"""Resumable real-model calibration phases, without storing backbone weights."""
from __future__ import annotations

import os
from pathlib import Path
import torch

from .calibrate import calibration_step
from .sharing_gap import SharingGapStatistics


class StatisticsAccumulator:
    """Dataset mean gradients and empirical Fisher with constant sample memory."""

    def __init__(self, state=None):
        self.count = 0 if state is None else state['count']
        self.sums = {} if state is None else state['sums']
        self.squares = {} if state is None else state['squares']
        self.parameters = {} if state is None else state['parameters']

    def add(self, statistics):
        if self.count and set(statistics) != set(self.sums):
            raise ValueError('SLT candidate set changed within a scan')
        for key, item in statistics.items():
            item.validate()
            gradients = item.gradients.detach().to(device='cpu', dtype=torch.float64)
            if not self.count:
                self.sums[key] = torch.zeros_like(gradients)
                self.squares[key] = torch.zeros_like(gradients[0])
                self.parameters[key] = item.parameter_count
            if (gradients.shape != self.sums[key].shape or
                    item.parameter_count != self.parameters[key]):
                raise ValueError('SLT candidate shape or parameter count changed')
            self.sums[key].add_(gradients)
            # Keep the Fisher estimator separate from the numerator gradients.
            # Recomputing it here would silently discard externally collected
            # score-function Fisher and force every caller onto the same proxy.
            self.squares[key].add_(item.fisher_diagonal.detach().to(device='cpu', dtype=torch.float64))
        self.count += 1

    def result(self):
        if not self.count:
            raise ValueError('cannot aggregate an empty SLT scan')
        return {key: SharingGapStatistics(self.sums[key] / self.count,
                    self.squares[key] / self.count, self.parameters[key])
                for key in self.sums}

    def state_dict(self):
        return dict(count=self.count, sums=self.sums, squares=self.squares,
                    parameters=self.parameters)


def atomic_save(value, path):
    path = Path(path)
    path.parent.mkdir(parents=True, exist_ok=True)
    temporary = path.with_name(path.name + '.tmp')
    with temporary.open('wb') as stream:
        torch.save(value, stream)
        stream.flush()
        os.fsync(stream.fileno())
    os.replace(temporary, path)


def component_modules(qdq, cta):
    return {'las': qdq.las, 'shared': qdq.shared_transforms,
            'selected': qdq.selected_loop_transforms, 'cta': cta}


class CalibrationJournal:
    """Checkpoint scan cursors, optimizer moments, component tensors and RNG.

    Progressive-selection callbacks replay saved completed scans on resume.
    An interrupted scan/optimization phase continues from its committed cursor.
    Optimizer moments intentionally reset between adaptation phases, matching
    the explicit phase policy of the real drivers, but never within a phase.
    """

    def __init__(self, path, *, contract, qdq, cta, mu_cache, interval=25, resume=False, stop_after_steps=None):
        if interval <= 0:
            raise ValueError('checkpoint interval must be positive')
        self.path, self.contract = Path(path), contract
        self.qdq, self.cta, self.mu_cache = qdq, cta, mu_cache
        self.stop_after_steps = stop_after_steps
        self.interval = interval
        self.state = dict(step=0, traces=[], optimized={}, scans={}, scan=None,
                          optimizer=None, optimizer_phase=None)
        if resume:
            saved = torch.load(self.path, map_location='cpu', weights_only=True)
            if saved.get('format') != 'loopq_real_calibration_v1' or saved['contract'] != contract:
                raise ValueError('resume source/data/config contract does not match')
            for key in saved['selected_groups']:
                qdq.select_group(key)
            for key, module in component_modules(qdq, cta).items():
                module.load_state_dict(saved['modules'][key])
            mu_cache.load_state_dict(saved['mu'])
            self.state = saved['progress']
            torch.set_rng_state(saved['rng_cpu'])
            if torch.cuda.is_available():
                torch.cuda.set_rng_state_all(saved['rng_cuda'])
        elif self.path.exists():
            raise ValueError('checkpoint exists; use --resume to continue it')
        else:
            self.save()

    @property
    def step(self):
        return self.state['step']

    @property
    def traces(self):
        return self.state['traces']

    def save(self):
        atomic_save(dict(format='loopq_real_calibration_v1', contract=self.contract,
            selected_groups=[key for key in sorted(self.qdq.sites)
                if self.qdq._encoded[key] in self.qdq.selected_loop_transforms],
            modules={key: module.state_dict() for key, module in
                     component_modules(self.qdq, self.cta).items()},
            mu=self.mu_cache.state_dict(), progress=self.state,
            rng_cpu=torch.get_rng_state(),
            rng_cuda=torch.cuda.get_rng_state_all() if torch.cuda.is_available() else []), self.path)

    def optimize(self, phase, count, optimizer_factory, loss_for):
        done = self.state['optimized'].get(phase, 0)
        if done >= count:
            return
        allowlist, optimizer = optimizer_factory()
        if self.state['optimizer_phase'] == phase:
            optimizer.load_state_dict(self.state['optimizer'])
        for index in range(done, count):
            loss = loss_for(self.step, self.mu_cache, False)[0]
            trace = calibration_step(loss=loss, optimizer=optimizer, allowlist=allowlist)
            trace.update(step=self.step, phase=phase)
            self.traces.append(trace)
            self.state['step'] += 1
            self.state['optimized'][phase] = index + 1
            self.state['optimizer_phase'] = phase
            self.state['optimizer'] = optimizer.state_dict()
            print(f'loopq optimize {phase} {index + 1}/{count} step={self.step} loss={trace["total"]:.6g}', flush=True)
            if (index + 1) % self.interval == 0 or index + 1 == count:
                self.save()
            if self.stop_after_steps is not None and self.step >= self.stop_after_steps:
                self.save()
                raise SystemExit("Requested calibration step boundary reached; checkpoint saved. Resume with --resume.")

    def scan(self, round_index, sample_count, loss_for):
        key = str(round_index)
        if key in self.state['scans']:
            return {name: SharingGapStatistics.from_dict(item)
                    for name, item in self.state['scans'][key].items()}
        pending = self.state['scan']
        if pending is not None and pending['round'] != round_index:
            raise ValueError('resume SLT round does not match saved scan')
        accumulator = StatisticsAccumulator(None if pending is None else pending['statistics'])
        for index in range(accumulator.count, sample_count):
            # Scores evaluate each sample's detached trust weights at the current
            # model. A scan never advances the optimizer clock or its mu cache.
            from .objective import AdaptiveMuCache
            scan_mu = AdaptiveMuCache(self.mu_cache.update_interval)
            loss, statistics = loss_for(index, scan_mu, True)
            accumulator.add(statistics)
            del loss, statistics
            self.state['scan'] = dict(round=round_index, statistics=accumulator.state_dict())
            print(f'loopq SLT round={round_index} sample={index + 1}/{sample_count}', flush=True)
            if (index + 1) % self.interval == 0:
                self.save()
        result = accumulator.result()
        self.state['scans'][key] = {name: item.to_dict() for name, item in result.items()}
        self.state['scan'] = None
        self.save()
        return result