/-- Let `f` be a function which is sufficiently close (in the Lipschitz sense) to a given linear
map `A`. Then it expands the volume of any set by at most `m` for any `m > det A`. -/
theorem addHaar_image_le_mul_of_det_lt (A : E →L[ℝ] E) {m : ℝ≥0}
    (hm : ENNReal.ofReal |A.det| < m) :
    ∀ᶠ δ in 𝓝[>] (0 : ℝ≥0),
      ∀ (s : Set E) (f : E → E), ApproximatesLinearOn f A s δ → μ (f '' s) ≤ m * μ s := by
  apply nhdsWithin_le_nhds
  let d := ENNReal.ofReal |A.det|
  -- construct a small neighborhood of `A '' (closedBall 0 1)` with measure comparable to
  -- the determinant of `A`.
  obtain ⟨ε, hε, εpos⟩ :
    ∃ ε : ℝ, μ (closedBall 0 ε + A '' closedBall 0 1) < m * μ (closedBall 0 1) ∧ 0 < ε := by
    have HC : IsCompact (A '' closedBall 0 1) :=
      (ProperSpace.isCompact_closedBall _ _).image A.continuous
    have L0 :
      Tendsto (fun ε => μ (cthickening ε (A '' closedBall 0 1))) (𝓝[>] 0)
        (𝓝 (μ (A '' closedBall 0 1))) := by
      apply Tendsto.mono_left _ nhdsWithin_le_nhds
      exact tendsto_measure_cthickening_of_isCompact HC
    have L1 :
      Tendsto (fun ε => μ (closedBall 0 ε + A '' closedBall 0 1)) (𝓝[>] 0)
        (𝓝 (μ (A '' closedBall 0 1))) := by
      apply L0.congr' _
      filter_upwards [self_mem_nhdsWithin] with r hr
      rw [← HC.add_closedBall_zero (le_of_lt hr), add_comm]
    have L2 :
      Tendsto (fun ε => μ (closedBall 0 ε + A '' closedBall 0 1)) (𝓝[>] 0)
        (𝓝 (d * μ (closedBall 0 1))) := by
      convert L1
      exact (addHaar_image_continuousLinearMap _ _ _).symm
    have I : d * μ (closedBall 0 1) < m * μ (closedBall 0 1) :=
      (ENNReal.mul_lt_mul_right (measure_closedBall_pos μ _ zero_lt_one).ne'
            measure_closedBall_lt_top.ne).2
        hm
    have H :
      ∀ᶠ b : ℝ in 𝓝[>] 0, μ (closedBall 0 b + A '' closedBall 0 1) < m * μ (closedBall 0 1) :=
      (tendsto_order.1 L2).2 _ I
    exact (H.and self_mem_nhdsWithin).exists
  have : Iio (⟨ε, εpos.le⟩ : ℝ≥0) ∈ 𝓝 (0 : ℝ≥0) := by apply Iio_mem_nhds; exact εpos
  filter_upwards [this]
  -- fix a function `f` which is close enough to `A`.
  intro δ hδ s f hf
  -- This function expands the volume of any ball by at most `m`
  have I : ∀ x r, x ∈ s → 0 ≤ r → μ (f '' (s ∩ closedBall x r)) ≤ m * μ (closedBall x r) := by
    intro x r xs r0
    have K : f '' (s ∩ closedBall x r) ⊆ A '' closedBall 0 r + closedBall (f x) (ε * r) := by
      rintro y ⟨z, ⟨zs, zr⟩, rfl⟩
      apply Set.mem_add.2 ⟨A (z - x), f z - f x - A (z - x) + f x, _, _, _⟩
      · apply mem_image_of_mem
        simpa only [dist_eq_norm, mem_closedBall, mem_closedBall_zero_iff, sub_zero] using zr
      · rw [mem_closedBall_iff_norm, add_sub_cancel]
        calc
          ‖f z - f x - A (z - x)‖ ≤ δ * ‖z - x‖ := hf _ zs _ xs
          _ ≤ ε * r :=
            mul_le_mul (le_of_lt hδ) (mem_closedBall_iff_norm.1 zr) (norm_nonneg _) εpos.le
      · simp only [map_sub, Pi.sub_apply]
        abel
    have :
      A '' closedBall 0 r + closedBall (f x) (ε * r) =
        {f x} + r • (A '' closedBall 0 1 + closedBall 0 ε) := by
      rw [smul_add, ← add_assoc, add_comm {f x}, add_assoc, smul_closedBall _ _ εpos.le, smul_zero,
        singleton_add_closedBall_zero, ← image_smul_set ℝ E E A, smul_closedBall _ _ zero_le_one,
        smul_zero, Real.norm_eq_abs, abs_of_nonneg r0, mul_one, mul_comm]
    rw [this] at K
    calc
      μ (f '' (s ∩ closedBall x r)) ≤ μ ({f x} + r • (A '' closedBall 0 1 + closedBall 0 ε)) :=
        measure_mono K
      _ = ENNReal.ofReal (r ^ finrank ℝ E) * μ (A '' closedBall 0 1 + closedBall 0 ε) := by
        simp only [abs_of_nonneg r0, addHaar_smul, image_add_left, abs_pow, singleton_add,
          measure_preimage_add]
      _ ≤ ENNReal.ofReal (r ^ finrank ℝ E) * (m * μ (closedBall 0 1)) := by
        rw [add_comm]; exact mul_le_mul_left' hε.le _
      _ = m * μ (closedBall x r) := by simp only [addHaar_closedBall' μ _ r0]; ring
  -- covering `s` by closed balls with total measure very close to `μ s`, one deduces that the
  -- measure of `f '' s` is at most `m * (μ s + a)` for any positive `a`.
  have J : ∀ᶠ a in 𝓝[>] (0 : ℝ≥0∞), μ (f '' s) ≤ m * (μ s + a) := by
    filter_upwards [self_mem_nhdsWithin] with a ha
    change 0 < a at ha
    obtain ⟨t, r, t_count, ts, rpos, st, μt⟩ :
      ∃ (t : Set E) (r : E → ℝ),
        t.Countable ∧
          t ⊆ s ∧
            (∀ x : E, x ∈ t → 0 < r x) ∧
              (s ⊆ ⋃ x ∈ t, closedBall x (r x)) ∧
                (∑' x : ↥t, μ (closedBall (↑x) (r ↑x))) ≤ μ s + a :=
      Besicovitch.exists_closedBall_covering_tsum_measure_le μ ha.ne' (fun _ => Ioi 0) s
        fun x _ δ δpos => ⟨δ / 2, by simp [half_pos δpos, δpos]⟩
    haveI : Encodable t := t_count.toEncodable
    calc
      μ (f '' s) ≤ μ (⋃ x : t, f '' (s ∩ closedBall x (r x))) := by
        rw [biUnion_eq_iUnion] at st
        apply measure_mono
        rw [← image_iUnion, ← inter_iUnion]
        exact image_subset _ (subset_inter (Subset.refl _) st)
      _ ≤ ∑' x : t, μ (f '' (s ∩ closedBall x (r x))) := (measure_iUnion_le _)
      _ ≤ ∑' x : t, m * μ (closedBall x (r x)) :=
        (ENNReal.tsum_le_tsum fun x => I x (r x) (ts x.2) (rpos x x.2).le)
      _ ≤ m * (μ s + a) := by rw [ENNReal.tsum_mul_left]; exact mul_le_mul_left' μt _
  -- taking the limit in `a`, one obtains the conclusion
  have L : Tendsto (fun a => (m : ℝ≥0∞) * (μ s + a)) (𝓝[>] 0) (𝓝 (m * (μ s + 0))) := by
    apply Tendsto.mono_left _ nhdsWithin_le_nhds
    apply ENNReal.Tendsto.const_mul (tendsto_const_nhds.add tendsto_id)
    simp only [ENNReal.coe_ne_top, Ne.def, or_true_iff, not_false_iff]
  

Proof state:
case h
E : Type u_1
F : Type u_2
inst✝⁷ : NormedAddCommGroup E
inst✝⁶ : NormedSpace ℝ E
inst✝⁵ : FiniteDimensional ℝ E
inst✝⁴ : NormedAddCommGroup F
inst✝³ : NormedSpace ℝ F
s✝ : Set E
f✝ : E → E
f' : E → E →L[ℝ] E
inst✝² : MeasurableSpace E
inst✝¹ : BorelSpace E
μ : Measure E
inst✝ : IsAddHaarMeasure μ
A : E →L[ℝ] E
m : ℝ≥0
hm : ENNReal.ofReal |ContinuousLinearMap.det A| < ↑m
d : ℝ≥0∞ := ENNReal.ofReal |ContinuousLinearMap.det A|
ε : ℝ
hε : ↑↑μ (closedBall 0 ε + ⇑A '' closedBall 0 1) < ↑m * ↑↑μ (closedBall 0 1)
εpos : 0 < ε
this : Iio { val := ε, property := (_ : 0 ≤ ε) } ∈ 𝓝 0
δ : ℝ≥0
hδ : δ ∈ Iio { val := ε, property := (_ : 0 ≤ ε) }
s : Set E
f : E → E
hf : ApproximatesLinearOn f A s δ
I : ∀ (x : E) (r : ℝ), x ∈ s → 0 ≤ r → ↑↑μ (f '' (s ∩ closedBall x r)) ≤ ↑m * ↑↑μ (closedBall x r)
J : ∀ᶠ (a : ℝ≥0∞) in 𝓝[>] 0, ↑↑μ (f '' s) ≤ ↑m * (↑↑μ s + a)
L : Tendsto (fun a => ↑m * (↑↑μ s + a)) (𝓝[>] 0) (𝓝 (↑m * (↑↑μ s + 0)))
⊢ ↑↑μ (f '' s) ≤ ↑m * ↑↑μ s

Next tactic:
rw [add_zero] at L