import { describe, expect, it } from 'vitest'

import { extractLabelFacts } from '../labels/label-schema.js'
import type { LifecycleEvent } from '../lifecycle/lifecycle-v0.js'
import type { GiteaIssue } from '../gitea/types.js'
import {
  CALIBRATION_BUCKET_FLOOR,
  calibrationSamples,
  type CalibrationSample,
  COLD_START_THRESHOLD,
  fitCalibration,
  toDurationModel,
} from './calibration-v0.js'

function sample(over: Partial<CalibrationSample> = {}): CalibrationSample {
  return { issue: 1, estimateDays: 2, actualWorkingDays: 2, bucket: 2, person: null, ...over }
}

describe('fitCalibration', () => {
  it('is cold-start below the threshold and reports the honest n', () => {
    const m = fitCalibration([sample(), sample({ actualWorkingDays: 4 })])
    expect(m.n).toBe(2)
    expect(m.coldStart).toBe(true)
  })

  it('flips off cold-start at the threshold', () => {
    const many = Array.from({ length: COLD_START_THRESHOLD }, (_, i) =>
      sample({ issue: i, estimateDays: 2, actualWorkingDays: 3, bucket: 2 }),
    )
    const m = fitCalibration(many)
    expect(m.n).toBe(COLD_START_THRESHOLD)
    expect(m.coldStart).toBe(false)
  })

  it('recovers the global median ratio (mu = mean log-ratio)', () => {
    // every actual is exactly 2x its estimate → mu = ln 2
    const m = fitCalibration(Array.from({ length: 25 }, (_, i) => sample({ issue: i, estimateDays: 2, actualWorkingDays: 4 })))
    expect(m.global.mu).toBeCloseTo(Math.log(2), 6)
  })

  it('fits a bucket only once it clears the floor', () => {
    const twos = Array.from({ length: CALIBRATION_BUCKET_FLOOR }, (_, i) =>
      sample({ issue: i, estimateDays: 2, actualWorkingDays: 3, bucket: 2 }),
    )
    const oneThin = [sample({ issue: 99, estimateDays: 5, actualWorkingDays: 9, bucket: 5 })]
    const m = fitCalibration([...twos, ...oneThin])
    expect(m.byBucket[2]?.n).toBe(CALIBRATION_BUCKET_FLOOR)
    expect(m.byBucket[5]).toBeUndefined() // only 1 sample, below floor
  })

  it('drops non-positive estimates/actuals', () => {
    const m = fitCalibration([sample({ actualWorkingDays: 0 }), sample({ estimateDays: 0 }), sample()])
    expect(m.n).toBe(1)
  })

  it('derives a per-person bias relative to global', () => {
    // one person consistently runs longer than the mean
    const base = Array.from({ length: 20 }, (_, i) => sample({ issue: i, actualWorkingDays: 2, person: 'ak' }))
    const slow = Array.from({ length: 3 }, (_, i) => sample({ issue: 100 + i, actualWorkingDays: 6, person: 'sm' }))
    const m = fitCalibration([...base, ...slow])
    expect(m.byPerson['sm'].biasMu).toBeGreaterThan(0)
    expect(m.byPerson['ak'].biasMu).toBeLessThan(0)
  })
})

describe('toDurationModel', () => {
  it('projects the fit down to the params forecast needs', () => {
    const m = fitCalibration(
      Array.from({ length: 25 }, (_, i) => sample({ issue: i, estimateDays: 2, actualWorkingDays: 3, bucket: 2 })),
    )
    const dm = toDurationModel(m)
    expect(dm.coldStart).toBe(false)
    expect(dm.byBucket[2].mu).toBeCloseTo(m.byBucket[2].mu, 6)
    expect(dm.global.mu).toBeCloseTo(m.global.mu, 6)
  })
})

describe('calibrationSamples', () => {
  const asOf = new Date('2026-02-01T00:00:00Z')

  function issue(over: Partial<GiteaIssue>): GiteaIssue {
    const labels = over.labels ?? []
    return {
      number: 1,
      title: '#1',
      body: '',
      state: 'closed',
      labels,
      facts: extractLabelFacts(labels),
      milestone: null,
      assignee: null,
      assignees: [],
      createdAt: '2026-01-05T09:00:00Z',
      updatedAt: '2026-01-12T09:00:00Z',
      closedAt: '2026-01-12T09:00:00Z',
      url: '',
      ...over,
    }
  }

  const events = (i: number): Record<number, LifecycleEvent[]> => ({
    [i]: [{ type: 'commit', at: '2026-01-07T09:00:00Z' }, { type: 'close', at: '2026-01-12T09:00:00Z' }],
  })

  it('samples closed, estimated issues with a resolvable actual', () => {
    const i = issue({ number: 7, labels: ['est/2d'], assignee: 'sm', closedAt: '2026-01-12T09:00:00Z' })
    const [s] = calibrationSamples([i], events(7), asOf)
    expect(s.issue).toBe(7)
    expect(s.estimateDays).toBe(2)
    expect(s.bucket).toBe(2)
    expect(s.person).toBe('sm')
    // Wed 2026-01-07 → Mon 2026-01-12 = Wed,Thu,Fri = 3 working days
    expect(s.actualWorkingDays).toBe(3)
  })

  it('skips open issues and closed ones without an estimate', () => {
    const open = issue({ number: 8, state: 'open', labels: ['est/2d'], closedAt: null })
    const noEst = issue({ number: 9, labels: [] })
    expect(calibrationSamples([open, noEst], { ...events(8), ...events(9) }, asOf)).toEqual([])
  })
})
