/** Cross-runtime parity against the pinned PyTorch / Python ORT fixtures. */
import { resolve } from 'node:path'
import type { Question } from '../../src/decisions/adapters/crivo.js'
import { Crivo } from '../../src/decisions/adapters/crivo.js'
import { MODEL_SIZES } from '../../src/decisions/models.js'

for (const size of process.argv.slice(2).length ? process.argv.slice(2) : MODEL_SIZES) {
  const directory = resolve('.cache/variants', size)
  const fixture = await Bun.file(`${directory}/parity.json`).json()
  for (const dtype of ['fp32', 'q8'] as const) {
    const model = await Crivo.load({
      modelId: directory,
      dtype,
      device: 'cpu',
      localFilesOnly: true
    })
    let maxError = 0
    try {
      for (const sample of fixture.cases) {
        const answers = await model.answerMany(
          sample.state,
          sample.questions.map((question: Question) => ({ question }))
        )
        for (const [index, answer] of answers.entries()) {
          const expected: number[] = sample[dtype === 'q8' ? 'int8' : 'fp32'][index]
          const actual =
            answer.type === 'noul'
              ? [1 - answer.noul, answer.noul]
              : Object.values(answer.probabilities)
          for (const [column, value] of actual.entries()) {
            const difference = Math.abs(value - (expected[column] ?? Number.NaN))
            if (!Number.isFinite(difference) || difference > 1e-4)
              throw new Error(`${size}/${dtype}: probability mismatch ${difference}`)
            maxError = Math.max(maxError, difference)
          }
        }
      }
    } finally {
      await model.dispose()
    }
    console.log(JSON.stringify({ size, dtype, maxProbabilityError: maxError }))
  }
}