (head)->hh.tbl->num_buckets; ++_bkt_i) { \ + unsigned _bkt_count = 0; \ + _thh = (head)->hh.tbl->buckets[_bkt_i].hh_head; \ + _prev = NULL; \ + while (_thh) { \ + if (_prev != (char*)(_thh->hh_prev)) { \ + HASH_OOPS("%s: invalid hh_prev %p, actual %p\n", \ + (where), (void*)_thh->hh_prev, (void*)_prev); \ + } \ + _bkt_count++; \ + _prev = (char*)(_thh); \ + _thh = _thh->hh_next; \ + } \ + _count += _bkt_count; \ + if ((head)->hh.tbl->buckets[_bkt_i].count != _bkt_count) { \ + HASH_OOPS("%s: invalid bucket count %u, actual %u\n", \ + (where), (head)->hh.tbl->buckets[_bkt_i].count, _bkt_count); \ + } \ + } \ + if (_count != (head)->hh.tbl->num_items) { \ + HASH_OOPS("%s: invalid hh item count %u, actual %u\n", \ + (where), (head)->hh.tbl->num_items, _count); \ + } \ + _count = 0; \ + _prev = NULL; \ + _thh = &(head)->hh; \ + while (_thh) { \ + _count++; \ + if (_prev != (char*)_thh->prev) { \ + HASH_OOPS("%s: invalid prev %p, actual %p\n", \ + (where), (void*)_thh->prev, (void*)_prev); \ + } \ + _prev = (char*)ELMT_FROM_HH((head)->hh.tbl, _thh); \ + _thh = (_thh->next ? HH_FROM_ELMT((head)->hh.tbl, _thh->next) : NULL); \ + } \ + if (_count != (head)->hh.tbl->num_items) { \ + HASH_OOPS("%s: invalid app item count %u, actual %u\n", \ + (where), (head)->hh.tbl->num_items, _count); \ + } \ + } \ +} while (0) +#else +#define HASH_FSCK(hh,head,where) +#endif + +/* When compiled with -DHASH_EMIT_KEYS, length-prefixed keys are emitted to + * the descriptor to which this macro is defined for tuning the hash function. + * The app can #include to get the prototype for write(2). */ +#ifdef HASH_EMIT_KEYS +#define HASH_EMIT_KEY(hh,head,keyptr,fieldlen) \ +do { \ + unsigned _klen = fieldlen; \ + write(HASH_EMIT_KEYS, &_klen, sizeof(_klen)); \ + write(HASH_EMIT_KEYS, keyptr, (unsigned long)fieldlen); \ +} while (0) +#else +#define HASH_EMIT_KEY(hh,head,keyptr,fieldlen) +#endif + +/* The Bernstein hash function, used in Perl prior to v5.6. Note (x<<5+x)=x*33. */ +#define HASH_BER(key,keylen,hashv) \ +do { \ + unsigned _hb_keylen = (unsigned)keylen; \ + const unsigned char *_hb_key = (const unsigned char*)(key); \ + (hashv) = 0; \ + while (_hb_keylen-- != 0U) { \ + (hashv) = (((hashv) << 5) + (hashv)) + *_hb_key++; \ + } \ +} while (0) + + +/* SAX/FNV/OAT/JEN hash functions are macro variants of those listed at + * http://eternallyconfuzzled.com/tuts/algorithms/jsw_tut_hashing.aspx + * (archive link: https://archive.is/Ivcan ) + */ +#define HASH_SAX(key,keylen,hashv) \ +do { \ + unsigned _sx_i; \ + const unsigned char *_hs_key = (const unsigned char*)(key); \ + hashv = 0; \ + for (_sx_i=0; _sx_i < keylen; _sx_i++) { \ + hashv ^= (hashv << 5) + (hashv >> 2) + _hs_key[_sx_i]; \ + } \ +} while (0) +/* FNV-1a variation */ +#define HASH_FNV(key,keylen,hashv) \ +do { \ + unsigned _fn_i; \ + const unsigned char *_hf_key = (const unsigned char*)(key); \ + (hashv) = 2166136261U; \ + for (_fn_i=0; _fn_i < keylen; _fn_i++) { \ + hashv = hashv ^ _hf_key[_fn_i]; \ + hashv = hashv * 16777619U; \ + } \ +} while (0) + +#define HASH_OAT(key,keylen,hashv) \ +do { \ + unsigned _ho_i; \ + const unsigned char *_ho_key=(const unsigned char*)(key); \ + hashv = 0; \ + for(_ho_i=0; _ho_i < keylen; _ho_i++) { \ + hashv += _ho_key[_ho_i]; \ + hashv += (hashv << 10); \ + hashv ^= (hashv >> 6); \ + } \ + hashv += (hashv << 3); \ + hashv ^= (hashv >> 11); \ + hashv += (hashv << 15); \ +} while (0) + +#define HASH_JEN_MIX(a,b,c) \ +do { \ + a -= b; a -= c; a ^= ( c >> 13 ); \ + b -= c; b -= a; b ^= ( a << 8 ); \ + c -= a; c -= b; c ^= ( b >> 13 ); \ + a -= b; a -= c; a ^= ( c >> 12 ); \ + b -= c; b -= a; b ^= ( a << 16 ); \ + c -= a; c -= b; c ^= ( b >> 5 ); \ + a -= b; a -= c; a ^= ( c >> 3 ); \ + b -= c; b -= a; b ^= ( a << 10 ); \ + c -= a; c -= b; c ^= ( b >> 15 ); \ +} while (0) + +#define HASH_JEN(key,keylen,hashv) \ +do { \ + unsigned _hj_i,_hj_j,_hj_k; \ + unsigned const char *_hj_key=(unsigned const char*)(key); \ + hashv = 0xfeedbeefu; \ + _hj_i = _hj_j = 0x9e3779b9u; \ + _hj_k = (unsigned)(keylen); \ + while (_hj_k >= 12U) { \ + _hj_i += (_hj_key[0] + ( (unsigned)_hj_key[1] << 8 ) \ + + ( (unsigned)_hj_key[2] << 16 ) \ + + ( (unsigned)_hj_key[3] << 24 ) ); \ + _hj_j += (_hj_key[4] + ( (unsigned)_hj_key[5] << 8 ) \ + + ( (unsigned)_hj_key[6] << 16 ) \ + + ( (unsigned)_hj_key[7] << 24 ) ); \ + hashv += (_hj_key[8] + ( (unsigned)_hj_key[9] << 8 ) \ + + ( (unsigned)_hj_key[10] << 16 ) \ + + ( (unsigned)_hj_key[11] << 24 ) ); \ + \ + HASH_JEN_MIX(_hj_i, _hj_j, hashv); \ + \ + _hj_key += 12; \ + _hj_k -= 12U; \ + } \ + hashv += (unsigned)(keylen); \ + switch ( _hj_k ) { \ + case 11: hashv += ( (unsigned)_hj_key[10] << 24 ); /* FALLTHROUGH */ \ + case 10: hashv += ( (unsigned)_hj_key[9] << 16 ); /* FALLTHROUGH */ \ + case 9: hashv += ( (unsigned)_hj_key[8] << 8 ); /* FALLTHROUGH */ \ + case 8: _hj_j += ( (unsigned)_hj_key[7] << 24 ); /* FALLTHROUGH */ \ + case 7: _hj_j += ( (unsigned)_hj_key[6] << 16 ); /* FALLTHROUGH */ \ + case 6: _hj_j += ( (unsigned)_hj_key[5] << 8 ); /* FALLTHROUGH */ \ + case 5: _hj_j += _hj_key[4]; /* FALLTHROUGH */ \ + case 4: _hj_i += ( (unsigned)_hj_key[3] << 24 ); /* FALLTHROUGH */ \ + case 3: _hj_i += ( (unsigned)_hj_key[2] << 16 ); /* FALLTHROUGH */ \ + case 2: _hj_i += ( (unsigned)_hj_key[1] << 8 ); /* FALLTHROUGH */ \ + case 1: _hj_i += _hj_key[0]; /* FALLTHROUGH */ \ + default: ; \ + } \ + HASH_JEN_MIX(_hj_i, _hj_j, hashv); \ +} while (0) + +/* The Paul Hsieh hash function */ +#undef get16bits +#if (defined(__GNUC__) && defined(__i386__)) || defined(__WATCOMC__) \ + || defined(_MSC_VER) || defined (__BORLANDC__) || defined (__TURBOC__) +#define get16bits(d) (*((const uint16_t *) (d))) +#endif + +#if !defined (get16bits) +#define get16bits(d) ((((uint32_t)(((const uint8_t *)(d))[1])) << 8) \ + +(uint32_t)(((const uint8_t *)(d))[0]) ) +#endif +#define HASH_SFH(key,keylen,hashv) \ +do { \ + unsigned const char *_sfh_key=(unsigned const char*)(key); \ + uint32_t _sfh_tmp, _sfh_len = (uint32_t)keylen; \ + \ + unsigned _sfh_rem = _sfh_len & 3U; \ + _sfh_len >>= 2; \ + hashv = 0xcafebabeu; \ + \ + /* Main loop */ \ + for (;_sfh_len > 0U; _sfh_len--) { \ + hashv += get16bits (_sfh_key); \ + _sfh_tmp = ((uint32_t)(get16bits (_sfh_key+2)) << 11) ^ hashv; \ + hashv = (hashv << 16) ^ _sfh_tmp; \ + _sfh_key += 2U*sizeof (uint16_t); \ + hashv += hashv >> 11; \ + } \ + \ + /* Handle end cases */ \ + switch (_sfh_rem) { \ + case 3: hashv += get16bits (_sfh_key); \ + hashv ^= hashv << 16; \ + hashv ^= (uint32_t)(_sfh_key[sizeof (uint16_t)]) << 18; \ + hashv += hashv >> 11; \ + break; \ + case 2: hashv += get16bits (_sfh_key); \ + hashv ^= hashv << 11; \ + hashv += hashv >> 17; \ + break; \ + case 1: hashv += *_sfh_key; \ + hashv ^= hashv << 10; \ + hashv += hashv >> 1; \ + break; \ + default: ; \ + } \ + \ + /* Force "avalanching" of final 127 bits */ \ + hashv ^= hashv << 3; \ + hashv += hashv >> 5; \ + hashv ^= hashv << 4; \ + hashv += hashv >> 17; \ + hashv ^= hashv << 25; \ + hashv += hashv >> 6; \ +} while (0) + +/* iterate over items in a known bucket to find desired item */ +#define HASH_FIND_IN_BKT(tbl,hh,head,keyptr,keylen_in,hashval,out) \ +do { \ + if ((head).hh_head != NULL) { \ + DECLTYPE_ASSIGN(out, ELMT_FROM_HH(tbl, (head).hh_head)); \ + } else { \ + (out) = NULL; \ + } \ + while ((out) != NULL) { \ + if ((out)->hh.hashv == (hashval) && (out)->hh.keylen == (keylen_in)) { \ + if (HASH_KEYCMP((out)->hh.key, keyptr, keylen_in) == 0) { \ + break; \ + } \ + } \ + if ((out)->hh.hh_next != NULL) { \ + DECLTYPE_ASSIGN(out, ELMT_FROM_HH(tbl, (out)->hh.hh_next)); \ + } else { \ + (out) = NULL; \ + } \ + } \ +} while (0) + +/* add an item to a bucket */ +#define HASH_ADD_TO_BKT(head,hh,addhh,oomed) \ +do { \ + UT_hash_bucket *_ha_head = &(head); \ + _ha_head->count++; \ + (addhh)->hh_next = _ha_head->hh_head; \ + (addhh)->hh_prev = NULL; \ + if (_ha_head->hh_head != NULL) { \ + _ha_head->hh_head->hh_prev = (addhh); \ + } \ + _ha_head->hh_head = (addhh); \ + if ((_ha_head->count >= ((_ha_head->expand_mult + 1U) * HASH_BKT_CAPACITY_THRESH)) \ + && !(addhh)->tbl->noexpand) { \ + HASH_EXPAND_BUCKETS(addhh,(addhh)->tbl, oomed); \ + IF_HASH_NONFATAL_OOM( \ + if (oomed) { \ + HASH_DEL_IN_BKT(head,addhh); \ + } \ + ) \ + } \ +} while (0) + +/* remove an item from a given bucket */ +#define HASH_DEL_IN_BKT(head,delhh) \ +do { \ + UT_hash_bucket *_hd_head = &(head); \ + _hd_head->count--; \ + if (_hd_head->hh_head == (delhh)) { \ + _hd_head->hh_head = (delhh)->hh_next; \ + } \ + if ((delhh)->hh_prev) { \ + (delhh)->hh_prev->hh_next = (delhh)->hh_next; \ + } \ + if ((delhh)->hh_next) { \ + (delhh)->hh_next->hh_prev = (delhh)->hh_prev; \ + } \ +} while (0) + +/* Bucket expansion has the effect of doubling the number of buckets + * and redistributing the items into the new buckets. Ideally the + * items will distribute more or less evenly into the new buckets + * (the extent to which this is true is a measure of the quality of + * the hash function as it applies to the key domain). + * + * With the items distributed into more buckets, the chain length + * (item count) in each bucket is reduced. Thus by expanding buckets + * the hash keeps a bound on the chain length. This bounded chain + * length is the essence of how a hash provides constant time lookup. + * + * The calculation of tbl->ideal_chain_maxlen below deserves some + * explanation. First, keep in mind that we're calculating the ideal + * maximum chain length based on the *new* (doubled) bucket count. + * In fractions this is just n/b (n=number of items,b=new num buckets). + * Since the ideal chain length is an integer, we want to calculate + * ceil(n/b). We don't depend on floating point arithmetic in this + * hash, so to calculate ceil(n/b) with integers we could write + * + * ceil(n/b) = (n/b) + ((n%b)?1:0) + * + * and in fact a previous version of this hash did just that. + * But now we have improved things a bit by recognizing that b is + * always a power of two. We keep its base 2 log handy (call it lb), + * so now we can write this with a bit shift and logical AND: + * + * ceil(n/b) = (n>>lb) + ( (n & (b-1)) ? 1:0) + * + */ +#define HASH_EXPAND_BUCKETS(hh,tbl,oomed) \ +do { \ + unsigned _he_bkt; \ + unsigned _he_bkt_i; \ + struct UT_hash_handle *_he_thh, *_he_hh_nxt; \ + UT_hash_bucket *_he_new_buckets, *_he_newbkt; \ + _he_new_buckets = (UT_hash_bucket*)uthash_malloc( \ + sizeof(struct UT_hash_bucket) * (tbl)->num_buckets * 2U); \ + if (!_he_new_buckets) { \ + HASH_RECORD_OOM(oomed); \ + } else { \ + uthash_bzero(_he_new_buckets, \ + sizeof(struct UT_hash_bucket) * (tbl)->num_buckets * 2U); \ + (tbl)->ideal_chain_maxlen = \ + ((tbl)->num_items >> ((tbl)->log2_num_buckets+1U)) + \ + ((((tbl)->num_items & (((tbl)->num_buckets*2U)-1U)) != 0U) ? 1U : 0U); \ + (tbl)->nonideal_items = 0; \ + for (_he_bkt_i = 0; _he_bkt_i < (tbl)->num_buckets; _he_bkt_i++) { \ + _he_thh = (tbl)->buckets[ _he_bkt_i ].hh_head; \ + while (_he_thh != NULL) { \ + _he_hh_nxt = _he_thh->hh_next; \ + HASH_TO_BKT(_he_thh->hashv, (tbl)->num_buckets * 2U, _he_bkt); \ + _he_newbkt = &(_he_new_buckets[_he_bkt]); \ + if (++(_he_newbkt->count) > (tbl)->ideal_chain_maxlen) { \ + (tbl)->nonideal_items++; \ + if (_he_newbkt->count > _he_newbkt->expand_mult * (tbl)->ideal_chain_maxlen) { \ + _he_newbkt->expand_mult++; \ + } \ + } \ + _he_thh->hh_prev = NULL; \ + _he_thh->hh_next = _he_newbkt->hh_head; \ + if (_he_newbkt->hh_head != NULL) { \ + _he_newbkt->hh_head->hh_prev = _he_thh; \ + } \ + _he_newbkt->hh_head = _he_thh; \ + _he_thh = _he_hh_nxt; \ + } \ + } \ + uthash_free((tbl)->buckets, (tbl)->num_buckets * sizeof(struct UT_hash_bucket)); \ + (tbl)->num_buckets *= 2U; \ + (tbl)->log2_num_buckets++; \ + (tbl)->buckets = _he_new_buckets; \ + (tbl)->ineff_expands = ((tbl)->nonideal_items > ((tbl)->num_items >> 1)) ? \ + ((tbl)->ineff_expands+1U) : 0U; \ + if ((tbl)->ineff_expands > 1U) { \ + (tbl)->noexpand = 1; \ + uthash_noexpand_fyi(tbl); \ + } \ + uthash_expand_fyi(tbl); \ + } \ +} while (0) + + +/* This is an adaptation of Simon Tatham's O(n log(n)) mergesort */ +/* Note that HASH_SORT assumes the hash handle name to be hh. + * HASH_SRT was added to allow the hash handle name to be passed in. */ +#define HASH_SORT(head,cmpfcn) HASH_SRT(hh,head,cmpfcn) +#define HASH_SRT(hh,head,cmpfcn) \ +do { \ + unsigned _hs_i; \ + unsigned _hs_looping,_hs_nmerges,_hs_insize,_hs_psize,_hs_qsize; \ + struct UT_hash_handle *_hs_p, *_hs_q, *_hs_e, *_hs_list, *_hs_tail; \ + if (head != NULL) { \ + _hs_insize = 1; \ + _hs_looping = 1; \ + _hs_list = &((head)->hh); \ + while (_hs_looping != 0U) { \ + _hs_p = _hs_list; \ + _hs_list = NULL; \ + _hs_tail = NULL; \ + _hs_nmerges = 0; \ + while (_hs_p != NULL) { \ + _hs_nmerges++; \ + _hs_q = _hs_p; \ + _hs_psize = 0; \ + for (_hs_i = 0; _hs_i < _hs_insize; ++_hs_i) { \ + _hs_psize++; \ + _hs_q = ((_hs_q->next != NULL) ? \ + HH_FROM_ELMT((head)->hh.tbl, _hs_q->next) : NULL); \ + if (_hs_q == NULL) { \ + break; \ + } \ + } \ + _hs_qsize = _hs_insize; \ + while ((_hs_psize != 0U) || ((_hs_qsize != 0U) && (_hs_q != NULL))) { \ + if (_hs_psize == 0U) { \ + _hs_e = _hs_q; \ + _hs_q = ((_hs_q->next != NULL) ? \ + HH_FROM_ELMT((head)->hh.tbl, _hs_q->next) : NULL); \ + _hs_qsize--; \ + } else if ((_hs_qsize == 0U) || (_hs_q == NULL)) { \ + _hs_e = _hs_p; \ + if (_hs_p != NULL) { \ + _hs_p = ((_hs_p->next != NULL) ? \ + HH_FROM_ELMT((head)->hh.tbl, _hs_p->next) : NULL); \ + } \ + _hs_psize--; \ + } else if ((cmpfcn( \ + DECLTYPE(head)(ELMT_FROM_HH((head)->hh.tbl, _hs_p)), \ + DECLTYPE(head)(ELMT_FROM_HH((head)->hh.tbl, _hs_q)) \ + )) <= 0) { \ + _hs_e = _hs_p; \ + if (_hs_p != NULL) { \ + _hs_p = ((_hs_p->next != NULL) ? \ + HH_FROM_ELMT((head)->hh.tbl, _hs_p->next) : NULL); \ + } \ + _hs_psize--; \ + } else { \ + _hs_e = _hs_q; \ + _hs_q = ((_hs_q->next != NULL) ? \ + HH_FROM_ELMT((head)->hh.tbl, _hs_q->next) : NULL); \ + _hs_qsize--; \ + } \ + if ( _hs_tail != NULL ) { \ + _hs_tail->next = ((_hs_e != NULL) ? \ + ELMT_FROM_HH((head)->hh.tbl, _hs_e) : NULL); \ + } else { \ + _hs_list = _hs_e; \ + } \ + if (_hs_e != NULL) { \ + _hs_e->prev = ((_hs_tail != NULL) ? \ + ELMT_FROM_HH((head)->hh.tbl, _hs_tail) : NULL); \ + } \ + _hs_tail = _hs_e; \ + } \ + _hs_p = _hs_q; \ + } \ + if (_hs_tail != NULL) { \ + _hs_tail->next = NULL; \ + } \ + if (_hs_nmerges <= 1U) { \ + _hs_looping = 0; \ + (head)->hh.tbl->tail = _hs_tail; \ + DECLTYPE_ASSIGN(head, ELMT_FROM_HH((head)->hh.tbl, _hs_list)); \ + } \ + _hs_insize *= 2U; \ + } \ + HASH_FSCK(hh, head, "HASH_SRT"); \ + } \ +} while (0) + +/* This function selects items from one hash into another hash. + * The end result is that the selected items have dual presence + * in both hashes. There is no copy of the items made; rather + * they are added into the new hash through a secondary hash + * hash handle that must be present in the structure. */ +#define HASH_SELECT(hh_dst, dst, hh_src, src, cond) \ +do { \ + unsigned _src_bkt, _dst_bkt; \ + void *_last_elt = NULL, *_elt; \ + UT_hash_handle *_src_hh, *_dst_hh, *_last_elt_hh=NULL; \ + ptrdiff_t _dst_hho = ((char*)(&(dst)->hh_dst) - (char*)(dst)); \ + if ((src) != NULL) { \ + for (_src_bkt=0; _src_bkt < (src)->hh_src.tbl->num_buckets; _src_bkt++) { \ + for (_src_hh = (src)->hh_src.tbl->buckets[_src_bkt].hh_head; \ + _src_hh != NULL; \ + _src_hh = _src_hh->hh_next) { \ + _elt = ELMT_FROM_HH((src)->hh_src.tbl, _src_hh); \ + if (cond(_elt)) { \ + IF_HASH_NONFATAL_OOM( int _hs_oomed = 0; ) \ + _dst_hh = (UT_hash_handle*)(void*)(((char*)_elt) + _dst_hho); \ + _dst_hh->key = _src_hh->key; \ + _dst_hh->keylen = _src_hh->keylen; \ + _dst_hh->hashv = _src_hh->hashv; \ + _dst_hh->prev = _last_elt; \ + _dst_hh->next = NULL; \ + if (_last_elt_hh != NULL) { \ + _last_elt_hh->next = _elt; \ + } \ + if ((dst) == NULL) { \ + DECLTYPE_ASSIGN(dst, _elt); \ + HASH_MAKE_TABLE(hh_dst, dst, _hs_oomed); \ + IF_HASH_NONFATAL_OOM( \ + if (_hs_oomed) { \ + uthash_nonfatal_oom(_elt); \ + (dst) = NULL; \ + continue; \ + } \ + ) \ + } else { \ + _dst_hh->tbl = (dst)->hh_dst.tbl; \ + } \ + HASH_TO_BKT(_dst_hh->hashv, _dst_hh->tbl->num_buckets, _dst_bkt); \ + HASH_ADD_TO_BKT(_dst_hh->tbl->buckets[_dst_bkt], hh_dst, _dst_hh, _hs_oomed); \ + (dst)->hh_dst.tbl->num_items++; \ + IF_HASH_NONFATAL_OOM( \ + if (_hs_oomed) { \ + HASH_ROLLBACK_BKT(hh_dst, dst, _dst_hh); \ + HASH_DELETE_HH(hh_dst, dst, _dst_hh); \ + _dst_hh->tbl = NULL; \ + uthash_nonfatal_oom(_elt); \ + continue; \ + } \ + ) \ + HASH_BLOOM_ADD(_dst_hh->tbl, _dst_hh->hashv); \ + _last_elt = _elt; \ + _last_elt_hh = _dst_hh; \ + } \ + } \ + } \ + } \ + HASH_FSCK(hh_dst, dst, "HASH_SELECT"); \ +} while (0) + +#define HASH_CLEAR(hh,head) \ +do { \ + if ((head) != NULL) { \ + HASH_BLOOM_FREE((head)->hh.tbl); \ + uthash_free((head)->hh.tbl->buckets, \ + (head)->hh.tbl->num_buckets*sizeof(struct UT_hash_bucket)); \ + uthash_free((head)->hh.tbl, sizeof(UT_hash_table)); \ + (head) = NULL; \ + } \ +} while (0) + +#define HASH_OVERHEAD(hh,head) \ + (((head) != NULL) ? ( \ + (size_t)(((head)->hh.tbl->num_items * sizeof(UT_hash_handle)) + \ + ((head)->hh.tbl->num_buckets * sizeof(UT_hash_bucket)) + \ + sizeof(UT_hash_table) + \ + (HASH_BLOOM_BYTELEN))) : 0U) + +#ifdef NO_DECLTYPE +#define HASH_ITER(hh,head,el,tmp) \ +for(((el)=(head)), ((*(char**)(&(tmp)))=(char*)((head!=NULL)?(head)->hh.next:NULL)); \ + (el) != NULL; ((el)=(tmp)), ((*(char**)(&(tmp)))=(char*)((tmp!=NULL)?(tmp)->hh.next:NULL))) +#else +#define HASH_ITER(hh,head,el,tmp) \ +for(((el)=(head)), ((tmp)=DECLTYPE(el)((head!=NULL)?(head)->hh.next:NULL)); \ + (el) != NULL; ((el)=(tmp)), ((tmp)=DECLTYPE(el)((tmp!=NULL)?(tmp)->hh.next:NULL))) +#endif + +/* obtain a count of items in the hash */ +#define HASH_COUNT(head) HASH_CNT(hh,head) +#define HASH_CNT(hh,head) ((head != NULL)?((head)->hh.tbl->num_items):0U) + +typedef struct UT_hash_bucket { + struct UT_hash_handle *hh_head; + unsigned count; + + /* expand_mult is normally set to 0. In this situation, the max chain length + * threshold is enforced at its default value, HASH_BKT_CAPACITY_THRESH. (If + * the bucket's chain exceeds this length, bucket expansion is triggered). + * However, setting expand_mult to a non-zero value delays bucket expansion + * (that would be triggered by additions to this particular bucket) + * until its chain length reaches a *multiple* of HASH_BKT_CAPACITY_THRESH. + * (The multiplier is simply expand_mult+1). The whole idea of this + * multiplier is to reduce bucket expansions, since they are expensive, in + * situations where we know that a particular bucket tends to be overused. + * It is better to let its chain length grow to a longer yet-still-bounded + * value, than to do an O(n) bucket expansion too often. + */ + unsigned expand_mult; + +} UT_hash_bucket; + +/* random signature used only to find hash tables in external analysis */ +#define HASH_SIGNATURE 0xa0111fe1u +#define HASH_BLOOM_SIGNATURE 0xb12220f2u + +typedef struct UT_hash_table { + UT_hash_bucket *buckets; + unsigned num_buckets, log2_num_buckets; + unsigned num_items; + struct UT_hash_handle *tail; /* tail hh in app order, for fast append */ + ptrdiff_t hho; /* hash handle offset (byte pos of hash handle in element */ + + /* in an ideal situation (all buckets used equally), no bucket would have + * more than ceil(#items/#buckets) items. that's the ideal chain length. */ + unsigned ideal_chain_maxlen; + + /* nonideal_items is the number of items in the hash whose chain position + * exceeds the ideal chain maxlen. these items pay the penalty for an uneven + * hash distribution; reaching them in a chain traversal takes >ideal steps */ + unsigned nonideal_items; + + /* ineffective expands occur when a bucket doubling was performed, but + * afterward, more than half the items in the hash had nonideal chain + * positions. If this happens on two consecutive expansions we inhibit any + * further expansion, as it's not helping; this happens when the hash + * function isn't a good fit for the key domain. When expansion is inhibited + * the hash will still work, albeit no longer in constant time. */ + unsigned ineff_expands, noexpand; + + uint32_t signature; /* used only to find hash tables in external analysis */ +#ifdef HASH_BLOOM + uint32_t bloom_sig; /* used only to test bloom exists in external analysis */ + uint8_t *bloom_bv; + uint8_t bloom_nbits; +#endif + +} UT_hash_table; + +typedef struct UT_hash_handle { + struct UT_hash_table *tbl; + void *prev; /* prev element in app order */ + void *next; /* next element in app order */ + struct UT_hash_handle *hh_prev; /* previous hh in bucket order */ + struct UT_hash_handle *hh_next; /* next hh in bucket order */ + const void *key; /* ptr to enclosing struct's key */ + unsigned keylen; /* enclosing struct's key len */ + unsigned hashv; /* result of hash-fcn(key) */ +} UT_hash_handle; + +#endif /* UTHASH_H */ diff --git a/models/matris/matris/graph/radiusgraph.py b/models/matris/matris/graph/radiusgraph.py new file mode 100644 index 0000000000000000000000000000000000000000..5e31ff2d9367eaf7453e323aba0b2e67abc36b7c --- /dev/null +++ b/models/matris/matris/graph/radiusgraph.py @@ -0,0 +1,504 @@ +""" + This code is referenced from: https://github.com/CederGroupHub/chgnet/blob/main/chgnet/graph/converter.py + The original implementation can be found at the link above. +""" +from __future__ import annotations + +import os +from typing import Any + +import torch +from torch import Tensor +import sys +from abc import ABC, abstractmethod + +datatype = torch.float32 + +class RadiusGraph: + def __init__( + self, + graph_id: str | None, + mp_id: str | None, + composition: str | None, + atomic_number: Tensor, + atom_frac_coord: Tensor, + lattice: Tensor, + neighbor_image: Tensor, + atom_graph: Tensor, + atom_graph_cutoff: float, + line_graph: Tensor, + line_graph_cutoff: float, + directed2undirected: Tensor, + undirected2directed: Tensor, + ): + """Initialize a RadiusGraph object representing a material graph. + + Args: + graph_id (str | None): Unique identifier for this graph instance. + mp_id (str | None): Materials Project ID associated with this structure. + composition (str | None): Chemical composition of the compound. + + atomic_number (Tensor): Atomic numbers of all atoms in the structure. + Shape: [n_atom] + + atom_frac_coord (Tensor): Fractional atomic coordinates within the unit cell. + Shape: [n_atom, 3] + + lattice (Tensor): Lattice vectors defining the periodic cell. + Shape: [3, 3] + + neighbor_image (Tensor): Periodic image offsets for each directed edge, + indicating how many unit cells away the neighbor lies along each axis. + Shape: [n_directed_edges, 3] + + atom_graph (Tensor): Directed atomic graph, where each row stores + (center_atom_index, neighbor_atom_index). + Shape: [n_directed_edges, 2] + + atom_graph_cutoff (float): Cutoff radius (in Å) used to build the atom graph. + + line_graph (Tensor): Line graph describing angular connections between bonds. + Each row stores + (central_atom, undirected_bond_1, directed_bond_1, + undirected_bond_2, directed_bond_2). + Shape: [n_angle, 5] + + line_graph_cutoff (float): Cutoff radius (in Å) used to build the line graph. + + directed2undirected (Tensor): Mapping from each directed edge index + to its corresponding undirected edge index. + Shape: [n_directed_edges] + + undirected2directed (Tensor): Mapping from each undirected edge index + to one of its directed edge indices. + Shape: [n_undirected_edges] + """ + super().__init__() + self.graph_id = graph_id + self.mp_id = mp_id + self.composition = composition + + self.atomic_number = atomic_number + self.atom_frac_coord = atom_frac_coord + self.lattice = lattice + self.neighbor_image = neighbor_image + self.line_graph = line_graph + self.line_graph_cutoff = line_graph_cutoff + self.atom_graph = atom_graph + self.atom_graph_cutoff = atom_graph_cutoff + self.directed2undirected = directed2undirected + self.undirected2directed = undirected2directed + + assert len(directed2undirected) == 2 * len(undirected2directed), ( + f"Number of directed indices ({len(directed2undirected)}) != " + f"2 * number of undirected indices ({2 * len(undirected2directed)})!" + ) + + def to(self, device: str = "cpu") -> RadiusGraph: + """Move the graph to a device.""" + return RadiusGraph( + graph_id=self.graph_id, + mp_id=self.mp_id, + composition=self.composition, + atomic_number=self.atomic_number.to(device), + atom_frac_coord=self.atom_frac_coord.to(device), + lattice=self.lattice.to(device), + neighbor_image=self.neighbor_image.to(device), + atom_graph=self.atom_graph.to(device), + atom_graph_cutoff=self.atom_graph_cutoff, + line_graph=self.line_graph.to(device), + line_graph_cutoff=self.line_graph_cutoff, + directed2undirected=self.directed2undirected.to(device), + undirected2directed=self.undirected2directed.to(device), + ) + + def to_dict(self) -> dict[str, Any]: + return { + "graph_id": self.graph_id, + "mp_id": self.mp_id, + "composition": self.composition, + "atomic_number": self.atomic_number, + "atom_frac_coord": self.atom_frac_coord, + "lattice": self.lattice, + "neighbor_image": self.neighbor_image, + "atom_graph": self.atom_graph, + "atom_graph_cutoff": self.atom_graph_cutoff, + "line_graph": self.line_graph, + "line_graph_cutoff": self.line_graph_cutoff, + "directed2undirected": self.directed2undirected, + "undirected2directed": self.undirected2directed, + } + + def save(self, fname: str | None = None, save_dir: str = ".") -> str: + """Save the Radiusgraph to a file. + + Args: + fname (str, optional): File name. Defaults to None. + save_dir (str, optional): Directory to save the file. Defaults to ".". + """ + if fname is not None: + save_name = os.path.join(save_dir, fname) + elif self.graph_id is not None: + save_name = os.path.join(save_dir, f"{self.graph_id}.pt") + else: + save_name = os.path.join(save_dir, f"{self.composition}.pt") + torch.save(self.to_dict(), f=save_name) + return save_name + + @classmethod + def from_file(cls, file_name: str) -> RadiusGraph: + """Load a Radiusgraph from a file. + + Args: + file_name (str): The path to the file. + """ + return torch.load(file_name) + + @classmethod + def from_dict(cls, dic: dict[str, Any]) -> RadiusGraph: + """Load a RadiusGraph from a dictionary.""" + return RadiusGraph(**dic) + + def __repr__(self) -> str: + """String representation of the graph.""" + composition = self.composition + atom_graph_cutoff = self.atom_graph_cutoff + line_graph_cutoff = self.line_graph_cutoff + atom_graph_len = self.atom_graph + n_atoms = len(self.atomic_number) + atom_graph_len = len(self.atom_graph) + line_graph_len = len(self.line_graph) + return ( + f"RadiusGraph({composition=}, {atom_graph_cutoff=}, {line_graph_cutoff=}, " + f"{n_atoms=}, {atom_graph_len=}, {line_graph_len=})" + ) + + +class Node: + """A node in graph.""" + + def __init__(self, index: int, info: dict | None = None) -> None: + """Initialize a Node. + + Args: + index (int): the index of this node + info (dict, optional): any additional information about this node. + """ + self.index = index + self.info = info + self.neighbors: dict[int, list[DirectedEdge | UndirectedEdge]] = {} + + def add_neighbor(self, index, edge): + """Draw an directed edge between self and the node specified by index. + + Args: + index (int): the index of neighboring node + edge (DirectedEdge): an DirectedEdge object pointing from self to the node. + """ + if index not in self.neighbors: + self.neighbors[index] = [edge] + else: + self.neighbors[index].append(edge) + + +class Edge(ABC): + """Abstract base class for edges in a graph.""" + + def __init__( + self, nodes: list, index: int | None = None, info: dict | None = None + ) -> None: + """Initialize an Edge.""" + self.nodes = nodes + self.index = index + self.info = info + + def __repr__(self): + """String representation of this edge.""" + nodes, index, info = self.nodes, self.index, self.info + return f"{type(self).__name__}({nodes=}, {index=}, {info=})" + + def __hash__(self) -> int: + """Hash this edge.""" + img = (self.info or {}).get("image") + img_str = "" if img is None else img.tobytes() + return hash((self.nodes[0], self.nodes[1], img_str)) + + @abstractmethod + def __eq__(self, other: object) -> bool: + """Check if two edges are equal.""" + raise NotImplementedError + + +class UndirectedEdge(Edge): + """An undirected/bi-directed edge in a graph.""" + + __hash__ = Edge.__hash__ + + def __eq__(self, other): + """Check if two undirected edges are equal.""" + return set(self.nodes) == set(other.nodes) and self.info == other.info + + +class DirectedEdge(Edge): + """A directed edge in a graph.""" + + __hash__ = Edge.__hash__ + + def make_undirected(self, index: int, info: dict | None = None) -> UndirectedEdge: + """Make a directed edge undirected.""" + info = info or {} + info["distance"] = self.info["distance"] + return UndirectedEdge(self.nodes, index, info) + + def __eq__(self, other: object) -> bool: + """Check if the two directed edges are equal. + + Args: + other (DirectedEdge): another DirectedEdge to compare to + + Returns: + bool: True if other is the same directed edge, or if other is the directed + edge with reverse direction of self, else False. + """ + self_img = (self.info or {}).get("image") + other_img = (other.info or {}).get("image") + none_img = self_img is other_img is None + if self.nodes == other.nodes and (none_img or all(self_img == other_img)): + # the image key here is provided by Pymatgen, which refers to the periodic + # cell the neighbor node comes from + + # In this case the two directed edges are exactly the same, but this is not + # supposed tp happen unless there's a bug in Pymatgen. (we will never add + # the same edge twice in creating a crystal graph.) + print( + "!!!!!! the two directed edges are equal but this operation is " + "not supposed to happen", + file=sys.stderr, + ) + return True + + # In this case the first edge is from node i to j and the second edge is + # from node j to i + return self.nodes == other.nodes[::-1] and ( + none_img or all(self_img == -1 * other_img) + ) + + +class Graph: + """A graph for storing the neighbor information of atoms.""" + + def __init__(self, nodes: list[Node]) -> None: + """Initialize a Graph from a list of nodes.""" + self.nodes = nodes + self.directed_edges: dict[frozenset[int], list[DirectedEdge]] = {} + self.directed_edges_list: list[DirectedEdge] = [] + self.undirected_edges: dict[frozenset[int], list[UndirectedEdge]] = {} + self.undirected_edges_list: list[UndirectedEdge] = [] + + def add_edge(self, center_index, neighbor_index, image, distance) -> None: + """Add an directed edge to the graph. + + Args: + center_index (int): center node index + neighbor_index (int): neighbor node index + image (np.array): the periodic cell image the neighbor is from + distance (float): distance between center and neighbor. + """ + # Create directed_edge (DE) index using the length of added DEs + directed_edge_index = len(self.directed_edges_list) + + # Create the DE with info + this_directed_edge = DirectedEdge( + [center_index, neighbor_index], + index=directed_edge_index, + info={"image": image, "distance": distance}, + ) + # Record the two ends of the DE and see if they have been added + # Here the sequence of the ends doesn't matter since we want to + # search for associated undirected_edge (UDE) + tmp = frozenset([center_index, neighbor_index]) + # This combination of two ends hasn't been added + if tmp not in self.undirected_edges: + # Create UDE index + this_directed_edge.info["undirected_edge_index"] = len( + self.undirected_edges_list + ) + # Create UDE + this_undirected_edge = this_directed_edge.make_undirected( + index=len(self.undirected_edges_list), + info={"directed_edge_index": [directed_edge_index]}, + ) + self.undirected_edges[tmp] = [this_undirected_edge] + + self.undirected_edges_list.append(this_undirected_edge) + self.nodes[center_index].add_neighbor(neighbor_index, this_directed_edge) + self.directed_edges_list.append(this_directed_edge) + else: + # This pair of nodes has been added before, we need to see if this time, + # 1. it's the other directed edge of the same undirected edge or, + # 2. it's another totally different undirected edge that has + # different image and distance (this is possible consider periodicity) + for undirected_edge in self.undirected_edges[tmp]: + if ( + abs(undirected_edge.info["distance"] - distance) < 1e-6 + and len(undirected_edge.info["directed_edge_index"]) == 1 + ): + # There is an undirected edge with similar length and only one of + # the directed edges associated has been added + added_DE = self.directed_edges_list[ + undirected_edge.info["directed_edge_index"][0] + ] + + # See if the DE that's associated to this UDE + # is the reverse of our DE + if added_DE == this_directed_edge: + # Add UDE index to this DE + this_directed_edge.info[ + "undirected_edge_index" + ] = added_DE.info["undirected_edge_index"] + + # At the center node, draw edge with this DE + self.nodes[center_index].add_neighbor( + neighbor_index, this_directed_edge + ) + + # Add this DE to DE list + self.directed_edges_list.append(this_directed_edge) + + # Add this DE to the UDE + undirected_edge.info["directed_edge_index"].append( + directed_edge_index + ) + return + + # no undirected_edge matches to this directed edge + this_directed_edge.info["undirected_edge_index"] = len( + self.undirected_edges_list + ) + this_undirected_edge = this_directed_edge.make_undirected( + index=len(self.undirected_edges_list), + info={"directed_edge_index": [directed_edge_index]}, + ) + self.undirected_edges[tmp].append(this_undirected_edge) + + self.undirected_edges_list.append(this_undirected_edge) + self.nodes[center_index].add_neighbor(neighbor_index, this_directed_edge) + self.directed_edges_list.append(this_directed_edge) + + def adjacency_list(self): + """Get the adjacency list + Return: + graph: the adjacency list + [[0, 1], + [0, 2], + ... + [5, 2] + ... ]] + the fist column specifies center/source node, + the second column specifies neighbor/destination node + directed2undirected: + [0, 1, ...] + a list of length = num_directed_edge that specifies + the undirected edge index corresponding to the directed edges + represented in each row in the graph adjacency list. + """ + graph = [edge.nodes for edge in self.directed_edges_list] + directed2undirected = [ + edge.info["undirected_edge_index"] for edge in self.directed_edges_list + ] + + return graph, directed2undirected + + def line_graph_adjacency_list(self, cutoff): + """Get the line graph adjacency list. + + Args: + cutoff (float): a float to indicate the maximum edge length to be included + in constructing the line graph, this is used to decrease computation + complexity + + Return: + line_graph: + [[0, 1, 1, 2, 2], + [0, 1, 1, 4, 23], + [1, 4, 23, 5, 66], + ... ... ] + the fist column specifies node(atom) index at this angle, + the second column specifies 1st undirected edge(left bond) index, + the third column specifies 1st directed edge(left bond) index, + the fourth column specifies 2nd undirected edge(right bond) index, + the fifth column specifies 2nd directed edge(right bond) index,. + undirected2directed: + [32, 45, ...] + a list of length = num_undirected_edge that + maps the undirected edge index to one of its directed edges indices + """ + assert len(self.directed_edges_list) == 2 * len(self.undirected_edges_list), ( + f"Error: number of directed edges={len(self.directed_edges_list)} != 2 * " + f"number of undirected edges={len(self.directed_edges_list)}!" + f"This indicates directed edges are not complete" + ) + line_graph = [] + #undirected2directed = [] + + # Loop through all the undirected edges(UDE) + for u_edge in self.undirected_edges_list: + # Create the mapping from the UDE index to one of its DE index + #undirected2directed.append(u_edge.info["directed_edge_index"][0]) + + # Ignore the UDE if its length is larger than cutoff (3A for default) + if u_edge.info["distance"] > cutoff: + continue + + # Assert if the UDE is valid + # if encountered exception, + # it means after Atom_Graph creation, the UDE has only 1 DE associated + # This exception is not encountered from the develop team's experience + assert len(u_edge.info["directed_edge_index"]) == 2, ( + "Did not find 2 Directed_edges !!!" + f"undirected edge {u_edge} has:" + f"edge.info['directed_edge_index'] = " + f"{u_edge.info['directed_edge_index']}" + f"len directed_edges_list = {len(self.directed_edges_list)}" + f"len undirected_edges_list = {len(self.undirected_edges_list)}" + ) + + # This UDE is valid to be considered as a node in Bond_Graph + + # Get the two ends (centers) and the two DE associated with this UDE + # DE1 should have center=center1 and DE2 should have center=center2 + # We will need to find directed edges with center = center1 + # and create angles with DE1, then do the same for center2 and DE2 + for center, DE in zip(u_edge.nodes, u_edge.info["directed_edge_index"]): + for directed_edges in self.nodes[center].neighbors.values(): + for directed_edge in directed_edges: + if directed_edge.index == DE: + continue + if directed_edge.info["distance"] < cutoff: + line_graph.append( + [ + center, + u_edge.index, + DE, + directed_edge.info["undirected_edge_index"], + directed_edge.index, + ] + ) + return line_graph#, undirected2directed + + def undirected2directed(self): + """The index map from undirected_edge index to one of its directed_edge + index. + """ + return [ + undirected_edge.info["directed_edge_index"][0] + for undirected_edge in self.undirected_edges_list + ] + + def __repr__(self) -> str: + """Return string representation of the Graph.""" + num_nodes = len(self.nodes) + num_directed_edges = len(self.directed_edges_list) + num_undirected_edges = len(self.undirected_edges_list) + return f"Graph({num_nodes=}, {num_directed_edges=}, {num_undirected_edges=})" + \ No newline at end of file diff --git a/models/matris/matris/graph/setup.py b/models/matris/matris/graph/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..468098b9b8464f6f52430efdcf1c8b668c55c696 --- /dev/null +++ b/models/matris/matris/graph/setup.py @@ -0,0 +1,8 @@ + +from setuptools import Extension, setup + +ext_modules = [Extension("matris.graph.cygraph", ["matris/graph/cygraph.pyx"])] + +setup(ext_modules=ext_modules, setup_requires=["Cython"]) + +# python setup.py build_ext --inplace \ No newline at end of file diff --git a/models/matris/matris/model/__init__.py b/models/matris/matris/model/__init__.py new file mode 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PolynomialEnvelope(nn.Module): + """ + Polynomial cutoff function for distance + Details are given in: https://doi.org/10.48550/arXiv.2106.08903 + """ + + def __init__(self, cutoff: float = 6, envelope_exponent: float = 8) -> None: + """Initialize the polynomial cutoff function. + + Args: + cutoff (float): cutoff radius in graph construction + Default = 6 + envelope_exponent (float): envelope exponent + Default = 8 + """ + super().__init__() + assert envelope_exponent > 0 + self.cutoff = cutoff + self.p = float(envelope_exponent) + self.a = -(self.p + 1) * (self.p + 2) / 2 + self.b = self.p * (self.p + 2) + self.c = -self.p * (self.p + 1) / 2 + + def forward(self, dist: Tensor) -> Tensor: + """ + Args: + dist (Tensor): radius distance tensor + + Returns: + polynomial cutoff functions: decaying from 1 to 0 + """ + if self.p != 0: + dist_scaled = dist / self.cutoff + + env_val = ( + 1 + + self.a * dist_scaled ** self.p + + self.b * dist_scaled ** (self.p + 1) + + self.c * dist_scaled ** (self.p + 2) + ) + + return torch.where(dist_scaled < 1, env_val, torch.zeros_like(dist_scaled)) + return dist.new_ones(dist.shape) + + +@torch.jit.script +def SphericalHarmonics(lmax: int, x: torch.Tensor) -> torch.Tensor: + + sh_0_0 = torch.ones_like(x) * 0.5 * math.sqrt(1.0 / math.pi) + if lmax == 0: + return torch.stack( + [ + sh_0_0, + ], + dim=-1, + ) + + sh_1_1 = math.sqrt(3.0 / (4.0 * math.pi)) * x + if lmax == 1: + return torch.stack([sh_0_0, sh_1_1], dim=-1) + + sh_2_2 = math.sqrt(5.0 / (16.0 * math.pi)) * (3.0 * x**2 - 1.0) + if lmax == 2: + return torch.stack([sh_0_0, sh_1_1, sh_2_2], dim=-1) + + sh_3_3 = math.sqrt(7.0 / (16.0 * math.pi)) * x * (5.0 * x**2 - 3.0) + if lmax == 3: + return torch.stack([sh_0_0, sh_1_1, sh_2_2, sh_3_3], dim=-1) + + raise ValueError("lmax <= 3") + +class FourierExpansion(nn.Module): + """ + Fourier expansion for three-body feature. + Details are given in: https://doi.org/10.1038/s42256-023-00716-3 + """ + + def __init__(self, max_f: int = 5, + scale: float = np.pi, + learnable: bool = False): + """ + Args: + max_f (int): the maximum frequency of the expansion. + Default = 5 + learnable (bool): whether to set the frequencies as learnable parameters. + Default = False + """ + super().__init__() + self.max_f = max_f + self.scale = np.sqrt(scale) + if learnable: + self.frequencies = torch.nn.Parameter( + data=torch.arange(1, max_f + 1, dtype=torch.float), + requires_grad=True, + ) + else: + self.register_buffer("frequencies", torch.arange(1, max_f + 1, dtype=torch.float)) + + def forward(self, feature: Tensor) -> Tensor: + """Apply Fourier expansion to feature.""" + result = feature.new_zeros(feature.shape[0], 1 + 2 * self.max_f) + result[:, 0] = 1 / torch.sqrt(torch.tensor([2])) + tmp = torch.outer(feature, self.frequencies) + result[:, 1 : self.max_f + 1] = torch.sin(tmp) + result[:, self.max_f + 1 :] = torch.cos(tmp) + return result / self.scale + + +class BesselExpansion(torch.nn.Module): + """ + Bessel expansion for pairwise feature + Details are given in: https://arxiv.org/abs/2003.03123 + """ + def __init__( + self, + num_radial: int = 9, + cutoff: float = 6, + envelope_exponent: int = 8, + learnable: bool = False, + ): + """ + Args: + num_radial (int): number of radial basis. + Default = 9 + cutoff (float): cutoff distance. + Default = 6 + envelope_exponent (int): envelope exponent of Envelope function. + Default = 8 + learnable (bool): whether to set the frequencies as learnable parameters. + Default = False + """ + super().__init__() + self.num_radial = num_radial + self.inv_cutoff = 1 / cutoff + self.norm_const = (2 * self.inv_cutoff) ** 0.5 + + if learnable: + self.frequencies = torch.nn.Parameter( + data=torch.Tensor( + np.pi * torch.arange(1, self.num_radial + 1, dtype=torch.float) + ), requires_grad=True, + ) + else: + self.register_buffer( + "frequencies", + np.pi * torch.arange(1, self.num_radial + 1, dtype=torch.float), + ) + if envelope_exponent is not None: + self.envelope_func = PolynomialEnvelope( + cutoff=cutoff, envelope_exponent=envelope_exponent + ) + else: + self.envelope_func = None + + def forward( + self, dist: Tensor, return_smooth_factor: bool = False + ) -> Tensor | tuple[Tensor, Tensor]: + """Apply Bessel expansion to feature. + + Args: + dist (Tensor): tensor of distances. [nEdges] + return_smooth_factor (bool): whether to return the smooth factor + Default = False + + Returns: + out (Tensor): tensor of Bessel basis [nEdges, num_radial] + smooth_factor (Tensor): tensor of smooth factors [nEdges, 1] + + TODO: 'torch.script.jit' boosts performance. + """ + dist = dist[:, None] # [nEdges] -> [nEdges, 1] + d_scaled = dist * self.inv_cutoff + out = self.norm_const * torch.sin(self.frequencies * d_scaled) / dist + if self.envelope_func is not None: + smooth_factor = self.envelope_func(dist) + out = smooth_factor * out + if return_smooth_factor: + return out, smooth_factor + return out + + +class GaussianExpansion(nn.Module): + """ + Gaussian expansion for pairwise feature. + Code adapted from the repo: https://github.com/facebookresearch/fairchem/blob/main/src/fairchem/core/models/uma/nn/radial.py#L42 + """ + def __init__( + self, + start: float = 0.0, + stop: float = 6.0, + num_gaussians: int = 7, + basis_width_scalar: float = 2.0, + ): + super().__init__() + self.num_output = num_gaussians + offset = torch.linspace(start, stop, num_gaussians) + self.coeff = -0.5 / (basis_width_scalar * (offset[1] - offset[0])).item() ** 2 + self.register_buffer("offset", offset) + + def forward(self, dist) -> Tensor: + """Apply Bessel expansion to feature. + + Args: + dist (Tensor): tensor of distances. [nEdges] + + Returns: + out (Tensor): tensor of Bessel basis [nEdges, num_gaussians] + + TODO: 'torch.script.jit' boosts performance. + """ + dist = dist.view(-1, 1) - self.offset.view(1, -1) + return torch.exp(self.coeff * torch.pow(dist, 2)) + + +class SphericalExpansion(nn.Module): + """ + Spherical expansion for three-body feature. + Code adapted from the repo: https://github.com/microsoft/mattersim + """ + def __init__(self, max_n = 4, max_l = 4, cutoff = 6): + super().__init__() + + assert max_l <= 4, "lmax must be less than 5" + assert max_n <= 4, "max_n must be less than 5" + + self.max_n = max_n + self.max_l = max_l + self.cutoff = cutoff + + # retrieve formulas + self.register_buffer( + "factor", torch.sqrt(torch.tensor(2.0 / (self.cutoff**3))) + ) + self.coef = torch.zeros(4, 9, 4) + self.coef[0, 0, :] = torch.tensor( + [ 3.14159274101257, 6.28318548202515, 9.42477798461914, 12.5663709640503 ] + ) + self.coef[1, :4, :] = torch.tensor( + [ + [ -1.02446483277785, -1.00834335996107, -1.00419641763893, -1.00252381898662 ], + [ 4.49340963363647, 7.7252516746521, 10.9041213989258, 14.0661935806274 ], + [ 0.22799275301076, 0.130525632358311, 0.092093290316619, 0.0712718627992818 ], + [ 4.49340963363647, 7.7252516746521, 10.9041213989258, 14.0661935806274 ], + ] + ) + self.coef[2, :6, :] = torch.tensor( + [ + [ -1.04807944170731, -1.01861796359391, -1.01002272174988, -1.00628955560036 ], + [ 5.76345920562744, 9.09501171112061, 12.322940826416, 15.5146026611328 ], + [ 0.545547077361439, 0.335992298618515, 0.245888396928293, 0.194582402961821 ], + [ 5.76345920562744, 9.09501171112061, 12.322940826416, 15.5146026611328 ], + [ 0.0946561878721665, 0.0369424811413594, 0.0199537107571916, 0.0125418876146463 ], + [ 5.76345920562744, 9.09501171112061, 12.322940826416, 15.5146026611328 ], + ] + ) + self.coef[3, :8, :] = torch.tensor( + [ + [ 1.06942831392075, 1.0292173312802, 1.01650804843248, 1.01069656069999 ], + [ 6.9879322052002, 10.4171180725098, 13.6980228424072, 16.9236221313477 ], + [ 0.918235852195231, 0.592803493701152, 0.445250264272671, 0.358326327374518 ], + [ 6.9879322052002, 10.4171180725098, 13.6980228424072, 16.9236221313477 ], + [ 0.328507713452024, 0.142266673367543, 0.0812617757677838, 0.0529328657590962 ], + [ 6.9879322052002, 10.4171180725098, 13.6980228424072, 16.9236221313477 ], + [ 0.0470107184508114, 0.0136570088173405, 0.0059323726279831, 0.00312775039221944 ], + [ 6.9879322052002, 10.4171180725098, 13.6980228424072, 16.9236221313477 ], + ] + ) + + def forward(self, dist, three_body): + dist = dist / self.cutoff + rbfs = [] + + for j in range(self.max_l): + rbfs.append(torch.sin(self.coef[0, 0, j] * dist) / dist) + + if self.max_n > 1: + for j in range(self.max_l): + rbfs.append( + ( + self.coef[1, 0, j] + * dist + * torch.cos(self.coef[1, 1, j] * dist) + + self.coef[1, 2, j] + * torch.sin(self.coef[1, 3, j] * dist) + ) + / dist**2 + ) + + if self.max_n > 2: + for j in range(self.max_l): + rbfs.append( + ( + self.coef[2, 0, j] + * (dist**2) + * torch.sin(self.coef[2, 1, j] * dist) + - self.coef[2, 2, j] + * dist + * torch.cos(self.coef[2, 3, j] * dist) + + self.coef[2, 4, j] + * torch.sin(self.coef[2, 5, j] * dist) + ) + / (dist**3) + ) + + if self.max_n > 3: + for j in range(self.max_l): + rbfs.append( + ( + self.coef[3, 0, j] + * (dist**3) + * torch.cos(self.coef[3, 1, j] * dist) + - self.coef[3, 2, j] + * (dist**2) + * torch.sin(self.coef[3, 3, j] * dist) + - self.coef[3, 4, j] + * dist + * torch.cos(self.coef[3, 5, j] * dist) + + self.coef[3, 6, j] + * torch.sin(self.coef[3, 7, j] * dist) + ) + / dist**4 + ) + + rbfs = torch.stack(rbfs, dim=-1) + rbfs = rbfs * self.factor + + cbfs = SphericalHarmonics(self.max_l - 1, torch.cos(three_body)) + + cbfs = cbfs.repeat_interleave(self.max_n, dim=1) + + return rbfs * cbfs + + +class SinusoidalTimeExpansion(nn.Module): + """ Encode time """ + + def __init__(self, dim: int = 128): + """ + Args: + dim (int): the embedding size of Time. + """ + super().__init__() + + self.dim = dim + half_dim = self.dim // 2 + # Inverse frequencies for sinusoidal embedding + self.register_buffer( + "inv_freq", + torch.exp(torch.arange(half_dim, dtype=torch.float32) * (-math.log(10000) / (half_dim - 1))) + ) + + def forward(self, feature): + shape = feature.shape + feature = feature.view(-1).to(torch.float32) + sinusoid_in = torch.ger(feature, self.inv_freq) + time_emb = torch.cat([sinusoid_in.sin(), sinusoid_in.cos()], dim=-1) + # Restore original shape with embedding dimension + time_emb = time_emb.view(*shape, self.dim) + + return time_emb \ No newline at end of file diff --git a/models/matris/matris/model/feature_embed.py b/models/matris/matris/model/feature_embed.py new file mode 100644 index 0000000000000000000000000000000000000000..e47f822c22b91c6e00120547698d7c7e75e349f3 --- /dev/null +++ b/models/matris/matris/model/feature_embed.py @@ -0,0 +1,268 @@ +from __future__ import annotations + +import torch +from torch import Tensor, nn + +from .basis_function import ( + FourierExpansion, + BesselExpansion, + GaussianExpansion, + GaussianExpansion, + SphericalExpansion, + SinusoidalTimeExpansion +) +from .functions import get_normalization, SwishLayer, aggregate + +class AtomTypeEmbedding(nn.Module): + """Encode an atom by its atomic number using 'nn.Embedding'.""" + + def __init__(self, atom_feat_dim: int, max_num_elements: int = 94): + """ + Args: + atom_feature_dim (int): dimension of atomic embedding. + max_num_elements (int): maximum number of elements in the dataset. + Default = 94 + """ + super().__init__() + self.embedding = nn.Embedding(max_num_elements, atom_feat_dim) + self.atom_swish_layer = SwishLayer(input_dim = atom_feat_dim, + output_dim = atom_feat_dim, + bias=False) + + def forward(self, atom_type: Tensor) -> Tensor: + """ + Args: + atom_type (Tensor): [nAtoms, 1]. + """ + node_feat = self.embedding(atom_type) + node_feat = self.atom_swish_layer(node_feat) + return node_feat + + +class EdgeBasisEmbedding(nn.Module): + """Embed edge length using 'Bessel' or 'Gaussian'.""" + + def __init__( + self, + pairwise_cutoff: float = 6, + three_body_cutoff: float = 4, + num_radial: int = 7, + edge_feat_dim: int = 128, + envelope_exponent: int = 8, + distance_expansion: str = "Bessel", + learnable: bool = False, + ): + """ + Args: + pairwise_cutoff (float): The cutoff for pairwise interaction. + Default = 6 + three_body_cutoff (float): The cutoff for three-body interaction. + Default = 4 + num_radial (int): The number of radial. + Default = 7 + envelope_exponent (int): envelope exponent of Envelope function. + Default = 8 + distance_expansion (str): The function of basis, "Bessel" or "Gaussian". + Default = "Bessel" + learnable(bool): Whether the frequency in bessel expansion is learnable. + Default = False + """ + super().__init__() + + self.distance_expansion = distance_expansion + if self.distance_expansion.lower() == "bessel": + self.pairwise_rbf_expansion = BesselExpansion( + num_radial=num_radial, + cutoff=pairwise_cutoff, + envelope_exponent=envelope_exponent, + learnable=learnable, + ) + self.threebody_rbf_expansion = BesselExpansion( + num_radial=num_radial, + cutoff=three_body_cutoff, + envelope_exponent=envelope_exponent, + learnable=learnable, + ) + elif self.distance_expansion.lower() == "gaussian": + self.pairwise_rbf_expansion = GaussianExpansion( + start=0.0, + stop=pairwise_cutoff, + num_gaussians=num_radial, + basis_width_scalar=2.0, + ) + self.threebody_rbf_expansion = GaussianExpansion( + start=0.0, + stop=three_body_cutoff, + num_gaussians=num_radial, + basis_width_scalar=2.0, + ) + else: + raise NotImplementedError + + self.edge_linear1 = nn.Linear( + in_features=num_radial, out_features=edge_feat_dim, bias=False + ) + self.edge_linear2 = nn.Linear( + in_features=edge_feat_dim, out_features=edge_feat_dim, bias=False + ) + self.swish_layer = SwishLayer(input_dim = edge_feat_dim, + output_dim = edge_feat_dim, + bias=False) + self.edge_init_norm = get_normalization(name="layer", dim=edge_feat_dim) + + + def forward( + self, + graphs, + ) -> tuple[Tensor, Tensor, Tensor]: + """ + Args: + center_pos (Tensor): coord of center atoms. + neighbor_pos (Tensor): coordinates of neighbor atoms. + undirected2directed (Tensor): mapping from undirected vectors to one of its + directed vectors. Following repo: https://github.com/CederGroupHub/chgnet + image (Tensor): the periodic image specifying the location of neighbor atoms. + lattice (Tensor): the lattice matrix of structure. + """ + + # From directed edge to undirected edge, this motivation is inspired by https://github.com/CederGroupHub/chgnet + unique_edge_lengths = torch.index_select( graphs['edge_lengths'], 0, graphs['undirected2directed'] ) #[nEdges/2] + + pairwise_rbf = self.pairwise_rbf_expansion(unique_edge_lengths) #[nEdges/2, num_radial] + threebody_rbf = self.threebody_rbf_expansion(unique_edge_lengths) #[nEdges/2, num_radial] + + edge_feat_undirect = self.edge_linear1(pairwise_rbf) + # [edges, dim] -> [2*edges, dim] + edge_feat_direct = torch.index_select(edge_feat_undirect, 0, graphs['directed2undirected']) + edge_feat_direct = self.edge_linear2(edge_feat_direct) + edge_feat_direct = self.edge_init_norm(edge_feat_direct) + # Aggregate to bond_feas_ude + edge_feat = aggregate(data=edge_feat_direct, + segment=graphs['directed2undirected'], + bin_count=None, + average=True, + num_segment=None) + edge_feat = self.swish_layer(edge_feat) + smooth_weight={"atom graph": pairwise_rbf, "line graph": threebody_rbf} + #return edge_feat, pairwise_rbf, threebody_rbf + return edge_feat, smooth_weight + + +class ThreebodyEmbedding(nn.Module): + """Embed threebody using 'Fourier' or 'Sphere Harmonic'.""" + + def __init__(self, + num_angular: int = 7, # Fourier + max_n: int=4, max_l: int=4, cutoff: int=6, #SH + three_body_feat_dim: int = 128, + three_body_expansion: str = "fourier", + learnable: bool = True): + super().__init__() + + self.three_body_expansion = three_body_expansion.lower() + if self.three_body_expansion == "fourier": + max_f = (num_angular - 1) // 2 + self.expansion = FourierExpansion( + max_f=max_f, learnable=learnable + ) + self.angle_embedding = nn.Linear( + in_features=num_angular, out_features=three_body_feat_dim, bias=False + ) + elif self.three_body_expansion == "sh": + self.expansion = SphericalExpansion(max_n=max_n, max_l=max_l, cutoff=cutoff) + self.angle_embedding = nn.Linear( + in_features=max_n * max_l, out_features=three_body_feat_dim, bias=False + ) + else: + raise NotImplementedError + + self.swish_layer = SwishLayer(input_dim = three_body_feat_dim, + output_dim = three_body_feat_dim, + bias=False) + + def forward(self, graphs): + edge_vecs_ij = torch.index_select( + graphs['unit_edge_vectors'], 0, graphs['line_graph_dict']['target_DE_index'] + ) # normalized edge vector ij [nAngle, 3] + edge_vecs_jk = torch.index_select( + graphs['unit_edge_vectors'], 0, graphs['line_graph_dict']['source_DE_index'] + ) # normalized edge vector jk [nAngle, 3] + + theta_ijk = torch.sum(edge_vecs_ij * edge_vecs_jk, dim=1) * (1 - 1e-6) + angle = torch.acos(theta_ijk) + + if self.three_body_expansion == "sh": + edge_vecs_jk_len = torch.norm(edge_vecs_jk, dim=1) + three_body_basis = self.expansion(edge_vecs_jk_len, angle) + else: + three_body_basis = self.expansion(angle) + + threebody_feat = self.angle_embedding(three_body_basis) + threebody_feat = self.swish_layer(threebody_feat) + + return threebody_feat + + +class ThreebodyFourierExpansion(nn.Module): + """Encode an three-body terms using Fourier Expansion.""" + + def __init__(self, num_angular: int = 7, learnable: bool = True): + super().__init__() + + assert num_angular % 2 == 1, f"{num_angular=} must be an odd integer" + max_f = (num_angular - 1) // 2 + self.fourier_expansion = FourierExpansion( + max_f=max_f, learnable=learnable + ) + + def forward(self, edge_vec_ij: Tensor, edge_vec_jk: Tensor) -> Tensor: + """ + Args: + edge_vec_i (Tensor): normalized edge vector ij [n_angle, 3] + edge_vec_j (Tensor): normalized edge vector jk [n_angle, 3] + """ + theta_ijk = torch.sum(edge_vec_ij * edge_vec_jk, dim=1) * (1 - 1e-6) + angle = torch.acos(theta_ijk) + return self.fourier_expansion(angle) + + +class three_bodySHExpansion(nn.Module): + """Encode an three-body terms using Sphere Harmonic Expansion.""" + + def __init__(self, max_n: int=4, max_l: int=4, cutoff: int=6) -> None: + super().__init__() + self.sbf = SphericalExpansion(max_n=max_n, max_l=max_l, cutoff=cutoff) + + def forward(self, edge_vec_ij: Tensor, edge_vec_jk: Tensor) -> Tensor: + """ + Args: + edge_vec_i (Tensor): normalized edge vector ij [n_angle, 3] + edge_vec_j (Tensor): normalized edge vector jk [n_angle, 3] + """ + theta_ijk = torch.sum(edge_vec_ij * edge_vec_jk, dim=1) * (1 - 1e-6) + angle = torch.acos(theta_ijk) + + edge_vec_jk_len = torch.norm(edge_vec_jk, dim=1) + angle_basis = self.sbf(edge_vec_jk_len, angle) + return angle_basis + + +class TimeEmbedding(nn.Module): + """Encode time using nonlinear""" + + def __init__(self, time_feat_dim): + super().__init__() + self.time_encode = SinusoidalTimeExpansion(dim = time_feat_dim) + self.linear1 = nn.Linear( + in_features = time_feat_dim, out_features = 2 * time_feat_dim + ) + self.act = nn.SiLU() + self.linear2 = nn.Linear( + in_features = time_feat_dim * 2, out_features = time_feat_dim + ) + + def forward(self, time): + time_enc= self.time_encode(time) + time_enc = self.act(self.linear1(time_enc)) + time_emb = self.linear2(time_enc) + return time_emb \ No newline at end of file diff --git a/models/matris/matris/model/functions.py b/models/matris/matris/model/functions.py new file mode 100644 index 0000000000000000000000000000000000000000..d2d1caba3bfb8c3029ef6ac29b0642c04bb94d65 --- /dev/null +++ b/models/matris/matris/model/functions.py @@ -0,0 +1,466 @@ +from __future__ import annotations + +from collections.abc import Sequence + +import torch +from torch import Tensor, nn +import math +from .op import fused_silu, fused_sigmoid + +class FusedSiLU(torch.nn.Module): + """Fused Sigmoid Linear Unit.""" + + def __init__(self) -> None: + """Initialize a fused SiLU.""" + super().__init__() + + def forward(self, x: Tensor) -> Tensor: + """Forward pass.""" + if x.device.type == "cuda": + return fused_silu(x) + else: + return torch.nn.functional.silu(x) + +class FusedSigmoid(torch.nn.Module): + """Fused Sigmoid Linear Unit.""" + + def __init__(self) -> None: + """Initialize a fused SiLU.""" + super().__init__() + + def forward(self, x: Tensor) -> Tensor: + """Forward pass.""" + if x.device.type == "cuda": + return fused_sigmoid(x) + else: + return torch.nn.functional.sigmoid(x) + +def get_activation(name: str) -> nn.Module: + """Return an activation function""" + activation_map = { + "relu": nn.ReLU, + "silu": FusedSiLU, # Using fused version for better performance + "gelu": nn.GELU, + "softplus": nn.Softplus, + "sigmoid": FusedSigmoid, # Using fused version for better performance + "tanh": nn.Tanh, + } + + name_lower = name.lower() + if name_lower not in activation_map: + raise NotImplementedError( + f"Activation '{name}' is not implemented. " + f"Supported activations: {list(activation_map.keys())}" + ) + return activation_map[name_lower]() + +def get_normalization(name: str, dim: int | None = None) -> nn.Module | None: + """Return an normalization function""" + if name is None: + return None + + normalization_map = { + "layer": nn.LayerNorm(dim), + "rms": nn.RMSNorm(dim), # torch >= 2.6.0 + "batch": nn.BatchNorm1d(dim), + } + name_lower = name.lower() + return normalization_map[name_lower] + +class SwishLayer(nn.Module): + def __init__( + self, + input_dim: int = 128, + output_dim: int = 128, + bias: bool = True, + ) -> None: + """ + Args: + input_dim: Input dimension. + output_dim: Output dimension. + bias: Whether to use bias in the linear layer. Default: True. + """ + super().__init__() + self.linear = nn.Linear(input_dim, output_dim, bias=bias) + self.act = get_activation("silu") + + def forward(self, feas: Tensor) -> Tensor: + """ + Args: + feas: shape (feas_num, in_dim) + + Returns: + output: shape (feas_num, out_dim) + """ + return self.act(self.linear(feas)) + +def Dimwise_softmax(feas: Tensor, segment: Tensor, num_segment=None) -> Tensor: + """Computes a sparsely evaluated softmax. + + Args: + feas: The source tensor. shape: [num, dim] + segment: specify the segment of each row [num, 1] + """ + num, dim = feas.shape + if num_segment is None: + num_segment = int(segment.max()) + 1 + + segment_expanded = segment.unsqueeze(1).expand(-1, dim) # [num, dim] + + feas_max = torch.empty( num_segment, dim, dtype=feas.dtype, device=feas.device ) + feas_max.fill_(float("-inf")) + feas_max = feas_max.scatter_reduce( + 0, segment_expanded, feas, reduce='amax', include_self=False, + ) #[num_segment, dim] + # Gather: [num_segment, dim] -> [num, dim] + feas_max = feas_max[segment] + out = (feas - feas_max).exp() + + # =========== scatter sum ============ + out_sum = torch.zeros(num_segment, dim, device=feas.device, dtype=feas.dtype) + out_sum = out_sum.scatter_reduce( + 0, segment_expanded, out, reduce='sum', include_self=False + ) + # Gather: [num_segment, dim] -> [num, dim] + out_sum = out_sum[segment] + score = out / out_sum + return score + +def aggregate(data: torch.Tensor, + segment: torch.Tensor, + bin_count: torch.Tensor = None, + average=True, + num_segment=None) -> torch.Tensor: + """Aggregate rows in data by specifying the segment. + + Args: + data (Tensor): data tensor to aggregate [n_row, feature_dim] + segment (Tensor): specify the owner of each row [n_row, 1] + average (bool): if True, average the rows, if False, sum the rows. + Default = True + num_owner (int, optional): the number of owners, this is needed if the + max idx of owner is not presented in owners tensor + Default = None + + Returns: + output (Tensor): [num_owner, feature_dim] + """ + if bin_count is None: + bin_count = torch.bincount(segment) + bin_count = bin_count.where(bin_count != 0, bin_count.new_ones(1)) + + if (num_segment is not None) and (bin_count.shape[0] != num_segment): + difference = num_segment - bin_count.shape[0] + bin_count = torch.cat([bin_count, bin_count.new_ones(difference)]) + # make sure this operation is done on the same device of data and owners + output = data.new_zeros([bin_count.shape[0], data.shape[1]]) + output = output.index_add_(0, segment, data) + if average: + output = (output.T / bin_count).T + return output + + +class MLP(nn.Module): + + def __init__( + self, + input_dim: int = 128, + hidden_dim: int | Sequence[int] | None = (128, 128), + output_dim: int = 128, + dropout: float = 0.0, + activation: Literal["silu", "relu", "tanh", "gelu"] = "silu", + bias: bool = True, + use_fp16: bool = False, + ): + """Initialize the MLP layer. + Args: + input_dim: Dimension of input features. + hidden_dim: Number of hidden units. Can be an integer for a single + hidden layer, a sequence of integers for multiple hidden layers, + or None for no hidden layers. Default: (128, 128). + output_dim: Dimension of output predictions. Default: 128. + dropout: Dropout rate applied before each linear layer. Default: 0.0. + activation: Activation function. Supported: "relu", "silu", "tanh", "gelu". + bias: Whether to use bias in linear layers. Default: True. + use_fp16: Whether to use mixed precision (FP16). Default: False. + """ + super().__init__() + if not 0.0 <= dropout < 1.0: + raise ValueError(f"Dropout rate must be in [0.0, 1.0), got {dropout}") + + self.use_fp16 = use_fp16 + activation_func = get_activation(activation) + + layers = [] + if hidden_dim in (None, 0): + layers.append(nn.Dropout(dropout)) + layers.append(nn.Linear(input_dim, output_dim, bias=bias)) + elif isinstance(hidden_dim, int): + # Single hidden layer + layers.extend([ + nn.Linear(input_dim, hidden_dim, bias=bias), + activation_func, + nn.Dropout(dropout), + nn.Linear(hidden_dim, output_dim, bias=bias), + ]) + elif isinstance(hidden_dim, Sequence): + # Multiple hidden layers + layers.extend([ + nn.Linear(input_dim, hidden_dim[0], bias=bias), + activation_func, + ]) + # Additional hidden layers + for i in range(len(hidden_dim) - 1): + layers.extend([ + nn.Dropout(dropout), + nn.Linear(hidden_dim[i], hidden_dim[i + 1], bias=bias), + activation_func, + ]) + # Output layer + layers.extend([nn.Dropout(dropout), nn.Linear(hidden_dim[-1], output_dim, bias=bias)]) + else: + raise TypeError( + f"hidden_dim must be an integer, sequence of integers, or None, " + f"got {type(hidden_dim).__name__}" + ) + + self.layers = nn.Sequential(*layers) + + def forward(self, feas: Tensor) -> Tensor: + """ + Args: + feas: Input tensor of shape (features, input_dim) + Returns: + Output tensor of shape (features, output_dim) + """ + if self.use_fp16 and feas.is_cuda: + with torch.amp.autocast(dtype=torch.float16, device_type="cuda"): + out = self.layers(feas) + out = out.to(torch.float32) + else: + out = self.layers(feas) + return out + + +class GatedMLP(nn.Module): + + def __init__( + self, + input_dim: int = 128, + hidden_dim: int | Sequence[int] | None = (128, 128), + output_dim: int = 128, + dropout: float = 0.0, + activation: str = "silu", + norm_type: str = "layer", + bias: bool = True, + use_fp16: bool = False, + ) -> None: + """ + Args: + input_dim: The input dimension. + hidden_dim: A list of integers or a single integer representing the number + of hidden units in each layer of the MLP. Default: None. + output_dim: The output dimension. + dropout: The dropout rate. Default: 0.0. + activation: The name of the activation function. Must be one of "relu", + "silu", "tanh", or "gelu". Default: "silu". + norm_type: The name of the normalization layer to use. Must be one of + "layer", "rms", "batch", "group", or None. Default: "layer". + bias: Whether to use bias in linear layers. Default: True. + use_fp16: Whether to use mixed precision (FP16). Default: False. + """ + super().__init__() + self.use_fp16 = use_fp16 + self.activation_func = get_activation(activation) + self.activation_gate = get_activation("sigmoid") + self.gate_norm = get_normalization(name=norm_type, dim=output_dim) + self.core_norm = get_normalization(name=norm_type, dim=output_dim) + self.mlp_core = MLP( + input_dim=input_dim, + hidden_dim=hidden_dim, + output_dim=output_dim, + dropout=dropout, + activation=activation, + bias=bias, + use_fp16=use_fp16, + ) + self.mlp_gate = MLP( + input_dim=input_dim, + hidden_dim=hidden_dim, + output_dim=output_dim, + dropout=dropout, + activation=activation, + bias=bias, + use_fp16=use_fp16, + ) + + def forward(self, feas: Tensor) -> Tensor: + """ + Args: + feas (Tensor): shape (feas_num, input_dim) + Returns: + output: shape (feas_num, output_dim) + """ + if self.gate_norm is None: + core = self.activation_func(self.mlp_core(feas)) + gate = self.activation_gate(self.mlp_gate(feas)) + else: + core = self.activation_func(self.core_norm(self.mlp_core(feas))) + gate = self.activation_gate(self.gate_norm(self.mlp_gate(feas))) + out = core * gate # gate mul + return out + + +class MOE_Layer(nn.Module): + + def __init__( + self, + num_expert: int = 64, + input_dim: int = 128, + hidden_dim: int | Sequence[int] | None = (128, 128), + output_dim: int = 128, + dropout: float = 0.0, + activation: Literal["silu", "relu", "tanh", "gelu"] = "silu", + bias: bool = True, + use_fp16: bool = False, + ): + """Initialize the MOE layer. + + Args: + + """ + super().__init__() + + raise NotImplementedError + + def forward(self, feas: Tensor) -> Tensor: + return None + + +class GraphPooling(nn.Module): + def __init__(self, average: bool = False) -> None: + + super().__init__() + self.average = average + + def forward(self, node_feat: Tensor, segment: Tensor) -> Tensor: + """ + Args: + atom_feat (Tensor): batched atom features after convolution layers. + [num_batch_atoms, node_feat_dim or 1] + segment (Tensor): graph indices for each atom. + [num_batch_atoms] + + Returns: + crystal_feas (Tensor): crystal feature matrix. + [n_crystals, node_feat_dim or 1] + """ + bin_count = torch.bincount(segment) + bin_count = bin_count.where(bin_count != 0, bin_count.new_ones(1)) + + output = node_feat.new_zeros([bin_count.shape[0], node_feat.shape[1]]) + output = output.index_add_(0, segment, node_feat) + if self.average: + output = (output.T / bin_count).T + return output + + +def cg_change_mat(ang_mom: int, device: str = "cpu") -> torch.tensor: + if ang_mom not in [2]: + raise NotImplementedError + + if ang_mom == 2: + change_mat = torch.tensor( + [ + [3 ** (-0.5), 0, 0, 0, 3 ** (-0.5), 0, 0, 0, 3 ** (-0.5)], + [0, 0, 0, 0, 0, 2 ** (-0.5), 0, -(2 ** (-0.5)), 0], + [0, 0, -(2 ** (-0.5)), 0, 0, 0, 2 ** (-0.5), 0, 0], + [0, 2 ** (-0.5), 0, -(2 ** (-0.5)), 0, 0, 0, 0, 0], + [0, 0, 0.5**0.5, 0, 0, 0, 0.5**0.5, 0, 0], + [0, 2 ** (-0.5), 0, 2 ** (-0.5), 0, 0, 0, 0, 0], + [ + -(6 ** (-0.5)), + 0, + 0, + 0, + 2 * 6 ** (-0.5), + 0, + 0, + 0, + -(6 ** (-0.5)), + ], + [0, 0, 0, 0, 0, 2 ** (-0.5), 0, 2 ** (-0.5), 0], + [-(2 ** (-0.5)), 0, 0, 0, 0, 0, 0, 0, 2 ** (-0.5)], + ], + device=device, + ).detach() + + return change_mat + + +def irreps_sum(ang_mom: int) -> int: + """ + Returns the sum of the dimensions of the irreps up to the specified angular momentum. + + :param ang_mom: max angular momenttum to sum up dimensions of irreps + """ + total = 0 + for i in range(ang_mom + 1): + total += 2 * i + 1 + + return total + + +def reshape_stress(L0out, L2out, batch_size=1): + _max_rank = 2 + pred_irreps = torch.zeros( + (batch_size, irreps_sum(_max_rank)), + device = L0out.device, + ) + # L=0 + L=0 + pred_irreps[: ,irreps_sum(L-1): irreps_sum(L)] = L0out.view(batch_size, -1) + + L=2 + pred_irreps[: ,irreps_sum(L-1): irreps_sum(L)] = L2out.view(batch_size, -1) + + pred = torch.einsum( + "ba, cb->ca", + cg_change_mat(_max_rank, device = L0out.device), + pred_irreps, + ) + + return pred.view(batch_size, 3,3) + + +class Sphere(nn.Module): + + def __init__(self, lmax=2): + super(Sphere, self).__init__() + self.lmax = lmax + + def forward(self, edge_vec): + edge_sh = self._spherical_harmonics(self.lmax, edge_vec[..., 0], edge_vec[..., 1], edge_vec[..., 2]) + return edge_sh + + @staticmethod + def _spherical_harmonics(lmax: int, x: Tensor, y: Tensor, z: Tensor) -> Tensor: + sh_0_0 = torch.ones_like(x) + if lmax == 0: + return torch.stack([ sh_0_0, ], dim=-1) + + sh_1_0, sh_1_1, sh_1_2 = x, y, z + + if lmax == 1: + return torch.stack([sh_0_0, sh_1_0, sh_1_1, sh_1_2], dim=-1) + + sh_2_0 = math.sqrt(3.0) * x * z + sh_2_1 = math.sqrt(3.0) * x * y + y2 = y.pow(2) + x2z2 = x.pow(2) + z.pow(2) + sh_2_2 = y2 - 0.5 * x2z2 + sh_2_3 = math.sqrt(3.0) * y * z + sh_2_4 = math.sqrt(3.0) / 2.0 * (z.pow(2) - x.pow(2)) + + if lmax == 2: + return torch.stack([sh_0_0, sh_1_0, sh_1_1, sh_1_2, sh_2_0, sh_2_1, sh_2_2, sh_2_3, sh_2_4], dim=-1) diff --git a/models/matris/matris/model/interaction_block.py b/models/matris/matris/model/interaction_block.py new file mode 100644 index 0000000000000000000000000000000000000000..de1e20e4463cdccf44e4966d09c8088ba1878ff8 --- /dev/null +++ b/models/matris/matris/model/interaction_block.py @@ -0,0 +1,507 @@ +from __future__ import annotations + +import torch +from torch import Tensor, nn +from typing import Any, Dict +from .functions import ( + MLP, + GatedMLP, + aggregate, + get_normalization, + Dimwise_softmax, +) +from torch.utils.checkpoint import checkpoint + +THRESHOLD_VALUE = 60000 # Safe value for MatRIS-10M (A100-80GB) + +class Graph_Attention_Layer(nn.Module): + + def __init__( + self, + node_feat_dim: int = 128, + edge_feat_dim: int = 128, + hidden_dim: int = 128, + use_bias: bool = False, + dropout: float = 0.0, + mlp_type: str = "GateMLP", # MLP, GateMLP + activation_type: str = "silu", + norm_type: str = "layer", + use_fp16: bool = False, + ): + super().__init__() + + self.source_weight_linear = nn.Linear( + in_features = edge_feat_dim, out_features = edge_feat_dim, bias = False + ) + self.target_weight_linear = nn.Linear( + in_features = edge_feat_dim, out_features = edge_feat_dim, bias = False + ) + if mlp_type.lower() == "mlp": + self.node_nonlinear_update = nn.Sequential( + MLP( + input_dim=edge_feat_dim * 2 + node_feat_dim, + hidden_dim=hidden_dim, + output_dim=node_feat_dim, + dropout=dropout, + bias=use_bias, + activation=activation_type, + ), + get_normalization(name=norm_type, dim=node_feat_dim) + ) + self.edge_nonlinear_update = nn.Sequential( + MLP( + input_dim=node_feat_dim * 2 + edge_feat_dim, + hidden_dim=hidden_dim, + output_dim=edge_feat_dim, + dropout=dropout, + bias=use_bias, + activation=activation_type, + use_fp16=use_fp16, + ), + get_normalization(name=norm_type, dim=edge_feat_dim) + ) + elif mlp_type.lower() == "gatemlp": + self.node_nonlinear_update = GatedMLP( + input_dim=edge_feat_dim * 2 + node_feat_dim, + hidden_dim=hidden_dim, + output_dim=node_feat_dim, + norm_type=norm_type, + dropout=dropout, + activation=activation_type, + ) + self.edge_nonlinear_update = GatedMLP( + input_dim=node_feat_dim * 2 + edge_feat_dim, + hidden_dim=hidden_dim, + output_dim=edge_feat_dim, + norm_type=norm_type, + dropout=dropout, + activation=activation_type, + use_fp16=use_fp16, + ) + else: + raise NotImplementedError + + self.node_res_weight = torch.nn.Parameter(torch.ones(1, node_feat_dim), requires_grad=True) + self.edge_res_weight = torch.nn.Parameter(torch.ones(1, edge_feat_dim), requires_grad=True) + + def forward(self, + node_feat: Tensor, + edge_feat: Tensor, + graph: Dict, # atom graph or line graph + directed2undirected: Tensor = None, + ): + source_node_index = graph['source_index'] + target_node_index = graph['target_index'] + # gather + source_node_feat = torch.index_select(node_feat, 0, source_node_index) + target_node_feat = torch.index_select(node_feat, 0, target_node_index) + if directed2undirected is not None: + # Atom Graph Update + edge_feat_0 = torch.index_select(edge_feat, 0, directed2undirected) # [edge, dim] -> [2*edge, dim] + else: + # Line Graph Update + edge_feat_0 = edge_feat + + #======= combine feature ======= + attn_edge_feat = torch.cat([edge_feat_0, target_node_feat, source_node_feat], dim=1) + attn_edge_feat = self.edge_nonlinear_update(attn_edge_feat) + + # ======= update atom feature ======= + source_alpha_0 = self.source_weight_linear(edge_feat_0) + target_alpha_0 = self.target_weight_linear(edge_feat_0) + + # Softmax + num_segment = None #torch.unique(source_node_index).numel() + source_alpha = Dimwise_softmax(source_alpha_0, source_node_index, num_segment) + target_alpha = Dimwise_softmax(target_alpha_0, target_node_index, num_segment) + + source_weight = source_alpha * attn_edge_feat # refer to sa_{ij} * e'_{ij} in MatRIS paper + target_weight = target_alpha * attn_edge_feat # refer to ta_{ij} * e'_{ij} in MatRIS paper + + if directed2undirected is not None: + attn_edge_feat = aggregate(data=attn_edge_feat, segment=directed2undirected, bin_count=None, average=True, num_segment=None) #[2*edge, dim] -> [edge, dim] + # Compute Attention output + attn_source_feat = aggregate(data=source_weight, + segment=source_node_index, + bin_count=graph['source_bincount'],#bincount_source, + average=False, + num_segment=len(node_feat)) + + attn_target_feat = aggregate(data=target_weight, + segment=target_node_index, + bin_count=graph['target_bincount'],#bincount_target, + average=False, + num_segment=len(node_feat)) + + fusion_node_feat = torch.cat([node_feat, attn_target_feat, attn_source_feat], dim=1) + attn_node_feat = self.node_nonlinear_update(fusion_node_feat) + + # Resdual + attn_node_feat = attn_node_feat + self.node_res_weight * node_feat + attn_edge_feat = attn_edge_feat + self.edge_res_weight * edge_feat + + return attn_node_feat, attn_edge_feat + + +class Refinement(nn.Module): + + def __init__( + self, + node_feat_dim: int = 128, + edge_feat_dim: int = 128, + hidden_dim: int = 128, + num_basis: int = 7, + dropout: float = 0.0, + mlp_type: str = "GateMLP", + activation_type: str = "silu", + norm_type: str = "layer", + use_bias: bool = False, + graph_type: Literal["atom graph", "line graph"] = "atom graph", + atom_feat_dim: int = 128, + use_smoothed_for_delta_edge: bool = False, + use_fp16: bool = False, + ): + super().__init__() + self.graph_type = graph_type + self.use_smoothed_for_delta_edge = use_smoothed_for_delta_edge + + if graph_type == "atom graph": + input_dim = 2 * node_feat_dim + edge_feat_dim + else: + input_dim = atom_feat_dim + 2 * node_feat_dim + edge_feat_dim + if mlp_type.lower() == "mlp": + self.edge_nonlinear_update = nn.Sequential( + MLP( + input_dim=input_dim, + hidden_dim=hidden_dim, + output_dim=edge_feat_dim, + dropout=dropout, + bias=use_bias, + activation=activation_type, + use_fp16=use_fp16, + ), + get_normalization(name=norm_type, dim=edge_feat_dim) + ) + elif mlp_type.lower() == "gatemlp": + self.edge_nonlinear_update = GatedMLP( + input_dim=input_dim, + hidden_dim=hidden_dim, + output_dim=edge_feat_dim, + dropout=dropout, + norm_type=norm_type, + activation=activation_type, + use_fp16=use_fp16, + ) + else: + raise NotImplementedError + + self.node_FFN = MLP( + input_dim=edge_feat_dim, + hidden_dim=node_feat_dim, + output_dim=node_feat_dim, + bias=use_bias, + ) + self.edge_FFN = MLP( + input_dim=edge_feat_dim, + hidden_dim=edge_feat_dim, + output_dim=edge_feat_dim, + bias=use_bias, + use_fp16=use_fp16, + ) + self.learnable_envelope = nn.Linear( + in_features = num_basis, out_features = edge_feat_dim, bias = False + ) + + self.node_res_weight = torch.nn.Parameter(torch.ones(1, node_feat_dim), requires_grad=True) + self.edge_res_weight = torch.nn.Parameter(torch.ones(1, edge_feat_dim), requires_grad=True) + + def forward( + self, + node_feat: Tensor, + edge_feat: Tensor, + smooth_weight: Tensor, + graph: Dict, + directed2undirected: Tensor = None, + atom_feat: Tensor = None, # Line graph + ) -> Tensor: + # Gather + # when graph=="line graph", make sure atom_deat is not None. + is_atom_graph = (self.graph_type == "atom graph") + + if is_atom_graph: + edge_feat_0 = torch.index_select(edge_feat, 0, directed2undirected) + else: + edge_feat_0 = edge_feat + + source_node_feat = torch.index_select(node_feat, 0, graph['source_index']) + target_node_feat = torch.index_select(node_feat, 0, graph['target_index']) + # Envelope + if is_atom_graph: + smooth_weight = torch.index_select(smooth_weight, 0, directed2undirected) + smooth_weight = self.learnable_envelope(smooth_weight) + # Fusion feature + refine_fusion_feat = torch.cat([edge_feat_0, target_node_feat, source_node_feat], dim=1) + else: + base_envelope = self.learnable_envelope(smooth_weight) + base_weights_i = torch.index_select(base_envelope, 0, graph['source_index']) + base_weights_j = torch.index_select(base_envelope, 0, graph['target_index']) + smooth_weight = base_weights_i * base_weights_j + # Fusion feature + three_body_atom_feat = torch.index_select(atom_feat, 0, graph['atom_list']) + refine_fusion_feat = torch.cat([edge_feat_0, three_body_atom_feat, target_node_feat, source_node_feat], dim=1) + + # Nonlinear + refine_fusion_feat_nonlinear = self.edge_nonlinear_update(refine_fusion_feat) + refine_fusion_feat_smooth = refine_fusion_feat_nonlinear * smooth_weight + + refine_node_feas = aggregate(refine_fusion_feat_smooth, + graph['target_index'], + graph['target_bincount'], + average=False, + num_segment=len(node_feat)) + + input2edgeFFN = ( + refine_fusion_feat_smooth + if is_atom_graph and self.use_smoothed_for_delta_edge + else refine_fusion_feat_nonlinear + ) + + delta_node_feat = self.node_FFN(refine_node_feas) + delta_edge_feat = self.edge_FFN(input2edgeFFN) + + if is_atom_graph: + delta_edge_feat = aggregate(data=delta_edge_feat, segment=directed2undirected, bin_count=None, average=True, num_segment=None) # [2*edge, dim] -> [edge, dim] + + update_node_feat = delta_node_feat + self.node_res_weight * node_feat + update_edge_feat = delta_edge_feat + self.edge_res_weight * edge_feat + + return update_node_feat, update_edge_feat + + +class Interaction_Block(nn.Module): + """ + Interaction Block for MatRIS that processes both atom graphs and line graphs. + + This block performs attention-based message passing and refinement on two hierarchical graph structures: + 1. Atom graph: Nodes represent atoms, edges represent bonds + 2. Line graph: Nodes represent bonds, edges represent three-body interactions (angles) + + Attributes: + attn_block_atom_graph (Graph_Attention_Layer): Attention layer for atom graph + attn_block_line_graph (Graph_Attention_Layer): Attention layer for line graph + refine_block_atom_graph (Refinement): Refinement layer for atom graph + refine_block_line_graph (Refinement): Refinement layer for line graph + """ + + def __init__(self, + node_feat_dim: int = 128, + edge_feat_dim: int = 128, + three_body_feat_dim: int = 128, + num_radial: int = 7, + num_angular: int = 7, + dropout: float = 0.0, + use_bias: bool = False, + use_smoothed_for_delta_edge: bool = False, + mlp_type: str = "GateMLP", + norm_type: str = "layer", + activation_type: str = "silu", + ): + """ + Initialize the Interaction Block. + + Args: + node_feat_dim (int): Dimension of node features (atom features) + edge_feat_dim (int): Dimension of edge features (bond features) + three_body_feat_dim (int): Dimension of three-body features (angle features) + mlp_type (str): Type of MLP to use in the layers + norm_type (str): Type of normalization to apply + activation_type (str): Type of activation function to use + """ + super().__init__() + + self.attn_block_atom_graph = Graph_Attention_Layer( + node_feat_dim=node_feat_dim, + edge_feat_dim=edge_feat_dim, + hidden_dim=node_feat_dim, + use_bias=use_bias, + mlp_type=mlp_type, + norm_type=norm_type, + activation_type=activation_type, + ) + + self.attn_block_line_graph = Graph_Attention_Layer( + node_feat_dim=edge_feat_dim, + edge_feat_dim=three_body_feat_dim, + hidden_dim=edge_feat_dim, + use_bias=use_bias, + mlp_type=mlp_type, + norm_type=norm_type, + activation_type=activation_type, + use_fp16=False, + ) + + self.refine_block_atom_graph = Refinement( + node_feat_dim=node_feat_dim, + edge_feat_dim=edge_feat_dim, + hidden_dim=node_feat_dim, + num_basis=num_radial, + dropout=dropout, + activation_type=activation_type, + norm_type=norm_type, + use_bias=use_bias, + mlp_type=mlp_type, + graph_type="atom graph", + use_smoothed_for_delta_edge=use_smoothed_for_delta_edge, + ) + + self.refine_block_line_graph = Refinement( + node_feat_dim=edge_feat_dim, + edge_feat_dim=three_body_feat_dim, + hidden_dim=edge_feat_dim, + num_basis=num_angular, + dropout=dropout, + activation_type=activation_type, + norm_type=norm_type, + use_bias=use_bias, + mlp_type=mlp_type, + graph_type="line graph", + atom_feat_dim=node_feat_dim, + use_fp16=False, + ) + + def forward( + self, + batch_graph: Dict, + node_feat: Tensor, + edge_feat: Tensor, + threebody_feat: Tensor | None, + smooth_weight: Tensor, + ) -> Tuple[Tensor, Tensor, Tensor]: + """Forward pass of the Interaction Block. + + Args: + batch_graph: Graph object containing: + - atom_graph_dict: Atom graph structure + - line_graph_dict: Bond graph (line graph) structure + - directed2undirected: Mapping from directed to undirected edges + - bond_bases_bg: Smooth weights for bond graph + - bond_bases_ag: Smooth weights for atom graph + node_feat (Tensor): Node features [num_atoms, node_feat_dim] + edge_feat (Tensor): Edge features [num_bonds, edge_feat_dim] + threebody_feat (Tensor): Three-body features [num_angles, three_body_feat_dim] or None + bincount_atom_graph (Dict): Bincount information for atom graph + bincount_line_graph (Dict): Bincount information for line graph + """ + # Initialize variables to handle both cases (with and without threebody features) + attn_edge_feat = edge_feat + attn_threebody_feat = threebody_feat + update_edge_feat = edge_feat + update_threebody_feat = threebody_feat + use_checkpoint = ( + isinstance(threebody_feat, torch.Tensor) + and threebody_feat.shape[0] > THRESHOLD_VALUE + ) + + # Process line graph (bond graph) with attention if threebody features exist + if threebody_feat is not None: + attn_edge_feat, attn_threebody_feat = self.wrapper_attn_layer( + attn_layer=self.attn_block_line_graph, + node_feat=edge_feat, + edge_feat=threebody_feat, + graph=batch_graph['line_graph_dict'], + use_checkpoint=use_checkpoint, + ) + + # Process atom graph with attention + attn_node_feat, attn_edge_feat = self.wrapper_attn_layer( + attn_layer=self.attn_block_atom_graph, + node_feat=node_feat, + edge_feat=attn_edge_feat, + graph=batch_graph['atom_graph_dict'], + directed2undirected=batch_graph['directed2undirected'], + ) + + # Refine line graph features if threebody features exist + if threebody_feat is not None: + update_edge_feat, update_threebody_feat = self.wrapper_refine_layer( + refine_layer=self.refine_block_line_graph, + node_feat=attn_edge_feat, + edge_feat=attn_threebody_feat, + smooth_weight=smooth_weight['line graph'], + graph=batch_graph['line_graph_dict'], + atom_feat=attn_node_feat, + use_checkpoint=use_checkpoint, + ) + + # Refine atom graph features + update_node_feat, update_edge_feat = self.wrapper_refine_layer( + refine_layer=self.refine_block_atom_graph, + node_feat=attn_node_feat, + edge_feat=update_edge_feat, + smooth_weight=smooth_weight['atom graph'], + graph=batch_graph['atom_graph_dict'], + directed2undirected=batch_graph['directed2undirected'], + ) + + return update_node_feat, update_edge_feat, update_threebody_feat + + def wrapper_attn_layer(self, + attn_layer: nn.Module, + node_feat: Tensor, + edge_feat: Tensor, + graph: Dict, + directed2undirected: Tensor = None, + use_checkpoint: bool = False, + ): + if use_checkpoint: + attn_node_feat, attn_edge_feat = checkpoint( + attn_layer, + node_feat, + edge_feat, + graph, + directed2undirected, + use_reentrant=False, + ) + else: + attn_node_feat, attn_edge_feat = attn_layer( + node_feat=node_feat, + edge_feat=edge_feat, + graph=graph, + directed2undirected=directed2undirected, + ) + + return attn_node_feat, attn_edge_feat + + def wrapper_refine_layer(self, + refine_layer: nn.Module, + node_feat: Tensor, + edge_feat: Tensor, + smooth_weight: Tensor, + graph: Dict, + directed2undirected: Tensor = None, + atom_feat: Tensor = None, + use_checkpoint: bool = False, + ): + if use_checkpoint: + update_node_feat, update_edge_feat = checkpoint( + refine_layer, + node_feat, + edge_feat, + smooth_weight, + graph, + directed2undirected, + atom_feat, + use_reentrant=False, + ) + else: + update_node_feat, update_edge_feat = refine_layer( + node_feat=node_feat, + edge_feat=edge_feat, + smooth_weight=smooth_weight, + graph=graph, + directed2undirected=directed2undirected, + atom_feat=atom_feat, + ) + return update_node_feat, update_edge_feat + + \ No newline at end of file diff --git a/models/matris/matris/model/model.py b/models/matris/matris/model/model.py new file mode 100644 index 0000000000000000000000000000000000000000..8794e67b8b7fd80b33688ec41092d06eccb97132 --- /dev/null +++ b/models/matris/matris/model/model.py @@ -0,0 +1,321 @@ +from typing import Literal, Union, Sequence + +import torch +from torch import Tensor, nn +import os +from collections.abc import Sequence + +from ..graph import RadiusGraph, GraphConverter, datatype +from .reference_energy import AtomRef +from .processgraph import process_graphs +from .feature_embed import ( + ThreebodyFourierExpansion, + AtomTypeEmbedding, + EdgeBasisEmbedding, + ThreebodyEmbedding +) +from .functions import ( + MLP, + GatedMLP, + get_normalization +) +from .interaction_block import Interaction_Block +from .readout import ( + EnergyHead, + MagmomHead, + ForceStressHead, +) + + +class MatRIS(nn.Module): + """ Init MatRIS Potential """ + + def __init__( + self, + num_layers: int = 6, # NOTE orig,debug = 6,2 + node_feat_dim: int = 128, # NOTE orig,debug = 128,32 + edge_feat_dim: int = 128, # NOTE orig,debug = 128,3 + three_body_feat_dim: int = 128, # NOTE orig,debug = 128,32 + mlp_hidden_dims: Union[int, Sequence[int]] = (128, 128), + dropout: float = 0.0, + use_bias: bool = False, + distance_expansion: str = "Bessel", + three_body_expansion: str = "SH", + num_radial: int = 7, + num_angular: int = 7, + max_l: int = 4, + max_n: int = 4, + envelope_exponent: int = 8, + graph_conv_mlp: str = "GateMLP", + activation_type: str = "silu", + norm_type: str = "rms", + pairwise_cutoff: float = 6, + three_body_cutoff: float = 4, + use_smoothed_for_delta_edge: bool = False, + learnable_basis: bool = True, + is_intensive: bool = True, + is_conservation: bool = True, + reference_energy: str | None = None, + ): + """ + Args: + num_layers (int): message passing layers. + node_feat_dim (int): atom feature embedding dim. + edge_feat_dim (int): edge(pairwise) feature embedding dim. + three_body_feat_dim (int): angle(three body) feature embedding dim. + mlp_hidden_dims (List or int): hidden dims of MLP. + Can be 'int' or 'list'. + dropout (float): dropout rate in MLP. + use_bias (bool): whether use bias in Interaction block. + distance_expansion (str): The function of pairwise basis. + Can be "Bessel" or "Gaussian". + three_body_expansion (str): The function of three body basis. + Can be "Fourier(fourier)" or "Spherical Harmonics(sh)". + num_radial (int): number of radial basis used in Bessel and Gaussian basis. + num_angular (int): number of three_body basis used in Fourier basis. + max_l (int): Maximum l value for Spherical Harmonics basis (SH). + max_n (int): Maximum n value for Spherical Harmonics basis (SH). + envelope_exponent (int): exponent of 'PolynomialEnvelope'. + graph_conv_mlp (str): The type of MLP in mp layers. + Can be "MLP", "GatedMLP" and "MoE". + See fucntion.py for more informations. + activation_type (str): activation function. + Can be "SiLU(silu)", "Sigmoid(sigmoid)", "ReLU(relu)"... + See fucntion.py for more informations. + norm_type (str): normalization function used in MLP. + Can be "LayerNorm(layer)", "BatchNorm(batch)", "RMSNorm(rms)"... + See fucntion.py for more informations. + pairwise_cutoff (float): The cutoff of Atom graph. + three_body_cutoff (float): The cutoff of Line graph. + use_smoothed_for_delta_edge (bool): Whether to use the smoothed features for edge feature update. + learnable_basis (bool): Whether the basis functions are learnable. + is_intensive (bool): whether the model outputs energy per atom (True) or total energy (False). + is_conservation (bool): whether use conservate force and stress. + reference_energy (str): refernece energy of 'str'(eg. MPtrj, OMat..) dataset(Caculated by linear regression). + more details can be found at reference_energy.py. + """ + + super().__init__() + # model configs + self.config = { k: v for k, v in locals().items() if k not in ["self", "__class__"] } + + self.is_intensive = is_intensive + + self.reference_energy = None + if reference_energy is not None: + self.reference_energy = AtomRef( + reference_energy=reference_energy, + is_intensive=is_intensive + ) + + # Define Graph Converter + self.graph_converter = GraphConverter( + atom_graph_cutoff=pairwise_cutoff, + line_graph_cutoff=three_body_cutoff, + ) + + # ====== embedding layers ======== + self.atom_embedding = AtomTypeEmbedding(atom_feat_dim=node_feat_dim) + self.edge_embedding = EdgeBasisEmbedding( + pairwise_cutoff=pairwise_cutoff, + three_body_cutoff=three_body_cutoff, + num_radial=num_radial, + edge_feat_dim=edge_feat_dim, + envelope_exponent=envelope_exponent, + learnable=learnable_basis, + distance_expansion=distance_expansion, + ) + self.three_body_embedding = ThreebodyEmbedding( + num_angular = num_angular, # Fourier + max_n=max_n, max_l=max_l, cutoff=pairwise_cutoff, # Spherical Harmonics + three_body_feat_dim = three_body_feat_dim, + three_body_expansion = three_body_expansion, + learnable = learnable_basis + ) + # ====== Interaction layers ======== + interaction_block = [ + Interaction_Block( + node_feat_dim=node_feat_dim, + edge_feat_dim=edge_feat_dim, + three_body_feat_dim=three_body_feat_dim, + num_radial=num_radial, + num_angular=num_angular, + dropout=dropout, + use_bias=use_bias, + use_smoothed_for_delta_edge=use_smoothed_for_delta_edge, + mlp_type=graph_conv_mlp, + norm_type=norm_type, + activation_type=activation_type, + ) + for _ in range(num_layers) + ] + self.interaction_block = nn.ModuleList(interaction_block) + + # ====== Readout layers ======== + self.readout_norm = get_normalization(norm_type, dim=node_feat_dim) + + self.energy_head = EnergyHead( + feat_dim = node_feat_dim, + hidden_dim = mlp_hidden_dims, + output_dim = 1, + mlp_type = "mlp", + activation_type = activation_type, + ) + self.magmom_head = MagmomHead( + feat_dim = node_feat_dim, + hidden_dim = 2 * node_feat_dim, + output_dim = 1, + mlp_type = "mlp", + activation_type = activation_type, + ) + self.force_stress_head = ForceStressHead( + is_conservation = is_conservation, + feat_dim = edge_feat_dim, # is_conservation == False + hidden_dim = mlp_hidden_dims, # is_conservation == False + output_dim = 3, # is_conservation == False + mlp_type = "mlp", # is_conservation == False + activation_type = activation_type, # is_conservation == False + ) + + if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0: + print(f"MatRIS initialized with {self.get_params()} parameters") + + def forward( + self, + graphs: Sequence[RadiusGraph], + task: str = "ef", + is_training: bool = False, + ) -> dict[str, Tensor]: + """ + Args: + graphs (List): a list of RadiusGraph. + task (str): the prediction task. Can be 'e', 'em', 'ef', 'efs', 'efsm'. + """ + prediction = {} + # ======== Graph processing ======== + batch_graph = process_graphs(graphs, compute_stress="s" in task) + + # ======== Feature embedding ======== + node_feat = self.atom_embedding( batch_graph['atomic_numbers'] - 1 ) # atom type feature init (use 0 for 'H') + edge_feat, smooth_weight = self.edge_embedding(graphs=batch_graph) # pairwise feature init + threebody_feat = None + if len(batch_graph['line_graph_dict']['line_graph']) != 0: + threebody_feat = self.three_body_embedding(graphs=batch_graph) # three body feature init + + # ======== Interaction Block ======= + for mp_layer in self.interaction_block: + node_feat, edge_feat, threebody_feat = mp_layer( + batch_graph=batch_graph, + node_feat=node_feat, + edge_feat=edge_feat, + threebody_feat=threebody_feat, + smooth_weight=smooth_weight, + ) + + # ======== Readout Block ======= + node_feat = self.readout_norm(node_feat) + + total_energy = self.energy_head(batch_graph = batch_graph, node_feat = node_feat) + + force_stress_dict = self.force_stress_head( + batch_graph = batch_graph, + compute_force="f" in task, + compute_stress="s" in task, + total_energy = total_energy, + node_feat = node_feat, + edge_feat = edge_feat, + is_training = is_training) + prediction.update(force_stress_dict) + + if "m" in task: + magmom = self.magmom_head(batch_graph = batch_graph, node_feat = node_feat) + prediction["m"] = magmom + + atoms_per_graph_tensor = torch.tensor(batch_graph['atoms_per_graph'], + dtype=torch.int32, + device=total_energy.device) + if self.is_intensive: + energy_per_atom = total_energy / atoms_per_graph_tensor + prediction["e"] = energy_per_atom + else: + prediction["e"] = total_energy + + prediction["atoms_per_graph"] = atoms_per_graph_tensor + + ref_energy = ( + 0 if self.reference_energy is None else self.reference_energy(graphs) + ) + prediction["e"] += ref_energy + prediction["ref_energy"] = ref_energy + return prediction + + def get_params(self) -> int: + """Return the number of parameters in the model.""" + return sum(p.numel() for p in self.parameters()) + + @classmethod + def from_dict(cls, dct: dict): + matris = MatRIS(**dct["config"]) + matris.load_state_dict(dct["state_dict"]) + return matris + + @classmethod + def load( + cls, + model_name: str = "matris_10m_oam", + device: str | None = None, + cache_dir: str | None = None, + ): + """Load pretrained model.""" + model_name = model_name.lower() + supported_models = ["matris_10m_oam", "matris_10m_mp"] + if model_name not in supported_models: + raise ValueError(f"Unsupported model_name: {model_name}. Supported models are: {supported_models}") + + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + + + if cache_dir is None: + cache_dir = os.environ.get("MATRIS_CACHE_DIR", "~/.cache/matris") + + cache_dir = os.path.expanduser(cache_dir) + os.makedirs(cache_dir, exist_ok=True) + + checkpoint_files = { + "matris_10m_omat": "MatRIS_10M_OMAT.pth.tar", + "matris_10m_oam": "MatRIS_10M_OAM.pth.tar", + "matris_10m_mp": "MatRIS_10M_MP.pth.tar", + "matris_6m_mp": "MatRIS_6M_MP.pth.tar", + } + + DOWNLOAD_URLS = { + "matris_10m_omat": "", # TODO + "matris_10m_oam": "https://figshare.com/ndownloader/files/59142728", + "matris_10m_mp": "https://figshare.com/ndownloader/files/59143058", + "matris_6m_mp": "", # TODO + } + + ckpt_filename = checkpoint_files[model_name] + ckpt_path = os.path.join(cache_dir, ckpt_filename) + + if not os.path.exists(ckpt_path): + url = DOWNLOAD_URLS.get(model_name) + if not url: + raise ValueError(f"No download URL provided for model: {model_name}") + + print(f"Checkpoint not found, downloading to {ckpt_path} ...") + torch.hub.download_url_to_file(url, ckpt_path) + + + ckpt_state = torch.load( + ckpt_path, + map_location=torch.device("cpu"), + weights_only=False + ) + model = MatRIS.from_dict(ckpt_state) + + model = model.to(device) + print(f"Loading {model_name} successfully, running on {device}.") + + return model diff --git a/models/matris/matris/model/op/__init__.py b/models/matris/matris/model/op/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..dd006e06de22ad4d418e00bc09ad6d2accb1973c --- /dev/null +++ b/models/matris/matris/model/op/__init__.py @@ -0,0 +1,9 @@ +from .fused_silu_op import fused_silu +from .fuse_sigmoid_op import fused_sigmoid +from .fuse_basis_func import fusion_env_val + +__all__ = [ + "fused_silu", + "fused_sigmoid", + "fusion_env_val", +] \ No newline at end of file diff --git a/models/matris/matris/model/op/__pycache__/__init__.cpython-310.pyc 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1 + a * pow(r_scaled, p) + b * pow(r_scaled, (p + 1)) + c * pow(r_scaled, (p + 2)) ); + } +""" +#return ( 1 + a * r_scaled ** p + b * r_scaled ** (p + 1) + c * r_scaled ** (p + 2) ); +envelop_fwd_func = torch.cuda.jiterator._create_jit_fn(fusion_envelop_string) + +fusion_envelop_bwd_string = """ + template T envelop_bwd_kernel(T grad_output, T r_scaled, T a, T b, T c, T p) + { + T grad_r_scaled = ( + a * p * pow(r_scaled, (p - 1)) + + b * (p + 1) * pow(r_scaled, p) + + c * (p + 2) * pow(r_scaled, (p + 1)) + ); + return grad_output * grad_r_scaled; + } +""" +envelop_bwd_func = torch.cuda.jiterator._create_jit_fn(fusion_envelop_bwd_string) + +fusion_envelop_gradbwd_string = """ + template T envelop_bwd_kernel(T d_grad_r_scaled, T grad_output, T r_scaled, T a, T b, T c, T p, T &grad_grad_output, T &grad_r_scaled) + { + grad_grad_output = ( + a * p * pow(r_scaled, (p - 1)) + + b * (p + 1) * pow(r_scaled, p) + + c * (p + 2) * pow(r_scaled, (p + 1)) + ); + grad_grad_output = d_grad_r_scaled * grad_grad_output; + + T grad_r_scaled_term = ( + a * p * (p - 1) * pow(r_scaled, (p - 2)) + + b * (p + 1) * p * pow(r_scaled, (p - 1)) + + c * (p + 2) * (p + 1) * pow(r_scaled, p) + ); + grad_r_scaled = d_grad_r_scaled * grad_output * grad_r_scaled_term; + } +""" +envelop_gradbwd_func = torch.cuda.jiterator._create_multi_output_jit_fn(fusion_envelop_gradbwd_string, num_outputs=2) + + +class envelop_bwd(Function): + @staticmethod + def forward(ctx, grad_output: torch.Tensor, r_scaled: torch.Tensor, a: torch.Tensor, b: torch.Tensor, c: torch.Tensor, p: torch.Tensor): + """ + # Compute grad_r_scaled + grad_r_scaled_part = ( + a * p * r_scaled ** (p - 1) + + b * (p + 1) * r_scaled ** p + + c * (p + 2) * r_scaled ** (p + 1) + ) + + grad_r_scaled = grad_output * grad_r_scaled_part + """ + grad_r_scaled = envelop_bwd_func(grad_output, r_scaled, a, b, c, p) + # Save for backward + ctx.save_for_backward(grad_output, r_scaled) + ctx.a = a + ctx.b = b + ctx.c = c + ctx.p = p + + return grad_r_scaled + + @staticmethod + def backward(ctx, d_grad_r_scaled): + grad_output, r_scaled = ctx.saved_tensors + a = ctx.a + b = ctx.b + c = ctx.c + p = ctx.p + """ + # Compute gradient w.r.t grad_output + grad_grad_output = ( + a * p * r_scaled ** (p - 1) + + b * (p + 1) * r_scaled ** p + + c * (p + 2) * r_scaled ** (p + 1) + ) + grad_grad_output = d_grad_r_scaled * grad_grad_output # chain rule + + # Compute gradient w.r.t r_scaled + grad_r_scaled_term = ( + a * p * (p - 1) * r_scaled ** (p - 2) + + b * (p + 1) * p * r_scaled ** (p - 1) + + c * (p + 2) * (p + 1) * r_scaled ** p + ) + grad_r_scaled = d_grad_r_scaled * grad_output * grad_r_scaled_term # chain rule + """ + grad_grad_output, grad_r_scaled = envelop_gradbwd_func(d_grad_r_scaled, grad_output, r_scaled, a, b, c, p) + # Return gradients in the order of forward's inputs + return grad_grad_output, grad_r_scaled, None, None, None, None + + +class envelop_fwd(Function): + @staticmethod + def forward(ctx, r_scaled: torch.Tensor, a: torch.Tensor, b:torch.Tensor, c: torch.Tensor, p:torch.Tensor): + output = envelop_fwd_func(r_scaled, a, b, c, p) + ctx.save_for_backward(r_scaled) + ctx.a = a + ctx.b = b + ctx.c = c + ctx.p = p + return output + + @staticmethod + def backward(ctx, grad_output): + (r_scaled,) = ctx.saved_tensors + a = ctx.a + b = ctx.b + c = ctx.c + p = ctx.p + + grad_r_scaled = envelop_bwd.apply(grad_output, r_scaled, a, b, c, p) + + return grad_r_scaled, None, None, None, None + + +def fusion_env_val(r_scaled:torch.Tensor, a:torch.Tensor, b:torch.Tensor, c:torch.Tensor, p:torch.Tensor): + if r_scaled.device.type == "cuda": + return envelop_fwd.apply(r_scaled, a, b, c, p) + else: + return ( 1 + a * r_scaled ** p + b * r_scaled ** (p + 1) + c * r_scaled ** (p + 2) ) + diff --git a/models/matris/matris/model/op/fuse_sigmoid_op.py b/models/matris/matris/model/op/fuse_sigmoid_op.py new file mode 100644 index 0000000000000000000000000000000000000000..be063aed03849e3f711d83664eef6c02e9ba1b9a --- /dev/null +++ b/models/matris/matris/model/op/fuse_sigmoid_op.py @@ -0,0 +1,63 @@ +import torch +from torch.autograd import Function +from torch.profiler import profile, ProfilerActivity,record_function + + +sigmoid_bwd_string = """ + template T sigmoid_bwd_kernel(T dgrad, T output) + { + return dgrad * output * (1 - output); + } +""" +sigmoid_bwd_func = torch.cuda.jiterator._create_jit_fn(sigmoid_bwd_string) + +sigmoid_gradbwd_string = """ + template T sigmoid_grad_bwd_kernel(T grad_output, T dgrad, T output, T &grad_dgrad, T &grad_output_val) + { + grad_dgrad = grad_output * output * (1 - output); + grad_output_val = grad_output * dgrad * (1 - 2 * output); + } +""" +sigmoid_gradbwd_func = torch.cuda.jiterator._create_multi_output_jit_fn(sigmoid_gradbwd_string, num_outputs=2) + + +class sigmoid_bwd(Function): + @staticmethod + def forward(ctx, dgrad, output): + #grad_feas = dgrad * output * (1 - output) + with record_function("sigmoid bwd"): + grad_feas = sigmoid_bwd_func(dgrad, output) + ctx.save_for_backward(dgrad, output) + #ctx.save_for_backward() + return grad_feas + + @staticmethod + def backward(ctx, grad_output): + dgrad, output = ctx.saved_tensors + """ + grad_dgrad = grad_output * output * (1 - output) + grad_output_val = grad_output * dgrad * (1 - 2 * output) + """ + with record_function("sigmoid grad bwd"): + grad_dgrad, grad_output_val = sigmoid_gradbwd_func(grad_output, dgrad, output) + return grad_dgrad, grad_output_val + +class sigmoid_fwd(Function): + @staticmethod + def forward(ctx, feas): + output = torch.nn.functional.sigmoid(feas) + ctx.save_for_backward(output) + return output + + @staticmethod + def backward(ctx, dgrad): + output, = ctx.saved_tensors + grad_feas = sigmoid_bwd.apply(dgrad, output) + return grad_feas + +def fused_sigmoid(input_feas): + if input_feas.device.type == "cuda": + return sigmoid_fwd.apply(input_feas) + else: + return torch.nn.functional.sigmoid(input_feas) + \ No newline at end of file diff --git a/models/matris/matris/model/op/fused_silu_op.py b/models/matris/matris/model/op/fused_silu_op.py new file mode 100644 index 0000000000000000000000000000000000000000..40267f579e11a9d4d0fe78b00e65fc248055f83a --- /dev/null +++ b/models/matris/matris/model/op/fused_silu_op.py @@ -0,0 +1,82 @@ +import torch +from torch.autograd import Function +from torch.profiler import profile, ProfilerActivity,record_function + +silu_bwd_string = """ + template T silu_bwd_kernel(T dgrad, T x) + { + T sigmoid_x = 1/(1 + exp(-x)); + + T grad_input = dgrad * (1 + x*(1-sigmoid_x)) * sigmoid_x; + + return grad_input; + } +""" +silu_bwd_func = torch.cuda.jiterator._create_jit_fn(silu_bwd_string) + +silu_gradbwd_string = """ + template T silu_grad_bwd_kernel(T grad_grad_input, T grad_output, T x, T &grad_grad_output, T &grad_x) + { + T sigmoid_x = 1/(1 + exp(-x)); + T term = sigmoid_x * (1 + x * (1 - sigmoid_x)); + grad_grad_output = grad_grad_input * term; + + T sigmoid_derivative = sigmoid_x * (1 - sigmoid_x); + T d_term_dx = sigmoid_derivative * (2 + x * (1 - 2 * sigmoid_x)); + grad_x = grad_grad_input * grad_output * d_term_dx; + + } +""" +silu_gradbwd_func = torch.cuda.jiterator._create_multi_output_jit_fn(silu_gradbwd_string, num_outputs=2) + + +class fusion_silu_bwd(Function): + @staticmethod + def forward(ctx, grad_output, x): + ctx.save_for_backward(x, grad_output) + """ + sigmoid_x = torch.sigmoid(x) + grad_input = grad_output * (1 + x * (1 - sigmoid_x)) * sigmoid_x + """ + with record_function("silu bwd"): + grad_input = silu_bwd_func(grad_output, x) + return grad_input + + @staticmethod + def backward(ctx, grad_grad_input): + """ + sigmoid_x = torch.sigmoid(x) + term = sigmoid_x * (1 + x * (1 - sigmoid_x)) + grad_grad_output = grad_grad_input * term + sigmoid_derivative = sigmoid_x * (1 - sigmoid_x) + d_term_dx = sigmoid_derivative * (2 + x * (1 - 2 * sigmoid_x)) + + grad_x = grad_grad_input * grad_output * d_term_dx + """ + x, grad_output = ctx.saved_tensors + + with record_function("silu grad bwd"): + grad_grad_output, grad_x = silu_gradbwd_func(grad_grad_input, grad_output, x) + return grad_grad_output, grad_x + +class fusion_silu_fwd(Function): + @staticmethod + def forward(ctx, x): + + out = torch.nn.functional.silu(x) + ctx.save_for_backward(x) + return out + + @staticmethod + def backward(ctx, grad_output): + x, = ctx.saved_tensors + grad_input = fusion_silu_bwd.apply(grad_output, x) + return grad_input + +def fused_silu(input_feas): + if input_feas.device.type == "cuda": + return fusion_silu_fwd.apply(input_feas) + else: + return torch.nn.functional.silu(input_feas) + +# out = fused_silu(input) \ No newline at end of file diff --git a/models/matris/matris/model/op/src/Opdefine.h b/models/matris/matris/model/op/src/Opdefine.h new file mode 100644 index 0000000000000000000000000000000000000000..af3be39d74fd46143bd691491fee62f314cf90ec --- /dev/null +++ b/models/matris/matris/model/op/src/Opdefine.h @@ -0,0 +1,13 @@ +#ifndef OP_SRC_OPDECLARE_H_ +#define OP_SRC_OPDECLARE_H_ + +#include + + +torch::Tensor fused_SiLU_Bwd(const torch::Tensor &dgrad, const torch::Tensor &input); + +std::vector fused_SiLU_Grad_Bwd(const torch::Tensor &grad_grad_input, const torch::Tensor &grad_output, + const torch::Tensor &input); + + +#endif // OP_SRC_OPDECLARE_H_ \ No newline at end of file diff --git a/models/matris/matris/model/op/src/RegisterOp.cpp b/models/matris/matris/model/op/src/RegisterOp.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d7fe7b1645528c353bc1c6db3939b418bb9b5288 --- /dev/null +++ b/models/matris/matris/model/op/src/RegisterOp.cpp @@ -0,0 +1,13 @@ +#include "Opdefine.h" + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) +{ + m.def("fuse_silu_bwd", &fused_SiLU_Bwd, "fuse_silu_bwd"); + m.def("fuse_silu_grad_bwd", &fused_SiLU_Grad_Bwd, "fuse_silu_grad_bwd"); +} + +TORCH_LIBRARY(matris_op, m) +{ + m.def("fuse_silu_bwd", &fused_SiLU_Bwd); + m.def("fuse_silu_grad_bwd", &fused_SiLU_Grad_Bwd); +} diff --git a/models/matris/matris/model/op/src/cpp/fused_silu.cpp b/models/matris/matris/model/op/src/cpp/fused_silu.cpp new file mode 100644 index 0000000000000000000000000000000000000000..493e3e4b04ed36eb0c0c5a69f793577d5d2893d2 --- /dev/null +++ b/models/matris/matris/model/op/src/cpp/fused_silu.cpp @@ -0,0 +1,54 @@ +#include +#include +#include +#include +#include +#include + +using namespace std; + +void launch_silu_bwd_kernel(const float *dgrad, const float *input, float *output, int feas_num, int embed_dim); + +void launch_silu_grad_bwd_kernel(const float *grad_grad_input, const float *grad_output, const float *input, + float *grad_grad_output, float *grad_input, + int feas_num, int embed_dim); + +torch::Tensor fused_SiLU_Bwd(const torch::Tensor &dgrad, const torch::Tensor &input) +{ + const at::cuda::OptionalCUDAGuard device_guard(device_of(dgrad)); + int64_t embed_dim = input.size(-1); + int64_t feas_num = input.size(-2); + + torch::Tensor output = torch::empty({feas_num, embed_dim}, torch::TensorOptions().dtype(torch::kFloat).device(input.device())); + + launch_silu_bwd_kernel( + static_cast(dgrad.data_ptr()), + static_cast(input.data_ptr()), + static_cast(output.data_ptr()), + feas_num, + embed_dim + ); + return output; +} + +std::vector fused_SiLU_Grad_Bwd(const torch::Tensor &grad_grad_input, const torch::Tensor &grad_output, + const torch::Tensor &input) +{ + const at::cuda::OptionalCUDAGuard device_guard(device_of(grad_grad_input)); + int64_t embed_dim = input.size(-1); + int64_t feas_num = input.size(-2); + //int64_t total_elements = feas_num * embed_dim; + torch::Tensor grad_grad_output = torch::empty({feas_num, embed_dim}, torch::TensorOptions().dtype(torch::kFloat).device(input.device())); + torch::Tensor grad_input = torch::empty({feas_num, embed_dim}, torch::TensorOptions().dtype(torch::kFloat).device(input.device())); + + launch_silu_grad_bwd_kernel( + static_cast(grad_grad_input.data_ptr()), + static_cast(grad_output.data_ptr()), + static_cast(input.data_ptr()), + //output + static_cast(grad_grad_output.data_ptr()), + static_cast(grad_input.data_ptr()), + feas_num, embed_dim + ); + return {grad_grad_output, grad_input}; +} \ No newline at end of file diff --git a/models/matris/matris/model/op/src/kernel/fused_silu_bwd.cu b/models/matris/matris/model/op/src/kernel/fused_silu_bwd.cu new file mode 100644 index 0000000000000000000000000000000000000000..a840d4e354a2b523e2d4478eda5f8df2c29e15a1 --- /dev/null +++ b/models/matris/matris/model/op/src/kernel/fused_silu_bwd.cu @@ -0,0 +1,39 @@ +#include +#include +#include + +__global__ void silu_bwd_kernel(const float* dgrad, const float* input, float* output, int num_float4) { + int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx >= num_float4) return; + + float4 x4 = reinterpret_cast(input)[idx]; + float4 g4 = reinterpret_cast(dgrad)[idx]; + float4 out4; + + auto process = [](float g_val, float x_val) { + float sigmoid_x = 1.0f / (1.0f + expf(-x_val)); + float term = 1.0f + x_val * (1.0f - sigmoid_x); + return g_val * (sigmoid_x * term); + }; + + out4.x = process(g4.x, x4.x); + out4.y = process(g4.y, x4.y); + out4.z = process(g4.z, x4.z); + out4.w = process(g4.w, x4.w); + + reinterpret_cast(output)[idx] = out4; +} + +void launch_silu_bwd_kernel(const float* dgrad, + const float* input, + float* output, + int feas_num, + int embed_dim) +{ + int num_elements = feas_num * embed_dim; + int num_float4 = num_elements / 4; + int block_size = 256; + int grid_size = (num_float4 + block_size - 1) / block_size; + + silu_bwd_kernel<<>>(dgrad, input, output, num_float4); +} diff --git a/models/matris/matris/model/op/src/kernel/fused_silu_grad_bwd.cu b/models/matris/matris/model/op/src/kernel/fused_silu_grad_bwd.cu new file mode 100644 index 0000000000000000000000000000000000000000..8b912f2437d12c110ea9a9e037a1371a99df49e1 --- /dev/null +++ b/models/matris/matris/model/op/src/kernel/fused_silu_grad_bwd.cu @@ -0,0 +1,123 @@ +#include +#include +#include + +constexpr int VEC_SIZE = 4; +constexpr int THREADS_PER_BLOCK = 256; + +__device__ __inline__ float4 sigmoid4(const float4& x) { + return make_float4( + 1.0f / (1.0f + expf(-x.x)), + 1.0f / (1.0f + expf(-x.y)), + 1.0f / (1.0f + expf(-x.z)), + 1.0f / (1.0f + expf(-x.w)) + ); +} + +__global__ void silu_grad_bwd_kernel( + const float* __restrict__ grad_grad_input, + const float* __restrict__ grad_output, + const float* __restrict__ input, + float* __restrict__ grad_grad_output, + float* __restrict__ grad_input, + int64_t total_elements) +{ + const int64_t idx = (blockIdx.x * blockDim.x + threadIdx.x) * VEC_SIZE; + if (idx >= total_elements) return; + + const float4 ggi = *reinterpret_cast(grad_grad_input + idx); + const float4 go = *reinterpret_cast(grad_output + idx); + const float4 x = *reinterpret_cast(input + idx); + + const float4 sig_x = sigmoid4(x); + float4 one_minus_sig; + one_minus_sig.x = 1.0f - sig_x.x; + one_minus_sig.y = 1.0f - sig_x.y; + one_minus_sig.z = 1.0f - sig_x.z; + one_minus_sig.w = 1.0f - sig_x.w; + + float4 x_one_minus_sig; + x_one_minus_sig.x = x.x * one_minus_sig.x; + x_one_minus_sig.y = x.y * one_minus_sig.y; + x_one_minus_sig.z = x.z * one_minus_sig.z; + x_one_minus_sig.w = x.w * one_minus_sig.w; + + float4 term; + term.x = sig_x.x * (1.0f + x_one_minus_sig.x); + term.y = sig_x.y * (1.0f + x_one_minus_sig.y); + term.z = sig_x.z * (1.0f + x_one_minus_sig.z); + term.w = sig_x.w * (1.0f + x_one_minus_sig.w); + + float4 grad_grad_out; + grad_grad_out.x = ggi.x * term.x; + grad_grad_out.y = ggi.y * term.y; + grad_grad_out.z = ggi.z * term.z; + grad_grad_out.w = ggi.w * term.w; + + float4 sig_x_2; // 2 * sig_x + sig_x_2.x = 2.0f * sig_x.x; + sig_x_2.y = 2.0f * sig_x.y; + sig_x_2.z = 2.0f * sig_x.z; + sig_x_2.w = 2.0f * sig_x.w; + + float4 one_minus_2sig; + one_minus_2sig.x = 1.0f - sig_x_2.x; + one_minus_2sig.y = 1.0f - sig_x_2.y; + one_minus_2sig.z = 1.0f - sig_x_2.z; + one_minus_2sig.w = 1.0f - sig_x_2.w; + + float4 x_one_minus_2sig; + x_one_minus_2sig.x = x.x * one_minus_2sig.x; + x_one_minus_2sig.y = x.y * one_minus_2sig.y; + x_one_minus_2sig.z = x.z * one_minus_2sig.z; + x_one_minus_2sig.w = x.w * one_minus_2sig.w; + + float4 part; + part.x = 2.0f + x_one_minus_2sig.x; + part.y = 2.0f + x_one_minus_2sig.y; + part.z = 2.0f + x_one_minus_2sig.z; + part.w = 2.0f + x_one_minus_2sig.w; + + float4 sig_deriv; + sig_deriv.x = sig_x.x * one_minus_sig.x; + sig_deriv.y = sig_x.y * one_minus_sig.y; + sig_deriv.z = sig_x.z * one_minus_sig.z; + sig_deriv.w = sig_x.w * one_minus_sig.w; + + float4 d_term_dx; + d_term_dx.x = sig_deriv.x * part.x; + d_term_dx.y = sig_deriv.y * part.y; + d_term_dx.z = sig_deriv.z * part.z; + d_term_dx.w = sig_deriv.w * part.w; + + float4 grad_in; + grad_in.x = ggi.x * go.x * d_term_dx.x; + grad_in.y = ggi.y * go.y * d_term_dx.y; + grad_in.z = ggi.z * go.z * d_term_dx.z; + grad_in.w = ggi.w * go.w * d_term_dx.w; + + *reinterpret_cast(grad_grad_output + idx) = grad_grad_out; + *reinterpret_cast(grad_input + idx) = grad_in; +} + +void launch_silu_grad_bwd_kernel( + const float* grad_grad_input, + const float* grad_output, + const float* input, + float* grad_grad_output, + float* grad_input, + int feas_num, + int embed_dim) +{ + const int64_t total_elements = static_cast(feas_num) * embed_dim; + const int64_t grid_size = (total_elements + THREADS_PER_BLOCK * VEC_SIZE - 1) / (THREADS_PER_BLOCK * VEC_SIZE); + + silu_grad_bwd_kernel<<>>( + grad_grad_input, + grad_output, + input, + grad_grad_output, + grad_input, + total_elements + ); +} \ No newline at end of file diff --git a/models/matris/matris/model/op/src/kernel/type_shim.h b/models/matris/matris/model/op/src/kernel/type_shim.h new file mode 100644 index 0000000000000000000000000000000000000000..ea607a184a73a0f8c838264315cf7823382a2ca1 --- /dev/null +++ b/models/matris/matris/model/op/src/kernel/type_shim.h @@ -0,0 +1,241 @@ +// modified from https://github.com/NVIDIA/apex + +#include + +#ifndef TORCH_CHECK +#define TORCH_CHECK AT_CHECK +#endif + +#ifdef VERSION_GE_1_3 +#define DATA_PTR data_ptr +#else +#define DATA_PTR data +#endif + +#define DISPATCH_HALF_AND_BFLOAT(TYPE, NAME, ...) \ + switch (TYPE) { \ + case at::ScalarType::Half: { \ + using scalar_t = at::Half; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::BFloat16: { \ + using scalar_t = at::BFloat16; \ + __VA_ARGS__; \ + break; \ + } \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ + } + +#define DISPATCH_FLOAT_HALF_AND_BFLOAT_INOUT_TYPES(TYPEIN, TYPEOUT, NAME, ...) \ + switch (TYPEIN) { \ + case at::ScalarType::Float: { \ + using scalar_t_in = float; \ + switch (TYPEOUT) { \ + case at::ScalarType::Float: { \ + using scalar_t_out = float; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::Half: { \ + using scalar_t_out = at::Half; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::BFloat16: { \ + using scalar_t_out = at::BFloat16; \ + __VA_ARGS__; \ + break; \ + } \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPEOUT), "'"); \ + } \ + break; \ + } \ + case at::ScalarType::Half: { \ + using scalar_t_in = at::Half; \ + using scalar_t_out = at::Half; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::BFloat16: { \ + using scalar_t_in = at::BFloat16; \ + using scalar_t_out = at::BFloat16; \ + __VA_ARGS__; \ + break; \ + } \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPEIN), "'"); \ + } + +// Forward/backward compatiblity hack around +// https://github.com/pytorch/pytorch/commit/3aeb78079bcd68282fe9117088e138b77318e288 +// pending more future-proof guidance from upstream. +// struct TypeShim +// { +// const at::Type& payload; +// TypeShim(const at::Type& type) : payload(type) {} +// // Enable trivial conversion to a const at::Type& for pre-3aeb78 +// operator const at::Type&(){ return payload; }; +// // Enable dispatch switch statements to take *this directly for post-3aeb78 +// //operator at::ScalarType(){ return payload.; }; +// }; + +#define DISPATCH_FLOAT_AND_HALF(TYPE, LEVEL, NAME, ...) \ + switch (TYPE) { \ + case at::ScalarType::Float: { \ + using scalar_t_##LEVEL = float; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::Half: { \ + using scalar_t_##LEVEL = at::Half; \ + __VA_ARGS__; \ + break; \ + } \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ + } + +#define DISPATCH_FLOAT_HALF_AND_BYTE(TYPE, LEVEL, NAME, ...) \ + switch (TYPE) { \ + case at::ScalarType::Float: { \ + using scalar_t_##LEVEL = float; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::Half: { \ + using scalar_t_##LEVEL = at::Half; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::Byte: { \ + using scalar_t_##LEVEL = uint8_t; \ + __VA_ARGS__; \ + break; \ + } \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ + } + +#define DISPATCH_DOUBLE_FLOAT_AND_HALF(TYPE, LEVEL, NAME, ...) \ + switch (TYPE) { \ + case at::ScalarType::Double: { \ + using scalar_t_##LEVEL = double; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::Float: { \ + using scalar_t_##LEVEL = float; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::Half: { \ + using scalar_t_##LEVEL = at::Half; \ + __VA_ARGS__; \ + break; \ + } \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ + } + +#define DISPATCH_DOUBLE_AND_FLOAT(TYPE, LEVEL, NAME, ...) \ + switch (TYPE) { \ + case at::ScalarType::Double: { \ + using scalar_t_##LEVEL = double; \ + __VA_ARGS__; \ + break; \ + } \ + case at::ScalarType::Float: { \ + using scalar_t_##LEVEL = float; \ + __VA_ARGS__; \ + break; \ + } \ + default: \ + AT_ERROR(#NAME, " not implemented for '", toString(TYPE), "'"); \ + } + +template +__device__ __forceinline__ T +reduce_block_into_lanes(T *x, T val, int lanes = 1, + bool share_result = false) // lanes is intended to be <= 32. +{ + int tid = threadIdx.x + threadIdx.y * blockDim.x; + int blockSize = blockDim.x * blockDim.y; // blockSize is intended to be a multiple of 32. + + if (blockSize >= 64) { + x[tid] = val; + __syncthreads(); + } + +#pragma unroll + for (int i = (blockSize >> 1); i >= 64; i >>= 1) { + if (tid < i) x[tid] = x[tid] + x[tid + i]; + __syncthreads(); + } + + T final; + + if (tid < 32) { + if (blockSize >= 64) + final = x[tid] + x[tid + 32]; + else + final = val; + // __SYNCWARP(); + +#pragma unroll + for (int i = 16; i >= lanes; i >>= 1) + final = final + __shfl_down_sync(0xffffffff, final, i); + } + + if (share_result) { + if (tid < lanes) x[tid] = final; // EpilogueOp + // Make sure the smem result is visible to all warps. + __syncthreads(); + } + + return final; +} + +template +__device__ __forceinline__ T +reduce_block_into_lanes_max_op(T *x, T val, int lanes = 1, + bool share_result = false) // lanes is intended to be <= 32. +{ + int tid = threadIdx.x + threadIdx.y * blockDim.x; + int blockSize = blockDim.x * blockDim.y; // blockSize is intended to be a multiple of 32. + + if (blockSize >= 64) { + x[tid] = val; + __syncthreads(); + } + +#pragma unroll + for (int i = (blockSize >> 1); i >= 64; i >>= 1) { + if (tid < i) x[tid] = fmaxf(fabsf(x[tid]), fabsf(x[tid + i])); + __syncthreads(); + } + + T final; + + if (tid < 32) { + if (blockSize >= 64) + final = fmaxf(fabsf(x[tid]), fabsf(x[tid + 32])); + else + final = val; + // __SYNCWARP(); + +#pragma unroll + for (int i = 16; i >= lanes; i >>= 1) + final = fmaxf(fabsf(final), fabsf(__shfl_down_sync(0xffffffff, final, i))); + } + + if (share_result) { + if (tid < lanes) x[tid] = final; // EpilogueOp + // Make sure the smem result is visible to all warps. + __syncthreads(); + } + + return final; +} \ No newline at end of file diff --git a/models/matris/matris/model/op/src/setup.py b/models/matris/matris/model/op/src/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..72c3a1206ed47caa07f4ef4ab79b254939b6e1d9 --- /dev/null +++ b/models/matris/matris/model/op/src/setup.py @@ -0,0 +1,21 @@ +from setuptools import setup +from torch.utils.cpp_extension import BuildExtension, CUDAExtension +import glob + +cuda_source = glob.glob("**/*.cu", recursive=True) +cpp_source = glob.glob("**/*.cpp", recursive=True) + +setup( + name="matris_op", + include_dirs=["include"], + ext_modules=[ + CUDAExtension( + "matris_op", + cuda_source + cpp_source, + extra_compile_args={"nvcc": ["-O3","-std=c++17"]}, + ) + ], + cmdclass={"build_ext": BuildExtension} +) + +#python setup.py build develop \ No newline at end of file diff --git a/models/matris/matris/model/processgraph.py b/models/matris/matris/model/processgraph.py new file mode 100644 index 0000000000000000000000000000000000000000..6b661446757e78af34426933afa6abc37bee1a30 --- /dev/null +++ b/models/matris/matris/model/processgraph.py @@ -0,0 +1,246 @@ +import torch +from matris.graph import RadiusGraph +from collections.abc import Sequence + + +def process_graphs(graphs: Sequence[RadiusGraph], + compute_stress: bool = True): + """ + Process a sequence of graphs and batch them into a unified graph. + + Args: + graphs: Sequence of RadiusGraph objects containing multiple crystal graph data. + compute_stress: Whether to compute stress. + + Returns: + batched_graph: + atomic_numbers: Atomic numbers of all atoms [total_atoms] + edge_lengths: Lengths of all edges [total_direct_edges] + unit_edge_vectors: Unit edge vectors [total_direct_edges, 3] + atom_segment: Graph(Batch) indices for each atom [total_atoms] + atoms_per_graph: List of atom counts per graph + directed2undirected: Mapping from directed to undirected edges [total_direct_edges] + undirected2directed: Mapping from undirected to directed edges [total_undirected_edges] + volumes: Volume of each graph [num_graphs, 1, 1] + lattice: Lattice vectors [num_graphs*3, 3] + + batch_cart_coords: Cartesian coordinates of all atoms [total_atoms, 3] + batch_strains: Strain tensors [num_graphs, 3, 3] (only when compute_stress=True) + + atom_graph_dict: Dict containing atom graph information + atom_graph: Atom connectivity [total_direct_edges, 2] + target_index/source_index: Target/source node indices for edges [total_direct_edges] + num_segment: Number of segments + source_bincount: The frequency of each source node in atom graph + target_bincount: The frequency of each target node in atom graph + line_graph_dict: Dict containing line graph information + line_graph: Compressed line graph [num_angles, 3] # remove directed edge index + atom_list: Central atom indices for angles [num_angles] + target_index/source_index: Target/source edge indices for line graph [num_angles] + target_DE_index/source_DE_index: Directed edge indices [num_angles] + source_bincount: The frequency of each source node in line graph + target_bincount: The frequency of each target node in line graph + """ + + batched_graph = {} + num_graphs = len(graphs) + device = graphs[0].lattice.device + dtype = graphs[0].lattice.dtype + + atomic_numbers = [] + batched_atom_graph, batched_line_graph = [], [] + atom_segment, atoms_per_graph = [], [] + + batch_lattice, batch_image, batch_cart_coords = [], [], [] + undirected2directed, directed2undirected = [], [] + + atom_graph_len, num_undirected2directed, num_directed2undirected = 0, 0, 0 + for graph_idx, graph in enumerate(graphs): + direct_edge_num = len(graph.atom_graph) + undirected2directed.append(graph.undirected2directed) + num_undirected2directed += int(direct_edge_num/2) # undirect edge num + + directed2undirected.append(graph.directed2undirected) + num_directed2undirected += direct_edge_num # direct edge num + + batched_atom_graph.append(graph.atom_graph) + atom_graph_len += direct_edge_num # direct edge num + + atom_offset_idx_list = torch.empty((atom_graph_len, 2), dtype=torch.int32) + edge_offses_idx_list = torch.empty(num_undirected2directed, dtype=torch.int32) + n_undirected_list = torch.empty(num_directed2undirected, dtype=torch.int32) + + atom_idx_offset, edge_idx_offset, undirected_list_offset, directed_list_offset = 0, 0, 0, 0 + start_atom_offset, start_edge_offset, start_n_undirected = 0, 0, 0 + + for graph_idx, graph in enumerate(graphs): + atomic_numbers.append(graph.atomic_number) # atom type + n_atom = graph.atomic_number.shape[0] + atoms_per_graph.append(n_atom) + if graph.atom_frac_coord.requires_grad: + #graph.atom_frac_coord.requires_grad = False + graph.atom_frac_coord = graph.atom_frac_coord.detach() + if graph.lattice.requires_grad: + #graph.lattice.requires_grad = False + graph.lattice = graph.lattice.detach() + + atom_cart_coords = graph.atom_frac_coord @ graph.lattice # [n_atom, 3] + + batch_cart_coords.append(atom_cart_coords) + batch_lattice.append(graph.lattice) + batch_image.append(graph.neighbor_image) + + edge_offses_idx_list[start_edge_offset : start_edge_offset+len(graph.undirected2directed)] = edge_idx_offset + atom_offset_idx_list[start_atom_offset : start_atom_offset+len(graph.atom_graph), :] = torch.tensor([atom_idx_offset, atom_idx_offset]) + n_undirected_list[start_n_undirected : start_n_undirected+len(graph.directed2undirected)] = undirected_list_offset + + if len(graph.line_graph) != 0: + this_line_graph = graph.line_graph.new_zeros([graph.line_graph.shape[0], 5]) + this_line_graph[:, 0] = graph.line_graph[:, 0] + atom_idx_offset # atom type at this angle + this_line_graph[:, 1] = graph.line_graph[:, 1] + undirected_list_offset # left undirect edge index + this_line_graph[:, 3] = graph.line_graph[:, 3] + undirected_list_offset # right undirect edge index + + this_line_graph[:, 2] = graph.line_graph[:, 2] + directed_list_offset # left direct edge index + this_line_graph[:, 4] = graph.line_graph[:, 4] + directed_list_offset # right direct edge index + + batched_line_graph.append(this_line_graph) + + atom_segment.append(torch.ones(n_atom, requires_grad=False) * graph_idx) + atom_idx_offset += n_atom + edge_idx_offset += len(graph.atom_graph) + directed_list_offset += len(graph.directed2undirected) + undirected_list_offset += len(graph.undirected2directed) + + start_edge_offset += len(graph.undirected2directed) + start_atom_offset += len(graph.atom_graph) + start_n_undirected += len(graph.directed2undirected) + + edge_offses_idx_list = edge_offses_idx_list.to(device) + atom_offset_idx_list = atom_offset_idx_list.to(device) + n_undirected_list = n_undirected_list.to(device) + + + # ======= packing features ======= + atomic_numbers = torch.cat(atomic_numbers, dim=0) + batched_atom_graph = torch.cat(batched_atom_graph, dim=0) # [edge, 2] + batched_atom_graph += atom_offset_idx_list #add offset + batched_atom_graph = batched_atom_graph.to(torch.int64) + + if batched_line_graph != []: + batched_line_graph = torch.cat(batched_line_graph, dim=0) # line_graph + batched_line_graph = batched_line_graph.to(torch.int64) + else: # when line graph is empty + batched_line_graph = torch.tensor([]) + + atom_segment = ( + torch.cat(atom_segment, dim=0).type(torch.int64) + ) + + directed2undirected = torch.cat(directed2undirected, dim=0) + directed2undirected += n_undirected_list #add offset + undirected2directed = torch.cat(undirected2directed, dim=0) + undirected2directed += edge_offses_idx_list # add offset + + batch_image = torch.block_diag(*batch_image) # [direct_edge_num, num_graphs * 3]. Note: Only the diagonal blocks are valid. + batch_lattice = torch.cat(batch_lattice, dim=0) #[num_graphs * 3, 3] + batch_cart_coords = torch.cat(batch_cart_coords, dim=0) + + # Reshape [n_graphs*3, 3] -> [n_graphs, 3, 3] for volumes calculation + batch_lattices = batch_lattice.reshape(num_graphs, 3, 3) + cross_product = torch.linalg.cross(batch_lattices[:, 1], batch_lattices[:, 2]) + volumes = torch.einsum('ni,ni->n', batch_lattices[:, 0], cross_product) + volumes = volumes.unsqueeze(1).unsqueeze(2) + + batch_line_graph_compress = torch.tensor([]) + if len(batched_line_graph) != 0: + # line graph index + line_graph_atom_index = batched_line_graph[:, 0] + line_graph_UDE_target_index = batched_line_graph[:, 1] + line_graph_DE_target_index = batched_line_graph[:, 2] + line_graph_UDE_source_index = batched_line_graph[:, 3] + line_graph_DE_source_index = batched_line_graph[:, 4] + + batch_line_graph_compress = batched_line_graph.new_zeros([batched_line_graph.shape[0], 3]) + batch_line_graph_compress[:, 0] = line_graph_atom_index + batch_line_graph_compress[:, 1] = line_graph_UDE_target_index + batch_line_graph_compress[:, 2] = line_graph_UDE_source_index + + #======================================== + strains = None + batch_cart_coords.requires_grad_(True) + if compute_stress: + strains = torch.zeros( + (num_graphs, 3, 3), + dtype=dtype, + device=device, + ) + + strains.requires_grad_(True) + symmetric_strains = 0.5 * ( + strains + strains.transpose(-1, -2) + ) + + batch_cart_coords = batch_cart_coords + torch.einsum( + "be,bec->bc", batch_cart_coords, symmetric_strains[atom_segment] + ) + batch_lattice = batch_lattice.view(-1, 3, 3) + batch_lattice = batch_lattice + torch.matmul(batch_lattice, symmetric_strains) + batch_lattice = batch_lattice.view(-1, 3) + #======================================== + + # node index + target_index, source_index = batched_atom_graph[:, 0], batched_atom_graph[:, 1] + # ===== compute pairwise distance and edge vectors ===== + center_pos = batch_cart_coords[target_index] + neighbor_pos = batch_cart_coords[source_index] + neighbor_pos = neighbor_pos + batch_image @ batch_lattice + edge_vectors = center_pos - neighbor_pos + edge_lengths = torch.norm(edge_vectors, dim=1) # pairwise distance + unit_edge_vectors = edge_vectors / edge_lengths[:, None] # edge vectors + + + batched_graph['atomic_numbers'] = atomic_numbers # [atoms] + batched_graph['edge_lengths'] = edge_lengths # [direct_edge_num] + batched_graph['unit_edge_vectors'] = unit_edge_vectors # [direct_edge_num, 3] + batched_graph['atom_segment'] = atom_segment.to(device=device) # [atoms] + batched_graph['atoms_per_graph'] = atoms_per_graph # List, size:[atoms] + batched_graph['directed2undirected'] = directed2undirected.to(torch.int64) #[direct_edge_num] + batched_graph['undirected2directed'] = undirected2directed.to(torch.int64) #[undirect_edge_num] + batched_graph['volumes'] = volumes # [num_graphs, 1, 1] + batched_graph['lattice'] = batch_lattice # [num_graphs*3, 3] + batched_graph['batch_cart_coords'] = batch_cart_coords # [atoms, 3] + batched_graph['batch_strains'] = strains # [num_graphs, 3, 3] + + atom_graph_dict, line_graph_dict = {}, {} + atom_graph_dict['atom_graph'] = batched_atom_graph # [direct_edge_num, 2] + atom_graph_dict['target_index'] = target_index # [direct_edge_num] + atom_graph_dict['source_index'] = source_index # [direct_edge_num] + atom_graph_dict['num_segment'] = torch.max(target_index)+1 + + bincount_source_atom_graph = torch.bincount(source_index) + bincount_source_atom_graph = bincount_source_atom_graph.where(bincount_source_atom_graph != 0, bincount_source_atom_graph.new_ones(1)) + bincount_target_atom_graph = torch.bincount(target_index) + bincount_target_atom_graph = bincount_target_atom_graph.where(bincount_target_atom_graph != 0, bincount_target_atom_graph.new_ones(1)) + atom_graph_dict['source_bincount'] = bincount_source_atom_graph + atom_graph_dict['target_bincount'] = bincount_target_atom_graph + + line_graph_dict['line_graph'] = batch_line_graph_compress + if len(line_graph_dict['line_graph']) != 0: + line_graph_dict['atom_list'] = line_graph_atom_index # [angles, 3] + line_graph_dict['target_index'] = line_graph_UDE_target_index # [angles] + line_graph_dict['source_index'] = line_graph_UDE_source_index # [angles] + line_graph_dict['target_DE_index'] = line_graph_DE_target_index # [angles] + line_graph_dict['source_DE_index'] = line_graph_DE_source_index # [angles] + + bincount_source_line_graph = torch.bincount(line_graph_UDE_source_index) + bincount_source_line_graph = bincount_source_line_graph.where(bincount_source_line_graph != 0, bincount_source_line_graph.new_ones(1)) + bincount_target_line_graph = torch.bincount(line_graph_UDE_target_index) + bincount_target_line_graph = bincount_target_line_graph.where(bincount_target_line_graph != 0, bincount_target_line_graph.new_ones(1)) + line_graph_dict['source_bincount'] = bincount_source_line_graph + line_graph_dict['target_bincount'] = bincount_target_line_graph + + batched_graph['atom_graph_dict'] = atom_graph_dict + batched_graph['line_graph_dict'] = line_graph_dict + + return batched_graph + diff --git a/models/matris/matris/model/readout.py b/models/matris/matris/model/readout.py new file mode 100644 index 0000000000000000000000000000000000000000..37dec37d2ab5f7081767bdbd2e9cc5b93f3c018c --- /dev/null +++ b/models/matris/matris/model/readout.py @@ -0,0 +1,334 @@ +from __future__ import annotations + +import torch +from torch import Tensor, nn +from typing import Literal, Union, Sequence + +from .functions import ( + MLP, + GatedMLP, + SwishLayer, + GraphPooling, + reshape_stress, + Sphere, + aggregate, + get_activation, +) + +class EnergyHead(nn.Module): + def __init__( + self, + feat_dim: int = 128, + hidden_dim: Union[int, Sequence[int]] = 128, + output_dim: int = 1, + mlp_type: str = "GateMLP", + activation_type: str = "silu", + ): + """ + Args: + feat_dim : Dimension of the input node (atom) features. + hidden_dim : Hidden layer size(s). + output_dim : Output dimension (usually 1 for energy). + mlp_type : Type of MLP to use: "MLP", "GateMLP" or "MOE". + activation_type : Activation function to use: "silu", "relu", "tanh", "gelu". + """ + super().__init__() + + # Ensure hidden_dim is a list of ints + if isinstance(hidden_dim, int): + hidden_dims = [hidden_dim] + else: + hidden_dims = list(hidden_dim) + + if mlp_type.lower() == "mlp": + self.energy_head = MLP( + input_dim=feat_dim, + hidden_dim=hidden_dims, + output_dim=output_dim, + activation=activation_type, + ) + elif mlp_type.lower() == "gatemlp": + self.energy_head = nn.Sequential( + GatedMLP( + input_dim=feat_dim, + hidden_dim=hidden_dims, + output_dim=hidden_dims[-1], + norm_type="layer", + activation=activation_type, + ), + nn.Linear(in_features=hidden_dims[-1], out_features=output_dim), + ) + else: + raise NotImplementedError(f"Unknown mlp_type: {mlp_type}") + + self.pooling = GraphPooling(average=False) + + def forward(self, batch_graph, node_feat: Tensor) -> Tensor: + """Compute the total energy per graph/batch from atomic features. + + Args: + atom_feat : Tensor(N_atoms, feat_dim) – atomic feature vectors. + batch_index : Tensor(N_atoms,) – graph index for each atom. + """ + energies = self.energy_head(node_feat) # (N_atoms, output_dim) + total_energy = self.pooling(energies, batch_graph['atom_segment']).view(-1) # (N_graphs,) + return total_energy + +class MagmomHead(nn.Module): + def __init__( + self, + feat_dim: int = 128, + hidden_dim: Union[int, Sequence[int]] = 128, + output_dim: int = 1, + mlp_type: str = "GateMLP", + activation_type: Literal["silu", "relu", "tanh", "gelu"] = "silu", + ): + """ + Args: + feat_dim : Dimension of the input node (atom) features. + hidden_dim : Hidden layer size(s). + output_dim : Output dimension. + mlp_type : Type of MLP to use: "MLP", "GateMLP" or "MOE". + activation_type : Activation function to use: "silu", "relu", "tanh", "gelu". + """ + super().__init__() + + # Ensure hidden_dim is a list of ints + if isinstance(hidden_dim, int): + hidden_dims = [hidden_dim] + else: + hidden_dims = list(hidden_dim) + + if mlp_type.lower() == "mlp": + self.magmom_head = MLP( + input_dim=feat_dim, + hidden_dim=hidden_dims, + output_dim=output_dim, + activation=activation_type, + ) + elif mlp_type.lower() == "gatemlp": + self.magmom_head = nn.Sequential( + GatedMLP( + input_dim=feat_dim, + hidden_dim=hidden_dims, + output_dim=hidden_dims[-1], + norm_type="layer", + activation=activation_type, + ), + nn.Linear(in_features=hidden_dims[-1], out_features=output_dim), + ) + else: + raise NotImplementedError(f"Unknown mlp_type: {mlp_type}") + + def forward(self, batch_graph, node_feat: Tensor) -> Tensor: + """Compute the magmom per atom from atomic features. + + Args: + atom_feat : Tensor, + batch_index : List(N_graphs,) - atom numbers for per graph. + """ + + magmom_feat = torch.abs(self.magmom_head(node_feat)) # [N_atoms, 1] + magmoms = torch.split(magmom_feat.view(-1), batch_graph['atoms_per_graph']) # Tuple + return list(magmoms) # [N_atoms] + + +class ForceStressHead(nn.Module): + def __init__( + self, + is_conservation: bool = True, + feat_dim: int = 128, + hidden_dim: Union[int, Sequence[int]] = 128, + output_dim: int = 3, + mlp_type: str = "GateMLP", + activation_type: str = "silu", + ): + """ + Args: + is_conservation (bool): using 'direct' or 'autograd(cons)' method. + feat_dim (int): edge feat dim. + hidden_dim (int): MLP hidden dim. + output_dim (int): output dim, force label should be 3. + mlp_type (str): MLP type. + Can be "MLP", "GateMLP". + activation_type (str): activation type. + Can be "SiLU", "Sigmoid"... See fucntion.py for more informations. + """ + super().__init__() + self.is_conservation = is_conservation + # Ensure hidden_dim is a list of ints + if isinstance(hidden_dim, int): + hidden_dims = [hidden_dim] + else: + hidden_dims = list(hidden_dim) + + if not self.is_conservation: + self.stress_head = EquivariantHead( + feas_dim=feat_dim, + lmax=2, + use_bias=False, + activation=activation_type, + ) + if mlp_type.lower() == "mlp": + self.force_head = MLP( + input_dim=feat_dim, + hidden_dim=hidden_dims, + output_dim=output_dim, + activation=activation_type, + ) + elif mlp_type.lower() == "gatemlp": + self.force_head = nn.Sequential( + GatedMLP( + input_dim=feat_dim, + hidden_dim=hidden_dims, + output_dim=hidden_dims[-1], + norm_type="layer", + activation=activation_type, + ), + nn.Linear(in_features=hidden_dims[-1], out_features=output_dim), + ) + else: + raise NotImplementedError(f"Unknown mlp_type: {mlp_type}") + + def forward(self, + batch_graph, + compute_force: bool = True, + compute_stress: bool = True, + total_energy: Tensor = None, + node_feat: Tensor = None, + edge_feat: Tensor = None, + is_training: bool=False): + predict = {} + if self.is_conservation: + assert total_energy is not None + if compute_force and compute_stress: + grad = torch.autograd.grad( + total_energy.sum(), [batch_graph['batch_cart_coords'], batch_graph['batch_strains']], + create_graph=is_training, retain_graph=is_training + ) + # force + force = -1 * grad[0] + predict["f"] = torch.split(force, batch_graph['atoms_per_graph']) + # stress + stress = grad[1] + scale = 1 / batch_graph['volumes'] * 160.21766208 # units.GPa + stress = stress * scale + stress = list(torch.unbind(stress, dim=0)) + predict["s"] = stress + elif compute_force: + grad = torch.autograd.grad( + total_energy.sum(), [batch_graph['batch_cart_coords']], + create_graph=is_training, retain_graph=is_training + ) + force = -1 * grad[0] + predict["f"] = torch.split(force, batch_graph['atoms_per_graph']) + else: + if compute_stress: + assert node_feat is not None + atom_coord = batch_graph['batch_cart_coords'] / ((torch.norm(batch_graph['batch_cart_coords'], dim=1)*(1-1e-6) + 1e-6).unsqueeze(1)) + stress = self.stress_head(node_feat, atom_coord) # [atoms, 1+3+5] + stress = aggregate(stress, batch_graph['atom_segment'], average=True) #[graphs , 1+3+5] + + # Reshape + isotropic = stress[:, 0] # L=0 + anisotropic = stress[:, [4,5,6,7,8]] # L=2 + stress = reshape_stress(isotropic, anisotropic, stress.shape[0]) #[graphs, 3, 3] + scale = (1 / batch_graph['volumes'] * 160.21766208) + stress = stress * scale + stress = list(torch.unbind(stress, dim=0)) + predict["s"] = stress + if compute_force: + assert edge_feat is not None + direct_edge_feas = torch.index_select(edge_feat, 0, batch_graph['directed2undirected']) + direct_edge_feas = self.force_head(direct_edge_feas) + direct_edge_feas = direct_edge_feas * batch_graph['unit_edge_vectors'] + + force = aggregate( + direct_edge_feas, batch_graph['atom_graph_dict']['target_index'], + average=False, + num_segment=len(node_feat) + ) # [N_atoms, 3] + predict["f"] = torch.split(force, batch_graph['atoms_per_graph']) + + return predict + + +class GatedEquivariantBlock(nn.Module): + """Gated Equivariant Block as defined in Schütt et al. (2021): + Equivariant message passing for the prediction of tensorial properties and molecular spectra + """ + + def __init__( + self, + hidden_channels, + out_channels, + activation="silu", + ): + super(GatedEquivariantBlock, self).__init__() + self.out_channels = out_channels + + self.vec1_proj = nn.Linear(hidden_channels, hidden_channels, bias=False) + self.vec2_proj = nn.Linear(hidden_channels, out_channels, bias=False) + + self.act = get_activation(activation) + self.update_net = nn.Sequential( + nn.Linear(hidden_channels*2, hidden_channels), + self.act, + nn.Linear(hidden_channels, out_channels*2), + ) + + def forward(self, feas, vec_feas): + # feas: [N_atoms, dim] + # vec_feas: [N_atoms, 3, dim] + vec1 = torch.norm(self.vec1_proj(vec_feas), dim=-2) # [N_atoms, dim] + + vec2 = self.vec2_proj(vec_feas) + + feas = torch.cat([feas, vec1], dim=-1) + feas, vec_feas = torch.split(self.update_net(feas), self.out_channels, dim=-1) + vec_feas = vec_feas.unsqueeze(1) * vec2 + + if self.act is not None: + feas = self.act(feas) + return feas, vec_feas + +class EquivariantHead(nn.Module): + def __init__( + self, + feas_dim: int = 64, + lmax: int = 1, + use_bias: bool = False, + activation: str = "silu", + ): + super().__init__() + assert lmax<=2, "Only support lmax <= 2" + + self.sh = Sphere(lmax = lmax) + self.feas_wise_linear = nn.Linear(feas_dim, feas_dim, bias=False) + self.basis_wise_linear = nn.Linear(1, feas_dim, bias=False) + + self.equivariant_block = nn.ModuleList( + [ + GatedEquivariantBlock( + feas_dim, + feas_dim// 2, + activation=activation, + ), + GatedEquivariantBlock(feas_dim // 2, 1, activation=activation), + ] + ) + + def forward(self, feas, vec): + # feas: [N_atoms, dim] + # vec: [N_atoms, 3] + sh_basis = self.sh(vec) # [N_atoms, ( (lmax+1) ** 2) - 1] + # reshape: [edges, (lmax+1)**2 - 1] -> [N_atoms, (lmax+1)**2 - 1, 1] + sh_basis = sh_basis.unsqueeze(2) + vec_feas = self.basis_wise_linear(sh_basis) #[N_atoms, 3, dim] + + feas = self.feas_wise_linear(feas) # [N_atoms, dim] + + for layer in self.equivariant_block: + feas, vec_feas = layer(feas, vec_feas) + + return vec_feas.squeeze() #[N_atoms, 3] \ No newline at end of file diff --git a/models/matris/matris/model/reference_energy.py b/models/matris/matris/model/reference_energy.py new file mode 100644 index 0000000000000000000000000000000000000000..87877324e6ee7868f14d0b6bfb18348311ae55ae --- /dev/null +++ b/models/matris/matris/model/reference_energy.py @@ -0,0 +1,209 @@ +""" Modified from CHGNet: https://github.com/CederGroupHub/chgnet """ + +import collections + +import numpy as np +import torch +from pymatgen.core import Structure +from torch import Tensor, nn + +from collections.abc import Sequence +from ..graph.radiusgraph import RadiusGraph + + + +class AtomRef(nn.Module): + """A linear regression for elemental energy. + From: https://github.com/materialsvirtuallab/m3gnet/. + """ + + def __init__(self, + reference_energy: str = None, + is_intensive: bool = True, + max_num_elements: int = 94) -> None: + + super().__init__() + self.is_intensive = is_intensive + self.max_num_elements = max_num_elements + self.fc = nn.Linear(max_num_elements, 1, bias=False) + self.fitted = False + + self.initialize_from(reference_energy) + for param in self.parameters(): + param.requires_grad = False + + def forward(self, graphs: list[RadiusGraph]): + """Get the energy of a list of CrystalGraphs. + + Args: + graphs (List(RadiusGraph)): a list of Crystal Graph to compute + + Returns: + energy (tensor) + """ + composition_feas = [] + for graph in graphs: + composition_fea = torch.bincount( + graph.atomic_number - 1, minlength=self.max_num_elements + ) + if self.is_intensive: + n_atom = graph.atomic_number.shape[0] + composition_fea = composition_fea / n_atom + composition_feas.append(composition_fea) + composition_feas = torch.stack(composition_feas, dim=0).float() + + ref_energy = self.fc(composition_feas).view(-1) + return ref_energy + + def fit( + self, + structures_or_graphs: Sequence[Structure | RadiusGraph], + energies: Sequence[float], + ) -> None: + """Fit the model to a list of crystals and energies. + + Args: + structures_or_graphs (list[Structure | RadiusGraph]): Any iterable of + pymatgen structures and/or graphs. + energies (list[float]): Target energies. + """ + num_data = len(energies) + composition_feas = torch.zeros([num_data, self.max_num_elements]) + e = torch.zeros([num_data]) + for index, (structure, energy) in enumerate( + zip(structures_or_graphs, energies) + ): + if isinstance(structure, Structure): + atomic_number = torch.tensor( + [site.specie.Z for site in structure], + dtype=int, + requires_grad=False, + ) + else: + atomic_number = structure.atomic_number + composition_fea = torch.bincount( + atomic_number - 1, minlength=self.max_num_elements + ) + if self.is_intensive: + composition_fea = composition_fea / atomic_number.shape[0] + composition_feas[index, :] = composition_fea + e[index] = energy + + # Use numpy for pinv + self.feature_matrix = composition_feas.detach().numpy() + self.energies = e.detach().numpy() + state_dict = collections.OrderedDict() + weight = ( + np.linalg.pinv(self.feature_matrix.T @ self.feature_matrix) + @ self.feature_matrix.T + @ self.energies + ) + state_dict["weight"] = torch.tensor(weight).view(1, 94) + self.fc.load_state_dict(state_dict) + self.fitted = True + + def initialize_from(self, dataset: str): + """Initialize pre-fitted weights from a dataset.""" + dataset = dataset.lower() + if dataset in ["mptrj", "demo"]: + self.initialize_from_MPtrj() + elif dataset in ["omat"]: + self.initialize_from_OMat() + elif dataset in ["mpa"]: + self.initialize_from_MPA() + else: + raise NotImplementedError(f"{dataset=} not supported yet") + + def initialize_from_MPtrj(self): + """Initialize refernece energy of MPtrj(uncorrected energy).""" + state_dict = collections.OrderedDict() + state_dict["weight"] = torch.tensor( + [ + -3.4631e+00, -2.7309e-01, -2.8321e+00, -3.6718e+00, -7.6282e+00, + -8.3344e+00, -7.5666e+00, -7.4720e+00, -5.0677e+00, -3.0058e-02, + -2.0124e+00, -1.6125e+00, -4.4889e+00, -6.3631e+00, -6.4289e+00, + -5.2847e+00, -3.0972e+00, -6.3163e-02, -1.7411e+00, -3.7065e+00, + -6.8949e+00, -9.5306e+00, -8.7439e+00, -9.0234e+00, -8.4526e+00, + -7.4091e+00, -6.1867e+00, -5.4593e+00, -3.3224e+00, -8.2911e-01, + -3.2546e+00, -4.9264e+00, -4.6854e+00, -4.5014e+00, -2.7020e+00, + 7.7055e-01, -1.6062e+00, -3.4319e+00, -7.7220e+00, -9.6074e+00, + -1.0678e+01, -9.2717e+00, -6.8685e+00, -8.2849e+00, -7.0391e+00, + -5.0754e+00, -1.7952e+00, -3.5316e-01, -2.5871e+00, -3.9603e+00, + -3.9316e+00, -3.6352e+00, -2.1676e+00, 2.7399e+00, -2.1183e+00, + -3.9493e+00, -7.1591e+00, -7.4358e+00, -6.5724e+00, -6.7311e+00, + -5.1406e+00, -6.7412e+00, -1.1751e+01, -1.6219e+01, -6.4131e+00, + -6.4793e+00, -6.3707e+00, -6.3809e+00, -6.3572e+00, -2.8174e+00, + -6.4382e+00, -1.0711e+01, -1.2433e+01, -1.0198e+01, -1.0641e+01, + -9.1970e+00, -8.2220e+00, -6.0345e+00, -2.7692e+00, 6.6550e-01, + -1.5987e+00, -3.2421e+00, -3.4098e+00, 1.0903e-16, 6.1043e-16, + 1.2149e-15, 3.4812e-16, -3.3371e-18, -4.2623e+00, -9.0547e+00, + -1.0285e+01, -1.2643e+01, -1.2739e+01, -1.4317e+01 + ] + ).view([1, 94]) + + self.fc.load_state_dict(state_dict) + self.is_intensive = True + self.fitted = True + + def initialize_from_OMat(self): + """Initialize refernece energy of Omat24.""" + state_dict = collections.OrderedDict() + state_dict["weight"] = torch.tensor( + [ + -3.0332e+00, 2.7781e-02, -2.0769e+00, -2.8506e+00, -5.5714e+00, + -6.9814e+00, -7.5916e+00, -6.8493e+00, -4.5307e+00, 3.7653e-01, + -1.2547e+00, -1.2771e+00, -3.5325e+00, -5.1621e+00, -5.2792e+00, + -5.1759e+00, -2.9546e+00, 9.4408e-02, -5.2747e-01, -1.9239e+00, + -6.3525e+00, -7.7363e+00, -8.1060e+00, -8.0094e+00, -7.9600e+00, + -6.9398e+00, -5.9301e+00, -4.8372e+00, -2.9134e+00, -9.2360e-01, + -2.8440e+00, -4.3353e+00, -4.5904e+00, -4.3398e+00, -2.4037e+00, + 2.3849e-01, -9.0706e-01, -1.9711e+00, -6.5034e+00, -8.4291e+00, + -9.0198e+00, -9.1690e+00, -8.9550e+00, -8.0029e+00, -6.8800e+00, + -5.2120e+00, -2.0796e+00, -3.0074e-01, -2.2362e+00, -3.7322e+00, + -3.9127e+00, -3.5653e+00, -1.9106e+00, 3.2219e+00, -9.6049e-01, + -2.0196e+00, -5.0013e+00, -6.5140e+00, -4.8854e+00, -4.8524e+00, + -4.9177e+00, -4.7900e+00, -1.0408e+01, -1.3873e+01, -4.7775e+00, + -4.5076e+00, -4.4782e+00, -4.4732e+00, -4.4918e+00, -4.9103e+00, + -4.4665e+00, -9.2834e+00, -1.0675e+01, -1.0502e+01, -1.0141e+01, + -9.1863e+00, -7.9159e+00, -6.0584e+00, -3.0528e+00, 1.7787e-01, + -1.9486e+00, -3.1409e+00, -3.5926e+00, 1.2319e-13, -3.4435e-13, + 2.9553e-12, 8.9567e-16, -2.9915e-17, -4.4779e+00, -7.9138e+00, + -1.0146e+01, -1.2389e+01, -1.3565e+01, -1.4339e+01 + ] + ).view([1, 94]) + + self.fc.load_state_dict(state_dict) + self.is_intensive = True + self.fitted = True + + def initialize_from_MPA(self): + """Initialize refernece energy of sAlex+MPtrj.""" + state_dict = collections.OrderedDict() + state_dict["weight"] = torch.tensor( + [ + -3.1737e+00, -2.6391e-01, -2.2369e+00, -3.4445e+00, -6.8277e+00, + -8.1473e+00, -8.8195e+00, -7.5632e+00, -5.0891e+00, 1.1973e-03, + -1.5680e+00, -1.6372e+00, -4.0944e+00, -5.8544e+00, -6.0222e+00, + -5.5525e+00, -3.0572e+00, -3.5725e-02, -8.9308e-01, -2.4140e+00, + -6.4643e+00, -8.3088e+00, -8.9037e+00, -8.8312e+00, -8.5999e+00, + -7.8178e+00, -6.7030e+00, -5.7014e+00, -3.7271e+00, -1.2587e+00, + -3.3469e+00, -5.0977e+00, -5.1029e+00, -4.7616e+00, -2.5345e+00, + 7.0565e-01, -1.3512e+00, -2.0171e+00, -6.4545e+00, -8.6960e+00, + -9.9447e+00, -9.8603e+00, -9.6430e+00, -8.9905e+00, -7.6219e+00, + -5.5586e+00, -2.4417e+00, -7.8289e-01, -2.6984e+00, -4.1773e+00, + -4.2378e+00, -3.8948e+00, -2.3448e+00, 2.6678e+00, -1.4716e+00, + -2.3332e+00, -5.1416e+00, -6.5570e+00, -4.8068e+00, -4.7959e+00, + -4.8117e+00, -4.7422e+00, -1.0256e+01, -1.3897e+01, -4.5928e+00, + -4.5515e+00, -4.5212e+00, -4.5335e+00, -4.4311e+00, -2.4671e+00, + -4.7002e+00, -1.0072e+01, -1.1422e+01, -1.1575e+01, -1.1294e+01, + -1.0299e+01, -8.8652e+00, -6.5876e+00, -3.3634e+00, -1.2897e-01, + -2.0918e+00, -3.4916e+00, -3.9858e+00, 1.0642e-14, 1.1423e-16, + -2.4249e-15, 6.9084e-17, -5.6083e-18, -4.4531e+00, -7.6120e+00, + -9.9345e+00, -1.1656e+01, -1.2598e+01, -1.4325e+01 + ] + ).view([1, 94]) + + self.fc.load_state_dict(state_dict) + self.is_intensive = True + self.fitted = True + \ No newline at end of file diff --git a/models/matris/matris_dataset.py b/models/matris/matris_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..3b67c96fc99f2d81f3861594429b07f735985a0f --- /dev/null +++ b/models/matris/matris_dataset.py @@ -0,0 +1,131 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Any + +import torch +from torch.utils.data import Dataset, DataLoader, random_split +from ase.io import read +from pymatgen.io.ase import AseAtomsAdaptor + +from matris.model.model import MatRIS + + +class MatRISDataset(Dataset): + """ + PyTorch dataset for MatRIS from an extxyz/xyz file readable by ASE. + + Each item returns: + { + "graph": RadiusGraph, + "energy": tensor scalar (total energy), + "forces": tensor [n_atoms, 3], + "stress": tensor [3, 3] or None, + "n_atoms": int, + "idx": int, + } + """ + + def __init__( + self, + xyz_path: str | Path, + model: MatRIS, + preload_atoms: bool = True, + ) -> None: + self.xyz_path = str(xyz_path) + self.model = model + self.adaptor = AseAtomsAdaptor() + + if preload_atoms: + self.atoms_list = read(self.xyz_path, index=":") + else: + # kept for future extension, but ASE random access in xyz is not efficient + self.atoms_list = read(self.xyz_path, index=":") + + if len(self.atoms_list) == 0: + raise ValueError(f"No structures found in file: {self.xyz_path}") + + def __len__(self) -> int: + return len(self.atoms_list) + + def __getitem__(self, idx: int): + atoms = self.atoms_list[idx] + + try: + structure = self.adaptor.get_structure(atoms) + graph = self.model.graph_converter(structure) + + energy = torch.tensor(float(atoms.get_potential_energy()), dtype=torch.float32) + forces = torch.tensor(atoms.get_forces(), dtype=torch.float32) + + stress = None + try: + stress_arr = atoms.get_stress(voigt=False) + stress = torch.tensor(stress_arr, dtype=torch.float32) + except Exception: + pass + + return { + "graph": graph, + "energy": energy, + "forces": forces, + "stress": stress, + "n_atoms": len(atoms), + "idx": idx, + } + + except Exception as e: + print(f"[SKIP] idx={idx}, formula={atoms.get_chemical_formula()}, error={e}") + return None + + +def matris_collate_fn(batch: list[dict[str, Any] | None]) -> dict[str, Any] | None: + """ + MatRIS-friendly collation. + + Important: + - filters out failed samples returned as None + - graphs remain a Python list of RadiusGraph objects + - forces remain a list because structures may have different atom counts + """ + + batch = [item for item in batch if item is not None] + + if len(batch) == 0: + return None + + has_stress = all(item["stress"] is not None for item in batch) + + out = { + "graphs": [item["graph"] for item in batch], + "energy": torch.stack([item["energy"] for item in batch], dim=0), + "forces": [item["forces"] for item in batch], + "n_atoms": torch.tensor([item["n_atoms"] for item in batch], dtype=torch.long), + "idx": torch.tensor([item["idx"] for item in batch], dtype=torch.long), + } + + if has_stress: + out["stress"] = torch.stack([item["stress"] for item in batch], dim=0) + else: + out["stress"] = None + + return out + + +def move_matris_batch_to_device(batch: dict[str, Any], device: str) -> dict[str, Any]: + """ + Move a collated batch to device. + Graphs must be moved individually since they are custom objects. + """ + batch = dict(batch) + + batch["graphs"] = [g.to(device) for g in batch["graphs"]] + batch["energy"] = batch["energy"].to(device) + batch["forces"] = [f.to(device) for f in batch["forces"]] + batch["n_atoms"] = batch["n_atoms"].to(device) + batch["idx"] = batch["idx"].to(device) + + if batch["stress"] is not None: + batch["stress"] = batch["stress"].to(device) + + return batch \ No newline at end of file diff --git a/models/matris/run_matris.py b/models/matris/run_matris.py new file mode 100644 index 0000000000000000000000000000000000000000..321a08f2661d249aefb62b3023af74cc7380788d --- /dev/null +++ b/models/matris/run_matris.py @@ -0,0 +1,453 @@ +from __future__ import annotations + +import collections +import random +from pathlib import Path + +import numpy as np +import torch +from torch import nn +from torch.utils.data import DataLoader +import wandb + +from matris.model.model import MatRIS +from matris.model.reference_energy import AtomRef +from matris_dataset import MatRISDataset, matris_collate_fn, move_matris_batch_to_device +import argparse +from datetime import datetime +from tqdm import tqdm + + +# ========================= +# Config +# ========================= +REPO_ROOT = Path(__file__).resolve().parents[2] + +TRAIN_PATH = str(REPO_ROOT / "datasets/xyz/train.xyz") +VAL_PATH = str(REPO_ROOT / "datasets/xyz/val.xyz") +SAVE_DIR = REPO_ROOT / "models/matris/checkpoints_matris" + +PROJECT_NAME = "matris-minimal-training" +RUN_NAME = "matris-ef" + +BATCH_SIZE = 1 +NUM_WORKERS = 0 +EPOCHS = 100 +LR = 3e-4 +MIN_LR = 3e-6 +WEIGHT_DECAY = 1e-2 +SEED = 42 + +ENERGY_LOSS_WEIGHT = 1.0 +FORCE_LOSS_WEIGHT = 1.0 + + +SAVE_DIR.mkdir(parents=True, exist_ok=True) + +DEVICE = "cuda" if torch.cuda.is_available() else "cpu" + +E0S_DEFAULT = { + 1: 2.467478445, # H + 6: 8.0273263225, # C + 8: 3.334308855, # O + 22: 4.879036065, # Ti +} + +# NOTE Make sure pretrained changes weights (the model size changes, so that's enough of an gurantee) + +# ========================= +# Utils +# ========================= +def set_seed(seed: int) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + +def build_reference_energy_module(model: MatRIS, e0_dict: dict[int, float]) -> AtomRef: + atom_ref = AtomRef(reference_energy="mptrj", is_intensive=model.is_intensive) + + weight = torch.zeros(94, dtype=torch.float32) + for Z, e0 in e0_dict.items(): + weight[Z - 1] = e0 + + state_dict = collections.OrderedDict() + state_dict["weight"] = weight.view(1, 94) + atom_ref.fc.load_state_dict(state_dict) + + atom_ref.fitted = True + for p in atom_ref.parameters(): + p.requires_grad = False + + return atom_ref + + + +# WHat Matris paper is doing +class WeightedEnergyForcesMAELoss(nn.Module): + def __init__(self, energy_weight: float = 1.0, forces_weight: float = 1.0): + super().__init__() + self.energy_weight = energy_weight + self.forces_weight = forces_weight + + def forward( + self, + model: MatRIS, + batch: dict, + out: dict, + do_eval: bool = False, + ): + n_atoms = out["atoms_per_graph"].float() # [B] + + # NOTE out["e"] is eV/atom if model.is_intensive=True (which it is), else eV total NOTE + if model.is_intensive: + pred_energy_per_atom = out["e"] + pred_total_energy = out["e"] * n_atoms + else: + pred_total_energy = out["e"] + pred_energy_per_atom = pred_total_energy / n_atoms + + ref_total_energy = batch["energy"] # [B], eV total + ref_energy_per_atom = ref_total_energy / n_atoms + + force_losses = [] + for pred_f, ref_f in zip(out["f"], batch["forces"]): + # one scalar per graph: mean over atoms and xyz components + force_losses.append(torch.abs(pred_f - ref_f).mean()) + + loss_forces = torch.stack(force_losses).mean() + + # Per atom, also per graph + loss_energy = torch.mean(torch.abs( + pred_energy_per_atom - ref_energy_per_atom + )) + + # Per system + # per_system_error = torch.abs(pred_total_energy - ref_total_energy) + # loss_energy = torch.sum(per_system_error * n_atoms) / torch.sum(n_atoms) + + + total_loss = ( + self.forces_weight * loss_forces + + self.energy_weight * loss_energy + ) + + return ( + total_loss, + loss_energy, # eV/atom MAE + loss_forces, # force component MAE + pred_total_energy, + ref_total_energy, + ) + +def run_epoch( + model: MatRIS, + loader: DataLoader, + optimizer: torch.optim.Optimizer | None, + scheduler: torch.optim.lr_scheduler._LRScheduler | None, + criterion: WeightedEnergyForcesMAELoss, + device: str, + epoch: int, + split: str, +) -> dict[str, float]: + is_train = optimizer is not None + model.train(is_train) + + running = { + "loss": 0.0, + "energy_mae_per_atom": 0.0, + "force_mae_per_component": 0.0, + "pred_total_energy_mean": 0.0, + "target_total_energy_mean": 0.0, + "ref_energy_mean": 0.0, + "lr": 0.0, + } + + + n_batches = 0 + + for batch in tqdm(loader, desc=f'Running {split}'): + + batch = move_matris_batch_to_device(batch, device=device) + + if is_train: + optimizer.zero_grad() + + out = model(batch["graphs"], task="ef", is_training=is_train) + + loss, energy_loss, force_loss, pred_total_energy, target_total_energy = criterion( + model=model, + batch=batch, + out=out, + do_eval=False, + ) + + if is_train: + loss.backward() + optimizer.step() + + # For logging: force MAE per atom/component over full batch + #pred_forces = torch.cat(out["f"], dim=0) + #ref_forces = torch.cat(batch["forces"], dim=0) + #force_mae_per_component = torch.abs(pred_forces - ref_forces).mean() + + current_lr = optimizer.param_groups[0]["lr"] if is_train else 0.0 + + running["loss"] += loss.detach().item() + running["energy_mae_per_atom"] += energy_loss.detach().item() + running["force_mae_per_component"] += force_loss.detach().item() + running["pred_total_energy_mean"] += pred_total_energy.detach().mean().item() + running["target_total_energy_mean"] += target_total_energy.detach().mean().item() + running["ref_energy_mean"] += out["ref_energy"].detach().mean().item() + running["lr"] += current_lr + + n_batches += 1 + + if n_batches == 0: + raise RuntimeError(f"No batches found in {split} loader.") + + metrics = {k: v / n_batches for k, v in running.items()} + metrics["epoch"] = epoch + + if is_train and scheduler is not None: + scheduler.step() + + wandb.log({f"{split}/{k}": v for k, v in metrics.items()}, step=epoch) + + return metrics + +def parse_args(): + parser = argparse.ArgumentParser(description="Train MatRIS on energy/force data.") + + parser.add_argument("--train_path", type=str, default=TRAIN_PATH) + parser.add_argument("--val_path", type=str, default=VAL_PATH) + + parser.add_argument("--project_name", type=str, default=PROJECT_NAME) + parser.add_argument("--run_name", type=str, default=RUN_NAME) + + parser.add_argument("--batch_size", type=int, default=BATCH_SIZE) + parser.add_argument("--num_workers", type=int, default=NUM_WORKERS) + parser.add_argument("--epochs", type=int, default=EPOCHS) + + parser.add_argument("--lr", type=float, default=LR) + parser.add_argument("--min_lr", type=float, default=MIN_LR) + parser.add_argument("--weight_decay", type=float, default=WEIGHT_DECAY) + + parser.add_argument("--energy_weight", type=float, default=ENERGY_LOSS_WEIGHT) + parser.add_argument("--force_weight", type=float, default=FORCE_LOSS_WEIGHT) + + parser.add_argument("--seed", type=int, default=SEED) + parser.add_argument("--save_dir", type=str, default=str(SAVE_DIR)) + parser.add_argument("--device", type=str, default=DEVICE) + + parser.add_argument( + "--is_conservative", + action=argparse.BooleanOptionalAction, + default=True, + help="Use conservative forces: F = -dE/dx. Use --no-is_conservative for direct force head.", + ) + + parser.add_argument( + "--use_reference_energy", + action=argparse.BooleanOptionalAction, + default=True, + ) + + parser.add_argument( + "--model_name", + type=str, + default=None, + help="Optional pretrained model name, e.g. matris_10m_oam. If None, trains MatRIS from scratch.", + ) + + return parser.parse_args() + +# ========================= +# Main +# ========================= +if __name__ == "__main__": + args = parse_args() + + set_seed(args.seed) + + timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + + run_dir = Path(args.save_dir) / f"{args.run_name}_{timestamp}" + run_dir.mkdir(parents=True, exist_ok=True) + + print(f"Saving checkpoints to: {run_dir}") + + wandb.init( + project=args.project_name, + name=args.run_name, + config=vars(args) | { + "scheduler": "CosineAnnealingLR", + "loss": "forces_mae + energy_mae_per_atom", + }, + ) + + + if args.model_name is None or args.model_name == "none": + print("Training from scratch") + model = MatRIS(is_conservation=args.is_conservative).to(args.device) + else: + print(f"Starting from foundation model: {args.model_name}") + model = MatRIS.load(args.model_name, + device=args.device, + cache_dir="foundation_models",) + # override loaded setting if needed + model.force_stress_head.is_conservation = args.is_conservative + + + if args.use_reference_energy: + model.reference_energy = build_reference_energy_module(model, E0S_DEFAULT).to(args.device) + else: + model.reference_energy = None + + print("Loaded config is_conservation:", model.config.get("is_conservation")) + + train_dataset = MatRISDataset(args.train_path, model=model) + val_dataset = MatRISDataset(args.val_path, model=model) + + #train_dataset = CachedMatRISDataset(args.train_path) + #val_dataset = CachedMatRISDataset(args.val_path) + + train_loader = DataLoader( + train_dataset, + batch_size=args.batch_size, + shuffle=True, + num_workers=args.num_workers, + collate_fn=matris_collate_fn, + ) + + val_loader = DataLoader( + val_dataset, + batch_size=args.batch_size, + shuffle=False, + num_workers=args.num_workers, + collate_fn=matris_collate_fn, + ) + + + optimizer = torch.optim.AdamW( + model.parameters(), + lr=args.lr, + weight_decay=args.weight_decay, + ) + + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, + T_max=args.epochs, + eta_min=args.min_lr, + ) + + criterion = WeightedEnergyForcesMAELoss( + energy_weight=args.energy_weight, + forces_weight=args.force_weight, + ) + + #best_val_loss = float("inf") + + print(f"Using device: {args.device}") + print(f"Conservative forces: {args.is_conservative}") + print(f"Train size: {len(train_dataset)}") + print(f"Val size: {len(val_dataset)}") + + best_checkpoints = [] # [{"path": Path, "val_loss": float, "epoch": int}] + MAX_BEST_CKPTS = 5 + + for epoch in tqdm(range(1, args.epochs + 1), desc=f'Epoch'): + train_metrics = run_epoch( + model=model, + loader=train_loader, + optimizer=optimizer, + scheduler=scheduler, + criterion=criterion, + device=args.device, + epoch=epoch, + split="train", + ) + + val_metrics = run_epoch( + model=model, + loader=val_loader, + optimizer=None, + scheduler=None, + criterion=criterion, + device=args.device, + epoch=epoch, + split="val", + ) + + current_lr = optimizer.param_groups[0]["lr"] + + print( + f"Epoch {epoch:03d} | " + f"lr={current_lr:.2e} | " + f"train/loss={train_metrics['loss']:.6f} | " + f"train/e_per_atom={train_metrics['energy_mae_per_atom']:.6f} | " + f"train/f_per_component={train_metrics['force_mae_per_component']:.6f} | " + f"val/loss={val_metrics['loss']:.6f} | " + f"val/e_per_atom={val_metrics['energy_mae_per_atom']:.6f} | " + f"val/f_per_component={val_metrics['force_mae_per_component']:.6f}" + ) + + wandb.log({"epoch": epoch, "lr": current_lr}, step=epoch) + + ckpt = { + "epoch": epoch, + "model_state_dict": model.state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + "scheduler_state_dict": scheduler.state_dict(), + "train_metrics": train_metrics, + "val_metrics": val_metrics, + "args": vars(args), + } + + val_loss = val_metrics["loss"] + val_e = val_metrics["energy_mae_per_atom"] + val_f = val_metrics["force_mae_per_component"] + + ckpt_name = ( + f"best_epoch={epoch:04d}" + f"_val_loss={val_loss:.6f}" + f"_val_e_atom={val_e:.6f}" + f"_val_f_comp={val_f:.6f}.pt" + ) + + ckpt_path = run_dir / ckpt_name + + should_save = False + + if len(best_checkpoints) < MAX_BEST_CKPTS: + should_save = True + else: + worst_ckpt = max(best_checkpoints, key=lambda x: x["val_loss"]) + + if val_loss < worst_ckpt["val_loss"]: + if worst_ckpt["path"].exists(): + worst_ckpt["path"].unlink() + + best_checkpoints.remove(worst_ckpt) + should_save = True + + if should_save: + torch.save(ckpt, ckpt_path) + + best_checkpoints.append( + { + "path": ckpt_path, + "val_loss": val_loss, + "epoch": epoch, + } + ) + + best_checkpoints = sorted(best_checkpoints, key=lambda x: x["val_loss"]) + + print(f"Saved top-{MAX_BEST_CKPTS} checkpoint: {ckpt_name}") + + wandb.run.summary["best_val_loss"] = best_checkpoints[0]["val_loss"] + wandb.run.summary["best_epoch"] = best_checkpoints[0]["epoch"] + + wandb.finish() \ No newline at end of file diff --git a/models/matris/run_matris_calc.py b/models/matris/run_matris_calc.py new file mode 100644 index 0000000000000000000000000000000000000000..90bdcf97bc492e4fdd406f9d8120763a6e0fdf66 --- /dev/null +++ b/models/matris/run_matris_calc.py @@ -0,0 +1,140 @@ +from __future__ import annotations + +from pathlib import Path + +import torch +from ase.io import read +from ase.optimize import BFGS +from ase.calculators.calculator import Calculator, all_changes +from pymatgen.io.ase import AseAtomsAdaptor + +from matris.model.model import MatRIS + +import collections +from matris.model.reference_energy import AtomRef + + +E0S_DEFAULT = { + 1: 2.467478445, + 6: 8.0273263225, + 8: 3.334308855, + 22: 4.879036065, +} + + +def build_reference_energy_module(model: MatRIS, e0_dict: dict[int, float]) -> AtomRef: + atom_ref = AtomRef(reference_energy="mptrj", is_intensive=model.is_intensive) + + weight = torch.zeros(94, dtype=torch.float32) + for Z, e0 in e0_dict.items(): + weight[Z - 1] = e0 + + state_dict = collections.OrderedDict() + state_dict["weight"] = weight.view(1, 94) + atom_ref.fc.load_state_dict(state_dict) + + atom_ref.fitted = True + for p in atom_ref.parameters(): + p.requires_grad = False + + return atom_ref + + +class MatRISCalculator(Calculator): + implemented_properties = ["energy", "forces"] + + def __init__( + self, + checkpoint: str | None = None, + model_name: str | None = None, + device: str = "cuda", + is_conservative: bool = True, + reference_energy=None, + **kwargs, + ): + super().__init__(**kwargs) + + self.device = device + self.adaptor = AseAtomsAdaptor() + + if checkpoint is not None: + print(f"Loading MatRIS checkpoint: {checkpoint}") + ckpt = torch.load(checkpoint, map_location="cpu", weights_only=False) + + self.model = MatRIS(is_conservation=is_conservative) + + # Must exist before loading if checkpoint contains reference_energy.fc.weight + self.model.reference_energy = build_reference_energy_module(self.model, E0S_DEFAULT) + + self.model.load_state_dict(ckpt["model_state_dict"], strict=True) + self.model = self.model.to(device) + + elif model_name is not None: + print(f"Loading MatRIS foundation model: {model_name}") + self.model = MatRIS.load(model_name, device=device) + + else: + raise ValueError("Provide either checkpoint=... or model_name=...") + + if reference_energy is not None: + self.model.reference_energy = reference_energy + + self.model.eval() + + def calculate( + self, + atoms=None, + properties=("energy", "forces"), + system_changes=all_changes, + ): + super().calculate(atoms, properties, system_changes) + + structure = self.adaptor.get_structure(atoms) + graph = self.model.graph_converter(structure).to(self.device) + + # Do NOT use torch.no_grad(), because conservative forces need autograd. + out = self.model([graph], task="ef", is_training=False) + + n_atoms = out["atoms_per_graph"][0].item() + + if self.model.is_intensive: + energy = out["e"][0] * n_atoms + else: + energy = out["e"][0] + + forces = out["f"][0] + + self.results["energy"] = energy.detach().cpu().item() + self.results["forces"] = forces.detach().cpu().numpy() + + +if __name__ == "__main__": + input_structure = "/proj/cvl/users/x_anmka/cataliust_icml/calc_results/run_001/ML_relaxed_POSCAR" + device = "cuda" if torch.cuda.is_available() else "cpu" + + atoms = read(input_structure) + + calc = MatRISCalculator( + # Option 1: your fine-tuned checkpoint + checkpoint="/proj/cvl/users/x_anmka/research/matris/ddp_checkpoints_matris/matris_seed2000_no_cons_ddp_new_loss_higher_weights_2026-05-03_22-02-31/best_epoch=0021_val_loss=0.171674_val_e_atom=0.007562_val_f_comp=0.026773.pt", + + # Option 2 instead: + # model_name="matris_10m_mp", + + device=device, + is_conservative=False, # NOTE CHANGE THIS ONE + reference_energy=None, # or your AtomRef module if needed + ) + + atoms.calc = calc + + opt = BFGS( + atoms, + logfile="matris_relax.log", + trajectory="matris_relax.traj", + ) + + opt.run(fmax=0.02, steps=1000) + + atoms.write("POSCAR_final") + print("Done.") \ No newline at end of file diff --git a/models/matris/run_matris_ddp.py b/models/matris/run_matris_ddp.py new file mode 100644 index 0000000000000000000000000000000000000000..5fedc734471ba8dee5b9edd08acfd57350a9c083 --- /dev/null +++ b/models/matris/run_matris_ddp.py @@ -0,0 +1,541 @@ +from __future__ import annotations + +import argparse +import collections +import os +import random +from datetime import datetime +from pathlib import Path + +import numpy as np +import torch +import torch.distributed as dist +from torch import nn +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler +from tqdm import tqdm +import wandb + +from matris.model.model import MatRIS +from matris.model.reference_energy import AtomRef +from matris_dataset import MatRISDataset, matris_collate_fn, move_matris_batch_to_device + + +TRAIN_PATH = "datasets/trainingset_251216_train.xyz" +VAL_PATH = "datasets/trainingset_251216_val.xyz" + +PROJECT_NAME = "matris-minimal-training" +RUN_NAME = "matris-ef-ddp" + +BATCH_SIZE = 1 +NUM_WORKERS = 0 +EPOCHS = 1000 +LR = 3e-4 +MIN_LR = 3e-6 +WEIGHT_DECAY = 1e-2 +SEED = 42 + +ENERGY_LOSS_WEIGHT = 1.0 +FORCE_LOSS_WEIGHT = 1.0 + +SAVE_DIR = Path("checkpoints_matris") +SAVE_DIR.mkdir(parents=True, exist_ok=True) + +E0S_DEFAULT = { + 1: 2.467478445, + 6: 8.0273263225, + 8: 3.334308855, + 22: 4.879036065, +} + + +def setup_ddp(): + dist.init_process_group(backend="nccl") + local_rank = int(os.environ["LOCAL_RANK"]) + rank = int(os.environ["RANK"]) + world_size = int(os.environ["WORLD_SIZE"]) + torch.cuda.set_device(local_rank) + device = f"cuda:{local_rank}" + return local_rank, rank, world_size, device + + +def cleanup_ddp(): + if dist.is_initialized(): + dist.destroy_process_group() + + +def is_main_process(): + return (not dist.is_initialized()) or dist.get_rank() == 0 + + +def reduce_mean(value: float, device: str) -> float: + tensor = torch.tensor(value, device=device, dtype=torch.float32) + dist.all_reduce(tensor, op=dist.ReduceOp.SUM) + tensor /= dist.get_world_size() + return tensor.item() + + +def set_seed(seed: int) -> None: + rank = dist.get_rank() if dist.is_initialized() else 0 + seed = seed + rank + + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + +def unwrap_model(model): + return model.module if isinstance(model, DDP) else model + + +def build_reference_energy_module(model: MatRIS, e0_dict: dict[int, float]) -> AtomRef: + atom_ref = AtomRef(reference_energy="mptrj", is_intensive=model.is_intensive) + + weight = torch.zeros(94, dtype=torch.float32) + for Z, e0 in e0_dict.items(): + weight[Z - 1] = e0 + + state_dict = collections.OrderedDict() + state_dict["weight"] = weight.view(1, 94) + atom_ref.fc.load_state_dict(state_dict) + + atom_ref.fitted = True + for p in atom_ref.parameters(): + p.requires_grad = False + + return atom_ref + + +class WeightedEnergyForcesMAELoss(nn.Module): + def __init__(self, energy_weight: float = 1.0, forces_weight: float = 1.0): + super().__init__() + self.energy_weight = energy_weight + self.forces_weight = forces_weight + + def forward( + self, + model: MatRIS, + batch: dict, + out: dict, + do_eval: bool = False, + ): + n_atoms = out["atoms_per_graph"].float() + + if model.is_intensive: + pred_energy_per_atom = out["e"] + pred_total_energy = out["e"] * n_atoms + else: + pred_total_energy = out["e"] + pred_energy_per_atom = pred_total_energy / n_atoms + + ref_total_energy = batch["energy"] + ref_energy_per_atom = ref_total_energy / n_atoms + + force_losses = [] + for pred_f, ref_f in zip(out["f"], batch["forces"]): + force_losses.append(torch.abs(pred_f - ref_f).mean()) + + loss_forces = torch.stack(force_losses).mean() + + loss_energy = torch.mean( + torch.abs(pred_energy_per_atom - ref_energy_per_atom) + ) + + total_loss = ( + self.forces_weight * loss_forces + + self.energy_weight * loss_energy + ) + + return ( + total_loss, + loss_energy, + loss_forces, + pred_total_energy, + ref_total_energy, + ) + + +def run_epoch( + model: DDP, + loader: DataLoader, + optimizer: torch.optim.Optimizer | None, + scheduler: torch.optim.lr_scheduler._LRScheduler | None, + criterion: WeightedEnergyForcesMAELoss, + device: str, + epoch: int, + split: str, +) -> dict[str, float]: + is_train = optimizer is not None + model.train(is_train) + + raw_model = unwrap_model(model) + + running = { + "loss": 0.0, + "energy_mae_per_atom": 0.0, + "force_mae_per_component": 0.0, + "pred_total_energy_mean": 0.0, + "target_total_energy_mean": 0.0, + "ref_energy_mean": 0.0, + "lr": 0.0, + } + + n_batches = 0 + + iterator = tqdm(loader, desc=f"Running {split}", disable=not is_main_process()) + + for batch in iterator: + if batch is None: + continue + + batch = move_matris_batch_to_device(batch, device=device) + + if is_train: + optimizer.zero_grad(set_to_none=True) + + # Conservative forces need gradients even during validation. + if is_train or raw_model.force_stress_head.is_conservation: + with torch.enable_grad(): + out = model(batch["graphs"], task="ef", is_training=is_train) + else: + with torch.no_grad(): + out = model(batch["graphs"], task="ef", is_training=False) + + loss, energy_loss, force_loss, pred_total_energy, target_total_energy = criterion( + model=raw_model, + batch=batch, + out=out, + do_eval=False, + ) + + if is_train: + loss.backward() + optimizer.step() + + current_lr = optimizer.param_groups[0]["lr"] if is_train else 0.0 + + running["loss"] += loss.detach().item() + running["energy_mae_per_atom"] += energy_loss.detach().item() + running["force_mae_per_component"] += force_loss.detach().item() + running["pred_total_energy_mean"] += pred_total_energy.detach().mean().item() + running["target_total_energy_mean"] += target_total_energy.detach().mean().item() + + ref_energy = out["ref_energy"] + if torch.is_tensor(ref_energy): + running["ref_energy_mean"] += ref_energy.detach().mean().item() + else: + running["ref_energy_mean"] += float(ref_energy) + + running["lr"] += current_lr + n_batches += 1 + + del out, loss, energy_loss, force_loss, pred_total_energy, target_total_energy + + if n_batches == 0: + raise RuntimeError(f"No batches found in {split} loader on rank {dist.get_rank()}.") + + metrics = {k: v / n_batches for k, v in running.items()} + metrics["epoch"] = epoch + + # Average metrics across GPUs. + for k in list(metrics.keys()): + if k != "epoch": + metrics[k] = reduce_mean(metrics[k], device=device) + + if is_train and scheduler is not None: + scheduler.step() + + if is_main_process(): + wandb.log({f"{split}/{k}": v for k, v in metrics.items()}, step=epoch) + + return metrics + + +def parse_args(): + parser = argparse.ArgumentParser(description="Train MatRIS on energy/force data with DDP.") + + parser.add_argument("--train_path", type=str, default=TRAIN_PATH) + parser.add_argument("--val_path", type=str, default=VAL_PATH) + + parser.add_argument("--project_name", type=str, default=PROJECT_NAME) + parser.add_argument("--run_name", type=str, default=RUN_NAME) + + parser.add_argument("--batch_size", type=int, default=BATCH_SIZE) + parser.add_argument("--num_workers", type=int, default=NUM_WORKERS) + parser.add_argument("--epochs", type=int, default=EPOCHS) + + parser.add_argument("--lr", type=float, default=LR) + parser.add_argument("--min_lr", type=float, default=MIN_LR) + parser.add_argument("--weight_decay", type=float, default=WEIGHT_DECAY) + + parser.add_argument("--energy_weight", type=float, default=ENERGY_LOSS_WEIGHT) + parser.add_argument("--force_weight", type=float, default=FORCE_LOSS_WEIGHT) + + parser.add_argument("--seed", type=int, default=SEED) + parser.add_argument("--save_dir", type=str, default=str(SAVE_DIR)) + + parser.add_argument( + "--is_conservative", + action=argparse.BooleanOptionalAction, + default=True, + ) + + parser.add_argument( + "--use_reference_energy", + action=argparse.BooleanOptionalAction, + default=True, + ) + + parser.add_argument( + "--model_name", + type=str, + default=None, + ) + + parser.add_argument( + "--find_unused_parameters", + action=argparse.BooleanOptionalAction, + default=True, + help="Useful if some heads/branches are unused.", + ) + + return parser.parse_args() + + +def main(): + local_rank, rank, world_size, device = setup_ddp() + args = parse_args() + + set_seed(args.seed) + + timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + run_dir = Path(args.save_dir) / f"{args.run_name}_{timestamp}" + + if is_main_process(): + run_dir.mkdir(parents=True, exist_ok=True) + print(f"Saving checkpoints to: {run_dir}") + print(f"World size: {world_size}") + print(f"Per-GPU batch size: {args.batch_size}") + print(f"Effective batch size: {args.batch_size * world_size}") + + wandb.init( + project=args.project_name, + name=args.run_name, + config=vars(args) | { + "scheduler": "CosineAnnealingLR", + "loss": "forces_mae + energy_mae_per_atom", + "world_size": world_size, + "effective_batch_size": args.batch_size * world_size, + }, + ) + + if args.model_name is None or args.model_name == "none": + if is_main_process(): + print("Training from scratch") + model = MatRIS(is_conservation=args.is_conservative) + else: + if is_main_process(): + print(f"Starting from foundation model: {args.model_name}") + model = MatRIS.load( + args.model_name, + device="cpu", + cache_dir="foundation_models", + ) + model.force_stress_head.is_conservation = args.is_conservative + + if args.use_reference_energy: + model.reference_energy = build_reference_energy_module(model, E0S_DEFAULT) + else: + model.reference_energy = None + + model = model.to(device) + + model = DDP( + model, + device_ids=[local_rank], + output_device=local_rank, + find_unused_parameters=args.find_unused_parameters, + ) + + raw_model = unwrap_model(model) + + if is_main_process(): + print("Loaded config is_conservation:", raw_model.config.get("is_conservation")) + print("Actual force head is_conservation:", raw_model.force_stress_head.is_conservation) + print("Model is_intensive:", raw_model.is_intensive) + print(f"Using device: {device}") + + train_dataset = MatRISDataset(args.train_path, model=raw_model) + val_dataset = MatRISDataset(args.val_path, model=raw_model) + + train_sampler = DistributedSampler( + train_dataset, + num_replicas=world_size, + rank=rank, + shuffle=True, + drop_last=False, + ) + + val_sampler = DistributedSampler( + val_dataset, + num_replicas=world_size, + rank=rank, + shuffle=False, + drop_last=False, + ) + + train_loader = DataLoader( + train_dataset, + batch_size=args.batch_size, + sampler=train_sampler, + shuffle=False, + num_workers=args.num_workers, + collate_fn=matris_collate_fn, + pin_memory=True, + ) + + val_loader = DataLoader( + val_dataset, + batch_size=args.batch_size, + sampler=val_sampler, + shuffle=False, + num_workers=args.num_workers, + collate_fn=matris_collate_fn, + pin_memory=True, + ) + + optimizer = torch.optim.AdamW( + model.parameters(), + lr=args.lr, + weight_decay=args.weight_decay, + ) + + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, + T_max=args.epochs, + eta_min=args.min_lr, + ) + + criterion = WeightedEnergyForcesMAELoss( + energy_weight=args.energy_weight, + forces_weight=args.force_weight, + ) + + if is_main_process(): + print(f"Conservative forces: {args.is_conservative}") + print(f"Train size: {len(train_dataset)}") + print(f"Val size: {len(val_dataset)}") + + best_checkpoints = [] + MAX_BEST_CKPTS = 5 + + for epoch in tqdm( + range(1, args.epochs + 1), + desc="Epoch", + disable=not is_main_process(), + ): + train_sampler.set_epoch(epoch) + + train_metrics = run_epoch( + model=model, + loader=train_loader, + optimizer=optimizer, + scheduler=scheduler, + criterion=criterion, + device=device, + epoch=epoch, + split="train", + ) + + val_metrics = run_epoch( + model=model, + loader=val_loader, + optimizer=None, + scheduler=None, + criterion=criterion, + device=device, + epoch=epoch, + split="val", + ) + + current_lr = optimizer.param_groups[0]["lr"] + + if is_main_process(): + print( + f"Epoch {epoch:03d} | " + f"lr={current_lr:.2e} | " + f"train/loss={train_metrics['loss']:.6f} | " + f"train/e_per_atom={train_metrics['energy_mae_per_atom']:.6f} | " + f"train/f_per_component={train_metrics['force_mae_per_component']:.6f} | " + f"val/loss={val_metrics['loss']:.6f} | " + f"val/e_per_atom={val_metrics['energy_mae_per_atom']:.6f} | " + f"val/f_per_component={val_metrics['force_mae_per_component']:.6f}" + ) + + wandb.log({"epoch": epoch, "lr": current_lr}, step=epoch) + + ckpt = { + "epoch": epoch, + "model_state_dict": raw_model.state_dict(), + "optimizer_state_dict": optimizer.state_dict(), + "scheduler_state_dict": scheduler.state_dict(), + "train_metrics": train_metrics, + "val_metrics": val_metrics, + "args": vars(args), + } + + val_loss = val_metrics["loss"] + val_e = val_metrics["energy_mae_per_atom"] + val_f = val_metrics["force_mae_per_component"] + + ckpt_name = ( + f"best_epoch={epoch:04d}" + f"_val_loss={val_loss:.6f}" + f"_val_e_atom={val_e:.6f}" + f"_val_f_comp={val_f:.6f}.pt" + ) + + ckpt_path = run_dir / ckpt_name + + should_save = False + + if len(best_checkpoints) < MAX_BEST_CKPTS: + should_save = True + else: + worst_ckpt = max(best_checkpoints, key=lambda x: x["val_loss"]) + if val_loss < worst_ckpt["val_loss"]: + if worst_ckpt["path"].exists(): + worst_ckpt["path"].unlink() + best_checkpoints.remove(worst_ckpt) + should_save = True + + if should_save: + torch.save(ckpt, ckpt_path) + + best_checkpoints.append( + { + "path": ckpt_path, + "val_loss": val_loss, + "epoch": epoch, + } + ) + + best_checkpoints = sorted(best_checkpoints, key=lambda x: x["val_loss"]) + + print(f"Saved top-{MAX_BEST_CKPTS} checkpoint: {ckpt_name}") + + wandb.run.summary["best_val_loss"] = best_checkpoints[0]["val_loss"] + wandb.run.summary["best_epoch"] = best_checkpoints[0]["epoch"] + + dist.barrier() + + if is_main_process(): + wandb.finish() + + cleanup_ddp() + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/models/matris/runs/matris_cons_seed2000_epoch43.pt b/models/matris/runs/matris_cons_seed2000_epoch43.pt new file mode 100644 index 0000000000000000000000000000000000000000..749355fc4960d9d52216ac4a4978df1096c2b1fa --- /dev/null +++ b/models/matris/runs/matris_cons_seed2000_epoch43.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:74902d387283b81c278dfb894b69703bda8e0ebe9a5390db0627fa332c1b936b +size 75609726 diff --git a/models/matris/runs/matris_no_cons_seed2000_epoch37.pt b/models/matris/runs/matris_no_cons_seed2000_epoch37.pt new file mode 100644 index 0000000000000000000000000000000000000000..0b8dd81856aef393a82d197a78c9b3e054941ae7 --- /dev/null +++ b/models/matris/runs/matris_no_cons_seed2000_epoch37.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:69aa330c1ed6d058be57a3401aae7b5d0bc4e66b55aed96bb1e6378e89ea37cb +size 76708909 diff 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