set_option maxHeartbeats 300000 in
/-- If two functions `g` and `f` have power series `q` and `p` respectively at `f x` and `x`, then
`g ∘ f` admits the power series `q.comp p` at `x`. -/
theorem HasFPowerSeriesAt.comp {g : F → G} {f : E → F} {q : FormalMultilinearSeries 𝕜 F G}
    {p : FormalMultilinearSeries 𝕜 E F} {x : E} (hg : HasFPowerSeriesAt g q (f x))
    (hf : HasFPowerSeriesAt f p x) : HasFPowerSeriesAt (g ∘ f) (q.comp p) x := by
  /- Consider `rf` and `rg` such that `f` and `g` have power series expansion on the disks
    of radius `rf` and `rg`. -/
  rcases hg with ⟨rg, Hg⟩
  rcases hf with ⟨rf, Hf⟩
  -- The terms defining `q.comp p` are geometrically summable in a disk of some radius `r`.
  rcases q.comp_summable_nnreal p Hg.radius_pos Hf.radius_pos with ⟨r, r_pos : 0 < r, hr⟩
  /- We will consider `y` which is smaller than `r` and `rf`, and also small enough that
    `f (x + y)` is close enough to `f x` to be in the disk where `g` is well behaved. Let
    `min (r, rf, δ)` be this new radius.-/
  obtain ⟨δ, δpos, hδ⟩ :
    ∃ (δ : ℝ≥0∞) (_H : 0 < δ), ∀ {z : E}, z ∈ EMetric.ball x δ → f z ∈ EMetric.ball (f x) rg := by
    have : EMetric.ball (f x) rg ∈ 𝓝 (f x) := EMetric.ball_mem_nhds _ Hg.r_pos
    rcases EMetric.mem_nhds_iff.1 (Hf.analyticAt.continuousAt this) with ⟨δ, δpos, Hδ⟩
    exact ⟨δ, δpos, fun hz => Hδ hz⟩
  let rf' := min rf δ
  have min_pos : 0 < min rf' r := by
    simp only [r_pos, Hf.r_pos, δpos, lt_min_iff, ENNReal.coe_pos, and_self_iff]
  /- We will show that `g ∘ f` admits the power series `q.comp p` in the disk of
    radius `min (r, rf', δ)`. -/
  refine' ⟨min rf' r, _⟩
  refine'
    ⟨le_trans (min_le_right rf' r) (FormalMultilinearSeries.le_comp_radius_of_summable q p r hr),
      min_pos, @fun y hy => _⟩
  /- Let `y` satisfy `‖y‖ < min (r, rf', δ)`. We want to show that `g (f (x + y))` is the sum of
    `q.comp p` applied to `y`. -/
  -- First, check that `y` is small enough so that estimates for `f` and `g` apply.
  have y_mem : y ∈ EMetric.ball (0 : E) rf :=
    (EMetric.ball_subset_ball (le_trans (min_le_left _ _) (min_le_left _ _))) hy
  have fy_mem : f (x + y) ∈ EMetric.ball (f x) rg := by
    apply hδ
    have : y ∈ EMetric.ball (0 : E) δ :=
      (EMetric.ball_subset_ball (le_trans (min_le_left _ _) (min_le_right _ _))) hy
    simpa [edist_eq_coe_nnnorm_sub, edist_eq_coe_nnnorm]
  /- Now the proof starts. To show that the sum of `q.comp p` at `y` is `g (f (x + y))`,
    we will write `q.comp p` applied to `y` as a big sum over all compositions.
    Since the sum is summable, to get its convergence it suffices to get
    the convergence along some increasing sequence of sets.
    We will use the sequence of sets `comp_partial_sum_target 0 n n`,
    along which the sum is exactly the composition of the partial sums of `q` and `p`, by design.
    To show that it converges to `g (f (x + y))`, pointwise convergence would not be enough,
    but we have uniform convergence to save the day. -/
  -- First step: the partial sum of `p` converges to `f (x + y)`.
  have A : Tendsto (fun n => ∑ a in Finset.Ico 1 n, p a fun _b => y)
      atTop (𝓝 (f (x + y) - f x)) := by
    have L :
      ∀ᶠ n in atTop, (∑ a in Finset.range n, p a fun _b => y) - f x
        = ∑ a in Finset.Ico 1 n, p a fun _b => y := by
      rw [eventually_atTop]
      refine' ⟨1, fun n hn => _⟩
      symm
      rw [eq_sub_iff_add_eq', Finset.range_eq_Ico, ← Hf.coeff_zero fun _i => y,
        Finset.sum_eq_sum_Ico_succ_bot hn]
    have :
      Tendsto (fun n => (∑ a in Finset.range n, p a fun _b => y) - f x) atTop
        (𝓝 (f (x + y) - f x)) :=
      (Hf.hasSum y_mem).tendsto_sum_nat.sub tendsto_const_nhds
    exact Tendsto.congr' L this
  -- Second step: the composition of the partial sums of `q` and `p` converges to `g (f (x + y))`.
  have B :
    Tendsto (fun n => q.partialSum n (∑ a in Finset.Ico 1 n, p a fun _b => y)) atTop
      (𝓝 (g (f (x + y)))) := by
    -- we use the fact that the partial sums of `q` converge locally uniformly to `g`, and that
    -- composition passes to the limit under locally uniform convergence.
    have B₁ : ContinuousAt (fun z : F => g (f x + z)) (f (x + y) - f x) := by
      refine' ContinuousAt.comp _ (continuous_const.add continuous_id).continuousAt
      simp only [add_sub_cancel'_right, id.def]
      exact Hg.continuousOn.continuousAt (IsOpen.mem_nhds EMetric.isOpen_ball fy_mem)
    have B₂ : f (x + y) - f x ∈ EMetric.ball (0 : F) rg := by
      simpa [edist_eq_coe_nnnorm, edist_eq_coe_nnnorm_sub] using fy_mem
    rw [← EMetric.isOpen_ball.nhdsWithin_eq B₂] at A
    convert Hg.tendstoLocallyUniformlyOn.tendsto_comp B₁.continuousWithinAt B₂ A
    simp only [add_sub_cancel'_right]
  -- Third step: the sum over all compositions in `comp_partial_sum_target 0 n n` converges to
  -- `g (f (x + y))`. As this sum is exactly the composition of the partial sum, this is a direct
  -- consequence of the second step
  have C :
    Tendsto
      (fun n => ∑ i in compPartialSumTarget 0 n n, q.compAlongComposition p i.2 fun _j => y)
      atTop (𝓝 (g (f (x + y)))) :=
    by simpa [comp_partialSum] using B
  -- Fourth step: the sum over all compositions is `g (f (x + y))`. This follows from the
  -- convergence along a subsequence proved in the third step, and the fact that the sum is Cauchy
  -- thanks to the summability properties.
  have D :
    HasSum (fun i : Σ n, Composition n => q.compAlongComposition p i.2 fun _j => y)
      (g (f (x + y))) :=
    haveI cau :
      CauchySeq fun s : Finset (Σ n, Composition n) =>
        ∑ i in s, q.compAlongComposition p i.2 fun _j => y := by
      apply cauchySeq_finset_of_norm_bounded _ (NNReal.summable_coe.2 hr) _
      simp only [coe_nnnorm, NNReal.coe_mul, NNReal.coe_pow]
      rintro ⟨n, c⟩
      calc
        ‖(compAlongComposition q p c) fun _j : Fin n => y‖ ≤
            ‖compAlongComposition q p c‖ * ∏ _j : Fin n, ‖y‖ :=
          by apply ContinuousMultilinearMap.le_op_norm
        _ ≤ ‖compAlongComposition q p c‖ * (r : ℝ) ^ n := by
          apply mul_le_mul_of_nonneg_left _ (norm_nonneg _)
          rw [Finset.prod_const, Finset.card_fin]
          apply pow_le_pow_left (norm_nonneg _)
          rw [EMetric.mem_ball, edist_eq_coe_nnnorm] at hy
          have := le_trans (le_of_lt hy) (min_le_right _ _)
          rwa [ENNReal.coe_le_coe, ← NNReal.coe_le_coe, coe_nnnorm] at this
    tendsto_nhds_of_cauchySeq_of_subseq cau compPartialSumTarget_tendsto_atTop C
  -- Fifth step: the sum over `n` of `q.comp p n` can be expressed as a particular resummation of
  -- the sum over all compositions, by grouping together the compositions of the same
  -- integer `n`. The convergence of the whole sum therefore implies the converence of the sum
  -- of `q.comp p n`
  have E : HasSum (fun n => (q.comp p) n fun _j => y) (g (f (x + y))) := by
    

Proof state:
𝕜 : Type u_1
E : Type u_2
F : Type u_3
G : Type u_4
H : Type u_5
inst✝⁸ : NontriviallyNormedField 𝕜
inst✝⁷ : NormedAddCommGroup E
inst✝⁶ : NormedSpace 𝕜 E
inst✝⁵ : NormedAddCommGroup F
inst✝⁴ : NormedSpace 𝕜 F
inst✝³ : NormedAddCommGroup G
inst✝² : NormedSpace 𝕜 G
inst✝¹ : NormedAddCommGroup H
inst✝ : NormedSpace 𝕜 H
g : F → G
f : E → F
q : FormalMultilinearSeries 𝕜 F G
p : FormalMultilinearSeries 𝕜 E F
x : E
rg : ℝ≥0∞
Hg : HasFPowerSeriesOnBall g q (f x) rg
rf : ℝ≥0∞
Hf : HasFPowerSeriesOnBall f p x rf
r : ℝ≥0
r_pos : 0 < r
hr : Summable fun i => ‖compAlongComposition q p i.snd‖₊ * r ^ i.fst
δ : ℝ≥0∞
δpos : 0 < δ
hδ : ∀ {z : E}, z ∈ EMetric.ball x δ → f z ∈ EMetric.ball (f x) rg
rf' : ℝ≥0∞ := min rf δ
min_pos : 0 < min rf' ↑r
y : E
hy : y ∈ EMetric.ball 0 (min rf' ↑r)
y_mem : y ∈ EMetric.ball 0 rf
fy_mem : f (x + y) ∈ EMetric.ball (f x) rg
A : Tendsto (fun n => ∑ a in Finset.Ico 1 n, (p a) fun _b => y) atTop (𝓝 (f (x + y) - f x))
B : Tendsto (fun n => partialSum q n (∑ a in Finset.Ico 1 n, (p a) fun _b => y)) atTop (𝓝 (g (f (x + y))))
C :
  Tendsto (fun n => ∑ i in compPartialSumTarget 0 n n, (compAlongComposition q p i.snd) fun _j => y) atTop
    (𝓝 (g (f (x + y))))
D : HasSum (fun i => (compAlongComposition q p i.snd) fun _j => y) (g (f (x + y)))
⊢ HasSum (fun n => (FormalMultilinearSeries.comp q p n) fun _j => y) (g (f (x + y)))

Next tactic:
apply D.sigma