-- Module: Mathlib.Analysis.Calculus.FDeriv.Measurable
Goal:
𝕜 : Type u_1
inst : NontriviallyNormedField 𝕜
E : Type u_2
inst_1 : NormedAddCommGroup E
inst_2 : NormedSpace 𝕜 E
F : Type u_3
inst_3 : NormedAddCommGroup F
inst_4 : NormedSpace 𝕜 F
f : E → F
K : Set (E →L[𝕜] F)
hK : IsComplete K
P : ∀ {n : ℕ}, 0 < (1 / 2) ^ n
c : 𝕜
hc : 1 < ‖c‖
x : E
hx : x ∈ D f K
n : (e : ℕ) → ℕ
L : (e p q : ℕ) → E →L[𝕜] F
hn :
  ∀ (e p q : ℕ),
    n e ≤ p →
      n e ≤ q → L e p q ∈ K ∧ x ∈ A f (L e p q) ((1 / 2) ^ p) ((1 / 2) ^ e) ∩ A f (L e p q) ((1 / 2) ^ q) ((1 / 2) ^ e)
M :
  ∀ (e p q e' p' q' : ℕ),
    n e ≤ p → n e ≤ q → n e' ≤ p' → n e' ≤ q' → e ≤ e' → ‖L e p q - L e' p' q'‖ ≤ 12 * ‖c‖ * (1 / 2) ^ e
L0 : ℕ → E →L[𝕜] F := fun (e : ℕ) ↦ L e (n e) (n e)
this : CauchySeq L0
f' : E →L[𝕜] F
f'K : f' ∈ K
hf' : Tendsto L0 atTop (𝓝 f')
Lf' : ∀ (e p : ℕ), n e ≤ p → ‖L e (n e) p - f'‖ ≤ 12 * ‖c‖ * (1 / 2) ^ e
⊢ HasFDerivAt f f' x

Tactic:
have : HasFDerivAt f f' x := by
    simp only [hasFDerivAt_iff_isLittleO_nhds_zero, isLittleO_iff]
    /- to get an approximation with a precision `ε`, we will replace `f` with `L e (n e) m` for
      some large enough `e` (yielding a small error by uniform approximation). As one can vary `m`,
      this makes it possible to cover all scales, and thus to obtain a good linear approximation in
      the whole ball of radius `(1/2)^(n e)`. -/
    intro ε εpos
    have pos : 0 < 4 + 12 * ‖c‖ := by positivity
    obtain ⟨e, he⟩ : ∃ e : ℕ, (1 / 2) ^ e < ε / (4 + 12 * ‖c‖) :=
      exists_pow_lt_of_lt_one (div_pos εpos pos) (by norm_num)
    rw [eventually_nhds_iff_ball]
    refine ⟨(1 / 2) ^ (n e + 1), P, fun y hy => ?_⟩
    -- We need to show that `f (x + y) - f x - f' y` is small. For this, we will work at scale
    -- `k` where `k` is chosen with `‖y‖ ∼ 2 ^ (-k)`.
    by_cases y_pos : y = 0
    · simp [y_pos]
    have yzero : 0 < ‖y‖ := norm_pos_iff.mpr y_pos
    have y_lt : ‖y‖ < (1 / 2) ^ (n e + 1) := by simpa using mem_ball_iff_norm.1 hy
    have yone : ‖y‖ ≤ 1 := le_trans y_lt.le (pow_le_one₀ (by norm_num) (by norm_num))
    -- define the scale `k`.
    obtain ⟨k, hk, h'k⟩ : ∃ k : ℕ, (1 / 2) ^ (k + 1) < ‖y‖ ∧ ‖y‖ ≤ (1 / 2) ^ k :=
      exists_nat_pow_near_of_lt_one yzero yone (by norm_num : (0 : ℝ) < 1 / 2)
        (by norm_num : (1 : ℝ) / 2 < 1)
    -- the scale is large enough (as `y` is small enough)
    have k_gt : n e < k := by
      have : ((1 : ℝ) / 2) ^ (k + 1) < (1 / 2) ^ (n e + 1) := lt_trans hk y_lt
      rw [pow_lt_pow_iff_right_of_lt_one₀ (by norm_num : (0 : ℝ) < 1 / 2) (by norm_num)] at this
      lia
    set m := k - 1
    have m_ge : n e ≤ m := Nat.le_sub_one_of_lt k_gt
    have km : k = m + 1 := (Nat.succ_pred_eq_of_pos k_gt.pos).symm
    rw [km] at hk h'k
    -- `f` is well approximated by `L e (n e) k` at the relevant scale
    -- (in fact, we use `m = k - 1` instead of `k` because of the precise definition of `A`).
    have J1 : ‖f (x + y) - f x - L e (n e) m (x + y - x)‖ ≤ (1 / 2) ^ e * (1 / 2) ^ m := by
      apply le_of_mem_A (hn e (n e) m le_rfl m_ge).2.2
      · simp only [mem_closedBall, dist_self]
        positivity
      · simpa only [dist_eq_norm, add_sub_cancel_left, mem_closedBall, pow_succ, mul_one_div] using
          h'k
    have J2 : ‖f (x + y) - f x - L e (n e) m y‖ ≤ 4 * (1 / 2) ^ e * ‖y‖ :=
      calc
        ‖f (x + y) - f x - L e (n e) m y‖ ≤ (1 / 2) ^ e * (1 / 2) ^ m := by
          simpa only [add_sub_cancel_left] using J1
        _ = 4 * (1 / 2) ^ e * (1 / 2) ^ (m + 2) := by ring
        _ ≤ 4 * (1 / 2) ^ e * ‖y‖ := by gcongr
    -- use the previous estimates to see that `f (x + y) - f x - f' y` is small.
    calc
      ‖f (x + y) - f x - f' y‖ = ‖f (x + y) - f x - L e (n e) m y + (L e (n e) m - f') y‖ :=
        congr_arg _ (by simp)
      _ ≤ 4 * (1 / 2) ^ e * ‖y‖ + 12 * ‖c‖ * (1 / 2) ^ e * ‖y‖ :=
        norm_add_le_of_le J2 <| (le_opNorm _ _).trans <| by gcongr; exact Lf' _ _ m_ge
      _ = (4 + 12 * ‖c‖) * ‖y‖ * (1 / 2) ^ e := by ring
      _ ≤ (4 + 12 * ‖c‖) * ‖y‖ * (ε / (4 + 12 * ‖c‖)) := by gcongr
      _ = ε * ‖y‖ := by field