/-- Preliminary lemma for the strong law of large numbers for vector-valued random variables,
assuming measurability in addition to integrability. This is weakened to ae measurability in
the full version `ProbabilityTheory.strong_law_ae`. -/
lemma strong_law_ae_of_measurable
    (X : ℕ → Ω → E) (hint : Integrable (X 0)) (h' : StronglyMeasurable (X 0))
    (hindep : Pairwise (fun i j ↦ IndepFun (X i) (X j))) (hident : ∀ i, IdentDistrib (X i) (X 0)) :
    ∀ᵐ ω, Tendsto (fun n : ℕ ↦ (n : ℝ) ⁻¹ • (∑ i in range n, X i ω)) atTop (𝓝 𝔼[X 0]) := by
  /- Choose a simple function `φ` such that `φ (X 0)` approximates well enough `X 0` -- this is
  possible as `X 0` is strongly measurable. Then `φ (X n)` approximates well `X n`.
  Then the strong law for `φ (X n)` implies the strong law for `X n`, up to a small
  error controlled by `n⁻¹ ∑_{i=0}^{n-1} ‖X i - φ (X i)‖`. This one is also controlled thanks
  to the one-dimensional law of large numbers: it converges ae to `𝔼[‖X 0 - φ (X 0)‖]`, which
  is arbitrarily small for well chosen `φ`. -/
  let s : Set E := Set.range (X 0) ∪ {0}
  have zero_s : 0 ∈ s := by simp
  have : SeparableSpace s := h'.separableSpace_range_union_singleton
  have : Nonempty s := ⟨0, zero_s⟩
  -- sequence of approximating simple functions.
  let φ : ℕ → SimpleFunc E E :=
    SimpleFunc.nearestPt (fun k => Nat.casesOn k 0 ((↑) ∘ denseSeq s) : ℕ → E)
  let Y : ℕ → ℕ → Ω → E := fun k i ↦ (φ k) ∘ (X i)
  -- strong law for `φ (X n)`
  have A : ∀ᵐ ω, ∀ k,
      Tendsto (fun n : ℕ ↦ (n : ℝ) ⁻¹ • (∑ i in range n, Y k i ω)) atTop (𝓝 𝔼[Y k 0]) :=
    ae_all_iff.2 (fun k ↦ strong_law_ae_simpleFunc_comp X h'.measurable hindep hident (φ k))
  -- strong law for the error `‖X i - φ (X i)‖`
  have B : ∀ᵐ ω, ∀ k, Tendsto (fun n : ℕ ↦ (∑ i in range n, ‖(X i - Y k i) ω‖) / n)
        atTop (𝓝 𝔼[(fun ω ↦ ‖(X 0 - Y k 0) ω‖)]) := by
    apply ae_all_iff.2 (fun k ↦ ?_)
    let G : ℕ → E → ℝ := fun k x ↦ ‖x - φ k x‖
    have G_meas : ∀ k, Measurable (G k) :=
      fun k ↦ (measurable_id.sub_stronglyMeasurable (φ k).stronglyMeasurable).norm
    have I : ∀ k i, (fun ω ↦ ‖(X i - Y k i) ω‖) = (G k) ∘ (X i) := fun k i ↦ rfl
    apply strong_law_ae_real (fun i ω ↦ ‖(X i - Y k i) ω‖)
    · exact (hint.sub ((φ k).comp (X 0) h'.measurable).integrable_of_isFiniteMeasure).norm
    · simp_rw [I]
      intro i j hij
      exact (hindep hij).comp (G_meas k) (G_meas k)
    · intro i
      simp_rw [I]
      apply (hident i).comp (G_meas k)
  -- check that, when both convergences above hold, then the strong law is satisfied
  filter_upwards [A, B] with ω hω h'ω
  rw [tendsto_iff_norm_sub_tendsto_zero, tendsto_order]
  refine ⟨fun c hc ↦ eventually_of_forall (fun n ↦ hc.trans_le (norm_nonneg _)), ?_⟩
  -- start with some positive `ε` (the desired precision), and fix `δ` with `3 δ < ε`.
  intro ε (εpos : 0 < ε)
  obtain ⟨δ, δpos, hδ⟩ : ∃ δ, 0 < δ ∧ δ + δ + δ < ε := ⟨ε/4, by positivity, by linarith⟩
  -- choose `k` large enough so that `φₖ (X 0)` approximates well enough `X 0`, up to the
  -- precision `δ`.
  obtain ⟨k, hk⟩ : ∃ k, ∫ ω, ‖(X 0 - Y k 0) ω‖ < δ := by
    simp_rw [Pi.sub_apply, norm_sub_rev (X 0 _)]
    exact ((tendsto_order.1 (tendsto_integral_norm_approxOn_sub h'.measurable hint)).2 δ
      δpos).exists
  have : ‖𝔼[Y k 0] - 𝔼[X 0]‖ < δ := by
    rw [norm_sub_rev, ← integral_sub hint]
    · exact (norm_integral_le_integral_norm _).trans_lt hk
    · exact ((φ k).comp (X 0) h'.measurable).integrable_of_isFiniteMeasure
  -- consider `n` large enough for which the above convergences have taken place within `δ`.
  

Proof state:
case h.intro.intro.intro
Ω : Type u_1
inst✝⁶ : MeasureSpace Ω
inst✝⁵ : IsProbabilityMeasure ℙ
E : Type u_2
inst✝⁴ : NormedAddCommGroup E
inst✝³ : NormedSpace ℝ E
inst✝² : CompleteSpace E
inst✝¹ : MeasurableSpace E
inst✝ : BorelSpace E
X : ℕ → Ω → E
hint : Integrable (X 0)
h' : StronglyMeasurable (X 0)
hindep : Pairwise fun i j => IndepFun (X i) (X j)
hident : ∀ (i : ℕ), IdentDistrib (X i) (X 0)
s : Set E := Set.range (X 0) ∪ {0}
zero_s : 0 ∈ s
this✝¹ : SeparableSpace ↑s
this✝ : Nonempty ↑s
φ : ℕ → SimpleFunc E E := SimpleFunc.nearestPt fun k => Nat.casesOn k 0 (Subtype.val ∘ denseSeq ↑s)
Y : ℕ → ℕ → Ω → E := fun k i => ↑(φ k) ∘ X i
A : ∀ᵐ (ω : Ω), ∀ (k : ℕ), Tendsto (fun n => (↑n)⁻¹ • ∑ i in Finset.range n, Y k i ω) atTop (𝓝 (∫ (a : Ω), Y k 0 a))
B :
  ∀ᵐ (ω : Ω),
    ∀ (k : ℕ),
      Tendsto (fun n => (∑ i in Finset.range n, ‖(X i - Y k i) ω‖) / ↑n) atTop
        (𝓝 (∫ (a : Ω), (fun ω => ‖(X 0 - Y k 0) ω‖) a))
ω : Ω
hω : ∀ (k : ℕ), Tendsto (fun n => (↑n)⁻¹ • ∑ i in Finset.range n, Y k i ω) atTop (𝓝 (∫ (a : Ω), Y k 0 a))
h'ω :
  ∀ (k : ℕ), Tendsto (fun n => (∑ i in Finset.range n, ‖(X i - Y k i) ω‖) / ↑n) atTop (𝓝 (∫ (a : Ω), ‖(X 0 - Y k 0) a‖))
ε : ℝ
εpos : 0 < ε
δ : ℝ
δpos : 0 < δ
hδ : δ + δ + δ < ε
k : ℕ
hk : ∫ (ω : Ω), ‖(X 0 - Y k 0) ω‖ < δ
this : ‖(∫ (a : Ω), Y k 0 a) - ∫ (a : Ω), X 0 a‖ < δ
⊢ ∀ᶠ (b : ℕ) in atTop, ‖(↑b)⁻¹ • ∑ i in Finset.range b, X i ω - ∫ (a : Ω), X 0 a‖ < ε

Next tactic:
have I : ∀ᶠ n in atTop, (∑ i in range n, ‖(X i - Y k i) ω‖) / n < δ :=
    (tendsto_order.1 (h'ω k)).2 δ hk