"""
Simulaciones OrbMol con interfaz estilo Facebook FAIRChem
"""

from __future__ import annotations
import os
import tempfile
from pathlib import Path
import numpy as np
import ase
import ase.io
from ase import units
from ase.io.trajectory import Trajectory
from ase.optimize import LBFGS
from ase.filters import FrechetCellFilter
from ase.md import MDLogger
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
from ase.md.verlet import VelocityVerlet
from ase.md.nose_hoover_chain import NoseHooverChainNVT

# OrbMol
from orb_models.forcefield import pretrained
from orb_models.forcefield.calculator import ORBCalculator

# -----------------------------
# Global model
# -----------------------------
_model_calc: ORBCalculator | None = None
_current_task_name = None
# Necesario para script de reproducción
model_name = {
    "OMol": "orb_v3_conservative_omol", 
    "OMat": "orb_v3_conservative_inf_omat", 
    "OMol-Direct": "orb_v3_direct_omol"}


def load_orbmol_model(task_name,device: str = "cpu", precision: str = "float32-high") -> ORBCalculator:
    """Load OrbMol calculator, switches only if another model is required."""
    global _model_calc, _current_task_name
    if _model_calc is None or _current_task_name != task_name:
        if task_name == "OMol":
            orbff= pretrained.orb_v3_conservative_omol(
                device=device,
                precision=precision,
            )
        elif task_name == "OMat":
            orbff = pretrained.orb_v3_conservative_inf_omat(
                device=device,
                precision=precision,
            )
        elif task_name == "OMol-Direct":
            orbff = pretrained.orb_v3_direct_omol(
                device=device,
                precision=precision,
            )
        else:
            raise ValueError(f"Unknown task_name: {task_name}")
        _model_calc = ORBCalculator(orbff, device=device)
        _current_task_name = task_name
    return _model_calc

# -----------------------------
# FUNCIONES ESTILO FACEBOOK - COPIADAS EXACTAS
# -----------------------------

def load_check_ase_atoms(structure_file):
    """COPIA EXACTA de Facebook - valida y carga estructura"""
    if not structure_file:
        raise Exception("You need an input structure file to run a simulation!")

    try:
        atoms = ase.io.read(structure_file)

        if not (all(atoms.pbc) or np.all(~np.array(atoms.pbc))):
            raise Exception(
                "Mixed PBC are not supported yet - please set PBC all True or False in your structure before uploading"
            )

        if len(atoms) == 0:
            raise Exception("Error: Structure file contains no atoms.")

        if len(atoms) > 2000:
            raise Exception(
                f"Error: Structure file contains {len(atoms)}, which is more than 2000 atoms. Please use a smaller structure for this demo, or run this on a local machine!"
            )

        # Centrar para visualización
        atoms.positions -= atoms.get_center_of_mass()
        cell_center = atoms.get_cell().sum(axis=0) / 2
        atoms.positions += cell_center

        return atoms
    except Exception as e:
        raise Exception(f"Error loading structure with ASE: {str(e)}")

def run_md_simulation(
    structure_file,
    num_steps,
    num_prerelax_steps,
    md_timestep,
    temperature_k,
    md_ensemble,
    task_name,  
    total_charge=0,
    spin_multiplicity=1,
    explanation: str | None = None,
    oauth_token=None,  # Ignorado
    progress=None,  # Para compatibilidad Gradio
):
    """
    MD simulation estilo Facebook pero con OrbMol
    """
    temp_path = None
    traj_path = None
    md_log_path = None
    atoms = None

    try:
        # Cargar átomos (igual que Facebook)
        atoms = load_check_ase_atoms(structure_file)

        # Configurar charge y spin
        atoms.info["charge"] = total_charge
        atoms.info["spin"] = spin_multiplicity

        # AQUÍ EL CAMBIO: OrbMol en lugar de HFEndpointCalculator
        calc = load_orbmol_model(task_name)
        atoms.calc = calc

        # Progress callback si existe
        interval = 1
        steps = [0]
        expected_steps = num_steps + num_prerelax_steps

        def update_progress():
            steps[-1] += interval
            if progress:
                progress(steps[-1] / expected_steps)

        # Archivos temporales (igual que Facebook)
        with tempfile.NamedTemporaryFile(suffix=".traj", delete=False) as traj_f:
            traj_path = traj_f.name
        with tempfile.NamedTemporaryFile(suffix=".log", delete=False) as log_f:
            md_log_path = log_f.name

        # Pre-relaxación (igual que Facebook)
        opt = LBFGS(atoms, logfile=md_log_path, trajectory=traj_path)
        if progress:
            opt.attach(update_progress, interval=interval)
        opt.run(fmax=0.05, steps=num_prerelax_steps)

        # Velocidades (igual que Facebook - x2 después de relajación)
        MaxwellBoltzmannDistribution(atoms, temperature_K=temperature_k * 2)

        # Integrador (igual que Facebook)
        if md_ensemble == "NVE":
            dyn = VelocityVerlet(atoms, timestep=md_timestep * units.fs)
        elif md_ensemble == "NVT":
            dyn = NoseHooverChainNVT(
                atoms,
                timestep=md_timestep * units.fs,
                temperature_K=temperature_k,
                tdamp=10 * md_timestep * units.fs,
            )

        # Trajectory y logging (igual que Facebook)
        traj = Trajectory(traj_path, "a", atoms)
        dyn.attach(traj.write, interval=1)
        if progress:
            dyn.attach(update_progress, interval=interval)
        dyn.attach(
            MDLogger(
                dyn,
                atoms,
                md_log_path,
                header=True,
                stress=False,
                peratom=True,
                mode="a",
            ),
            interval=10,
        )

        # Ejecutar MD
        dyn.run(num_steps)

        # Script de reproducción (estilo Facebook pero con OrbMol)
        
        reproduction_script = f"""
import ase.io
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
from ase.md.verlet import VelocityVerlet
from ase.optimize import LBFGS
from ase.io.trajectory import Trajectory
from ase.md import MDLogger
from ase import units
from orb_models.forcefield import pretrained
from orb_models.forcefield.calculator import ORBCalculator

# Read the atoms object from ASE read-able file
atoms = ase.io.read('input_file.traj')
# Set the total charge and spin multiplicity
atoms.info["charge"] = {total_charge}
atoms.info["spin"] = {spin_multiplicity}
# Set up the OrbMol calculator
orbff = pretrained.{model_name[task_name]}(device='cpu', precision='float32-high')
atoms.calc = ORBCalculator(orbff, device='cpu')
# Do a quick pre-relaxation to make sure the system is stable
opt = LBFGS(atoms, trajectory="relaxation_output.traj")
opt.run(fmax=0.05, steps={num_prerelax_steps})
# Initialize the velocity distribution; we set twice the temperature since we did a relaxation and
# much of the kinetic energy will partition to the potential energy right away
MaxwellBoltzmannDistribution(atoms, temperature_K={temperature_k}*2)
# Initialize the integrator; NVE is shown here as an example
dyn = VelocityVerlet(atoms, timestep={md_timestep} * units.fs)
# Set up trajectory and MD logger
dyn.attach(MDLogger(dyn, atoms, 'md.log', header=True, stress=False, peratom=True, mode="w"), interval=10)
traj = Trajectory("md_output.traj", "w", atoms)
dyn.attach(traj.write, interval=1)
# Run the simulation!
dyn.run({num_steps})
        """

        # Leer log
        with open(md_log_path, "r") as md_log_file:
            md_log = md_log_file.read()

        if explanation is None:
            explanation = f"MD simulation of {len(atoms)} atoms for {num_steps} steps with a timestep of {md_timestep} fs at {temperature_k} K in the {md_ensemble} ensemble using OrbMol. You submitted this simulation, so I hope you know what you're looking for or what it means!"

        return traj_path, md_log, reproduction_script, explanation

    except Exception as e:
        raise Exception(
            f"Error running MD simulation: {str(e)}. Please try again or report this error."
        )
    finally:
        # Limpieza (igual que Facebook)
        if temp_path and os.path.exists(temp_path):
            os.remove(temp_path)

        if md_log_path and os.path.exists(md_log_path):
            os.remove(md_log_path)

        if atoms is not None and getattr(atoms, "calc", None) is not None:
            atoms.calc = None

def run_relaxation_simulation(
    structure_file,
    num_steps,
    fmax,
    task_name,
    total_charge: float = 0,
    spin_multiplicity: float = 1,
    relax_unit_cell=False,
    explanation: str | None = None,
    oauth_token=None,  # Ignorado
    progress=None,
):
    """
    Relaxation simulation estilo Facebook pero con OrbMol
    """
    temp_path = None
    traj_path = None
    opt_log_path = None
    atoms = None

    try:
        # Cargar átomos (igual que Facebook)
        atoms = load_check_ase_atoms(structure_file)

        # Configurar charge y spin
        atoms.info["charge"] = total_charge
        atoms.info["spin"] = spin_multiplicity

        # AQUÍ EL CAMBIO: OrbMol en lugar de HFEndpointCalculator
        calc = load_orbmol_model(task_name)
        atoms.calc = calc

        # Archivos temporales
        with tempfile.NamedTemporaryFile(suffix=".traj", delete=False) as traj_f:
            traj_path = traj_f.name
        with tempfile.NamedTemporaryFile(suffix=".log", delete=False) as log_f:
            opt_log_path = log_f.name

        # Optimizador (igual que Facebook)
        optimizer = LBFGS(
            FrechetCellFilter(atoms) if relax_unit_cell else atoms,
            trajectory=traj_path,
            logfile=opt_log_path,
        )

        # Progress callback si existe
        if progress:
            interval = 1
            steps = [0]
            def update_progress(steps):
                steps[-1] += interval
                progress(steps[-1] / num_steps)
            optimizer.attach(update_progress, interval=interval, steps=steps)

        # Ejecutar optimización
        optimizer.run(fmax=fmax, steps=num_steps)

        # Script de reproducción
        reproduction_script = f"""
import ase.io
from ase.optimize import LBFGS
from ase.filters import FrechetCellFilter
from orb_models.forcefield import pretrained
from orb_models.forcefield.calculator import ORBCalculator

# Read the atoms object from ASE read-able file
atoms = ase.io.read('input_file.traj')
# Set the total charge and spin multiplicity
atoms.info["charge"] = {total_charge}
atoms.info["spin"] = {spin_multiplicity}
# Set up the OrbMol calculator
orbff = pretrained.{model_name[task_name]}(device='cpu', precision='float32-high')
atoms.calc = ORBCalculator(orbff, device='cpu')
# Initialize the optimizer
relax_unit_cell = {relax_unit_cell}
optimizer = LBFGS(FrechetCellFilter(atoms) if relax_unit_cell else atoms, trajectory="relaxation_output.traj")
# Run the optimization!
optimizer.run(fmax={fmax}, steps={num_steps})
        """

        # Leer log
        with open(opt_log_path, "r") as opt_log_file:
            opt_log = opt_log_file.read()

        if explanation is None:
            explanation = f"Relaxation of {len(atoms)} atoms for {num_steps} steps with a force tolerance of {fmax} eV/Å using OrbMol. You submitted this simulation, so I hope you know what you're looking for or what it means!"

        return traj_path, opt_log, reproduction_script, explanation

    except Exception as e:
        raise Exception(
            f"Error running relaxation: {str(e)}. Please try again or report this error."
        )
    finally:
        # Limpieza (igual que Facebook)
        if temp_path and os.path.exists(temp_path):
            os.remove(temp_path)
        if opt_log_path and os.path.exists(opt_log_path):
            os.remove(opt_log_path)
        if atoms is not None and getattr(atoms, "calc", None) is not None:
            atoms.calc = None

# -----------------------------
# Helper functions para compatibilidad
# -----------------------------

def atoms_to_xyz(atoms: ase.Atoms) -> str:
    """Convert ASE Atoms to an XYZ string for quick visualization."""
    lines = [str(len(atoms)), "generated by simulation_scripts_orbmol"]
    for s, (x, y, z) in zip(atoms.get_chemical_symbols(), atoms.get_positions()):
        lines.append(f"{s} {x:.6f} {y:.6f} {z:.6f}")
    return "\n".join(lines)

def last_frame_xyz_from_traj(traj_path: str | Path) -> str:
    """Read the last frame of an ASE .traj and return it as XYZ string."""
    tr = Trajectory(str(traj_path))
    last = tr[-1]
    return atoms_to_xyz(last)

# Función de validación simplificada (sin autenticación)
def validate_ase_atoms_and_login(structure_file, login_button_value="", oauth_token=None):
    """Validación simplificada - sin login UMA"""
    if not structure_file:
        return (False, False, "Missing input structure!")
    
    if isinstance(structure_file, dict):
        structure_file = structure_file["path"]
    
    try:
        atoms = ase.io.read(structure_file)
        
        if len(atoms) == 0:
            return (False, False, "No atoms in the structure file!")
        elif not (all(atoms.pbc) or np.all(~np.array(atoms.pbc))):
            return (False, False, f"Mixed PBC {atoms.pbc} not supported!")
        elif len(atoms) > 2000:
            return (False, False, f"Too many atoms ({len(atoms)}), max 2000!")
        else:
            return (True, True, "Structure loaded successfully - ready for OrbMol simulation!")
            
    except Exception as e:
        return (False, False, f"Failed to load structure: {str(e)}")