l3lab/ntp-mathlib huggingface dataset:
/-- If a sequence of functions real or complex functions are eventually differentiable on a
neighborhood of `x`, they are Cauchy _at_ `x`, and their derivatives
are a uniform Cauchy sequence in a neighborhood of `x`, then the functions form a uniform Cauchy
sequence in a neighborhood of `x`. -/
theorem uniformCauchySeqOnFilter_of_fderiv (hf' : UniformCauchySeqOnFilter f' l (𝓝 x))
    (hf : ∀ᶠ n : ι × E in l ×ˢ 𝓝 x, HasFDerivAt (f n.1) (f' n.1 n.2) n.2)
    (hfg : Cauchy (map (fun n => f n x) l)) : UniformCauchySeqOnFilter f l (𝓝 x) := by
  letI : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 _
  rw [SeminormedAddGroup.uniformCauchySeqOnFilter_iff_tendstoUniformlyOnFilter_zero] at hf' ⊢
  suffices
    TendstoUniformlyOnFilter (fun (n : ι × ι) (z : E) => f n.1 z - f n.2 z - (f n.1 x - f n.2 x)) 0
        (l ×ˢ l) (𝓝 x) ∧
      TendstoUniformlyOnFilter (fun (n : ι × ι) (_ : E) => f n.1 x - f n.2 x) 0 (l ×ˢ l) (𝓝 x) by
    have := this.1.add this.2
    rw [add_zero] at this
    exact this.congr (by simp)
  constructor
  · -- This inequality follows from the mean value theorem. To apply it, we will need to shrink our
    -- neighborhood to small enough ball
    rw [Metric.tendstoUniformlyOnFilter_iff] at hf' ⊢
    

Proof state:
case left
ι : Type u_1
l : Filter ι
E : Type u_2
inst✝⁴ : NormedAddCommGroup E
𝕜 : Type u_3
inst✝³ : IsROrC 𝕜
inst✝² : NormedSpace 𝕜 E
G : Type u_4
inst✝¹ : NormedAddCommGroup G
inst✝ : NormedSpace 𝕜 G
f : ι → E → G
g : E → G
f' : ι → E → E →L[𝕜] G
g' : E → E →L[𝕜] G
x : E
hf' : ∀ ε > 0, ∀ᶠ (n : (ι × ι) × E) in (l ×ˢ l) ×ˢ 𝓝 x, dist (OfNat.ofNat 0 n.2) (f' n.1.1 n.2 - f' n.1.2 n.2) < ε
hf : ∀ᶠ (n : ι × E) in l ×ˢ 𝓝 x, HasFDerivAt (f n.1) (f' n.1 n.2) n.2
hfg : Cauchy (map (fun n => f n x) l)
this : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 E
⊢ ∀ ε > 0,
    ∀ᶠ (n : (ι × ι) × E) in (l ×ˢ l) ×ˢ 𝓝 x,
      dist (OfNat.ofNat 0 n.2) (f n.1.1 n.2 - f n.1.2 n.2 - (f n.1.1 x - f n.1.2 x)) < ε

Next tactic:
intro ε hε
/-- If a sequence of functions real or complex functions are eventually differentiable on a
neighborhood of `x`, they are Cauchy _at_ `x`, and their derivatives
are a uniform Cauchy sequence in a neighborhood of `x`, then the functions form a uniform Cauchy
sequence in a neighborhood of `x`. -/
theorem uniformCauchySeqOnFilter_of_fderiv (hf' : UniformCauchySeqOnFilter f' l (𝓝 x))
    (hf : ∀ᶠ n : ι × E in l ×ˢ 𝓝 x, HasFDerivAt (f n.1) (f' n.1 n.2) n.2)
    (hfg : Cauchy (map (fun n => f n x) l)) : UniformCauchySeqOnFilter f l (𝓝 x) := by
  letI : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 _
  rw [SeminormedAddGroup.uniformCauchySeqOnFilter_iff_tendstoUniformlyOnFilter_zero] at hf' ⊢
  suffices
    TendstoUniformlyOnFilter (fun (n : ι × ι) (z : E) => f n.1 z - f n.2 z - (f n.1 x - f n.2 x)) 0
        (l ×ˢ l) (𝓝 x) ∧
      TendstoUniformlyOnFilter (fun (n : ι × ι) (_ : E) => f n.1 x - f n.2 x) 0 (l ×ˢ l) (𝓝 x) by
    have := this.1.add this.2
    rw [add_zero] at this
    exact this.congr (by simp)
  constructor
  · -- This inequality follows from the mean value theorem. To apply it, we will need to shrink our
    -- neighborhood to small enough ball
    rw [Metric.tendstoUniformlyOnFilter_iff] at hf' ⊢
    intro ε hε
    

Proof state:
case left
ι : Type u_1
l : Filter ι
E : Type u_2
inst✝⁴ : NormedAddCommGroup E
𝕜 : Type u_3
inst✝³ : IsROrC 𝕜
inst✝² : NormedSpace 𝕜 E
G : Type u_4
inst✝¹ : NormedAddCommGroup G
inst✝ : NormedSpace 𝕜 G
f : ι → E → G
g : E → G
f' : ι → E → E →L[𝕜] G
g' : E → E →L[𝕜] G
x : E
hf' : ∀ ε > 0, ∀ᶠ (n : (ι × ι) × E) in (l ×ˢ l) ×ˢ 𝓝 x, dist (OfNat.ofNat 0 n.2) (f' n.1.1 n.2 - f' n.1.2 n.2) < ε
hf : ∀ᶠ (n : ι × E) in l ×ˢ 𝓝 x, HasFDerivAt (f n.1) (f' n.1 n.2) n.2
hfg : Cauchy (map (fun n => f n x) l)
this : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 E
ε : ℝ
hε : ε > 0
⊢ ∀ᶠ (n : (ι × ι) × E) in (l ×ˢ l) ×ˢ 𝓝 x,
    dist (OfNat.ofNat 0 n.2) (f n.1.1 n.2 - f n.1.2 n.2 - (f n.1.1 x - f n.1.2 x)) < ε

Next tactic:
have := (tendsto_swap4_prod.eventually (hf.prod_mk hf)).diag_of_prod_right
/-- If a sequence of functions real or complex functions are eventually differentiable on a
neighborhood of `x`, they are Cauchy _at_ `x`, and their derivatives
are a uniform Cauchy sequence in a neighborhood of `x`, then the functions form a uniform Cauchy
sequence in a neighborhood of `x`. -/
theorem uniformCauchySeqOnFilter_of_fderiv (hf' : UniformCauchySeqOnFilter f' l (𝓝 x))
    (hf : ∀ᶠ n : ι × E in l ×ˢ 𝓝 x, HasFDerivAt (f n.1) (f' n.1 n.2) n.2)
    (hfg : Cauchy (map (fun n => f n x) l)) : UniformCauchySeqOnFilter f l (𝓝 x) := by
  letI : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 _
  rw [SeminormedAddGroup.uniformCauchySeqOnFilter_iff_tendstoUniformlyOnFilter_zero] at hf' ⊢
  suffices
    TendstoUniformlyOnFilter (fun (n : ι × ι) (z : E) => f n.1 z - f n.2 z - (f n.1 x - f n.2 x)) 0
        (l ×ˢ l) (𝓝 x) ∧
      TendstoUniformlyOnFilter (fun (n : ι × ι) (_ : E) => f n.1 x - f n.2 x) 0 (l ×ˢ l) (𝓝 x) by
    have := this.1.add this.2
    rw [add_zero] at this
    exact this.congr (by simp)
  constructor
  · -- This inequality follows from the mean value theorem. To apply it, we will need to shrink our
    -- neighborhood to small enough ball
    rw [Metric.tendstoUniformlyOnFilter_iff] at hf' ⊢
    intro ε hε
    have := (tendsto_swap4_prod.eventually (hf.prod_mk hf)).diag_of_prod_right
    

Proof state:
case left
ι : Type u_1
l : Filter ι
E : Type u_2
inst✝⁴ : NormedAddCommGroup E
𝕜 : Type u_3
inst✝³ : IsROrC 𝕜
inst✝² : NormedSpace 𝕜 E
G : Type u_4
inst✝¹ : NormedAddCommGroup G
inst✝ : NormedSpace 𝕜 G
f : ι → E → G
g : E → G
f' : ι → E → E →L[𝕜] G
g' : E → E →L[𝕜] G
x : E
hf' : ∀ ε > 0, ∀ᶠ (n : (ι × ι) × E) in (l ×ˢ l) ×ˢ 𝓝 x, dist (OfNat.ofNat 0 n.2) (f' n.1.1 n.2 - f' n.1.2 n.2) < ε
hf : ∀ᶠ (n : ι × E) in l ×ˢ 𝓝 x, HasFDerivAt (f n.1) (f' n.1 n.2) n.2
hfg : Cauchy (map (fun n => f n x) l)
this✝ : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 E
ε : ℝ
hε : ε > 0
this :
  ∀ᶠ (x : (ι × ι) × E) in (l ×ˢ l) ×ˢ 𝓝 x,
    HasFDerivAt (f (((x.1, x.2, x.2).1.1, (x.1, x.2, x.2).2.1), (x.1, x.2, x.2).1.2, (x.1, x.2, x.2).2.2).1.1)
        (f' (((x.1, x.2, x.2).1.1, (x.1, x.2, x.2).2.1), (x.1, x.2, x.2).1.2, (x.1, x.2, x.2).2.2).1.1
          (((x.1, x.2, x.2).1.1, (x.1, x.2, x.2).2.1), (x.1, x.2, x.2).1.2, (x.1, x.2, x.2).2.2).1.2)
        (((x.1, x.2, x.2).1.1, (x.1, x.2, x.2).2.1), (x.1, x.2, x.2).1.2, (x.1, x.2, x.2).2.2).1.2 ∧
      HasFDerivAt (f (((x.1, x.2, x.2).1.1, (x.1, x.2, x.2).2.1), (x.1, x.2, x.2).1.2, (x.1, x.2, x.2).2.2).2.1)
        (f' (((x.1, x.2, x.2).1.1, (x.1, x.2, x.2).2.1), (x.1, x.2, x.2).1.2, (x.1, x.2, x.2).2.2).2.1
          (((x.1, x.2, x.2).1.1, (x.1, x.2, x.2).2.1), (x.1, x.2, x.2).1.2, (x.1, x.2, x.2).2.2).2.2)
        (((x.1, x.2, x.2).1.1, (x.1, x.2, x.2).2.1), (x.1, x.2, x.2).1.2, (x.1, x.2, x.2).2.2).2.2
⊢ ∀ᶠ (n : (ι × ι) × E) in (l ×ˢ l) ×ˢ 𝓝 x,
    dist (OfNat.ofNat 0 n.2) (f n.1.1 n.2 - f n.1.2 n.2 - (f n.1.1 x - f n.1.2 x)) < ε

Next tactic:
obtain ⟨a, b, c, d, e⟩ := eventually_prod_iff.1 ((hf' ε hε).and this)
/-- A variant of the second fundamental theorem of calculus (FTC-2): If a sequence of functions
between real or complex normed spaces are differentiable on a ball centered at `x`, they
form a Cauchy sequence _at_ `x`, and their derivatives are Cauchy uniformly on the ball, then the
functions form a uniform Cauchy sequence on the ball.

NOTE: The fact that we work on a ball is typically all that is necessary to work with power series
and Dirichlet series (our primary use case). However, this can be generalized by replacing the ball
with any connected, bounded, open set and replacing uniform convergence with local uniform
convergence. See `cauchy_map_of_uniformCauchySeqOn_fderiv`.
-/
theorem uniformCauchySeqOn_ball_of_fderiv {r : ℝ} (hf' : UniformCauchySeqOn f' l (Metric.ball x r))
    (hf : ∀ n : ι, ∀ y : E, y ∈ Metric.ball x r → HasFDerivAt (f n) (f' n y) y)
    (hfg : Cauchy (map (fun n => f n x) l)) : UniformCauchySeqOn f l (Metric.ball x r) := by
  

Proof state:
ι : Type u_1
l : Filter ι
E : Type u_2
inst✝⁴ : NormedAddCommGroup E
𝕜 : Type u_3
inst✝³ : IsROrC 𝕜
inst✝² : NormedSpace 𝕜 E
G : Type u_4
inst✝¹ : NormedAddCommGroup G
inst✝ : NormedSpace 𝕜 G
f : ι → E → G
g : E → G
f' : ι → E → E →L[𝕜] G
g' : E → E →L[𝕜] G
x : E
r : ℝ
hf' : UniformCauchySeqOn f' l (Metric.ball x r)
hf : ∀ (n : ι), ∀ y ∈ Metric.ball x r, HasFDerivAt (f n) (f' n y) y
hfg : Cauchy (map (fun n => f n x) l)
⊢ UniformCauchySeqOn f l (Metric.ball x r)

Next tactic:
letI : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 _
/-- A variant of the second fundamental theorem of calculus (FTC-2): If a sequence of functions
between real or complex normed spaces are differentiable on a ball centered at `x`, they
form a Cauchy sequence _at_ `x`, and their derivatives are Cauchy uniformly on the ball, then the
functions form a uniform Cauchy sequence on the ball.

NOTE: The fact that we work on a ball is typically all that is necessary to work with power series
and Dirichlet series (our primary use case). However, this can be generalized by replacing the ball
with any connected, bounded, open set and replacing uniform convergence with local uniform
convergence. See `cauchy_map_of_uniformCauchySeqOn_fderiv`.
-/
theorem uniformCauchySeqOn_ball_of_fderiv {r : ℝ} (hf' : UniformCauchySeqOn f' l (Metric.ball x r))
    (hf : ∀ n : ι, ∀ y : E, y ∈ Metric.ball x r → HasFDerivAt (f n) (f' n y) y)
    (hfg : Cauchy (map (fun n => f n x) l)) : UniformCauchySeqOn f l (Metric.ball x r) := by
  letI : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 _
  

Proof state:
ι : Type u_1
l : Filter ι
E : Type u_2
inst✝⁴ : NormedAddCommGroup E
𝕜 : Type u_3
inst✝³ : IsROrC 𝕜
inst✝² : NormedSpace 𝕜 E
G : Type u_4
inst✝¹ : NormedAddCommGroup G
inst✝ : NormedSpace 𝕜 G
f : ι → E → G
g : E → G
f' : ι → E → E →L[𝕜] G
g' : E → E →L[𝕜] G
x : E
r : ℝ
hf' : UniformCauchySeqOn f' l (Metric.ball x r)
hf : ∀ (n : ι), ∀ y ∈ Metric.ball x r, HasFDerivAt (f n) (f' n y) y
hfg : Cauchy (map (fun n => f n x) l)
this : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 E
⊢ UniformCauchySeqOn f l (Metric.ball x r)

Next tactic:
have : NeBot l := (cauchy_map_iff.1 hfg).1
/-- A variant of the second fundamental theorem of calculus (FTC-2): If a sequence of functions
between real or complex normed spaces are differentiable on a ball centered at `x`, they
form a Cauchy sequence _at_ `x`, and their derivatives are Cauchy uniformly on the ball, then the
functions form a uniform Cauchy sequence on the ball.

NOTE: The fact that we work on a ball is typically all that is necessary to work with power series
and Dirichlet series (our primary use case). However, this can be generalized by replacing the ball
with any connected, bounded, open set and replacing uniform convergence with local uniform
convergence. See `cauchy_map_of_uniformCauchySeqOn_fderiv`.
-/
theorem uniformCauchySeqOn_ball_of_fderiv {r : ℝ} (hf' : UniformCauchySeqOn f' l (Metric.ball x r))
    (hf : ∀ n : ι, ∀ y : E, y ∈ Metric.ball x r → HasFDerivAt (f n) (f' n y) y)
    (hfg : Cauchy (map (fun n => f n x) l)) : UniformCauchySeqOn f l (Metric.ball x r) := by
  letI : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 _
  have : NeBot l := (cauchy_map_iff.1 hfg).1
  

Proof state:
ι : Type u_1
l : Filter ι
E : Type u_2
inst✝⁴ : NormedAddCommGroup E
𝕜 : Type u_3
inst✝³ : IsROrC 𝕜
inst✝² : NormedSpace 𝕜 E
G : Type u_4
inst✝¹ : NormedAddCommGroup G
inst✝ : NormedSpace 𝕜 G
f : ι → E → G
g : E → G
f' : ι → E → E →L[𝕜] G
g' : E → E →L[𝕜] G
x : E
r : ℝ
hf' : UniformCauchySeqOn f' l (Metric.ball x r)
hf : ∀ (n : ι), ∀ y ∈ Metric.ball x r, HasFDerivAt (f n) (f' n y) y
hfg : Cauchy (map (fun n => f n x) l)
this✝ : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 E
this : NeBot l
⊢ UniformCauchySeqOn f l (Metric.ball x r)

Next tactic:
rcases le_or_lt r 0 with (hr | hr)
/-- A variant of the second fundamental theorem of calculus (FTC-2): If a sequence of functions
between real or complex normed spaces are differentiable on a ball centered at `x`, they
form a Cauchy sequence _at_ `x`, and their derivatives are Cauchy uniformly on the ball, then the
functions form a uniform Cauchy sequence on the ball.

NOTE: The fact that we work on a ball is typically all that is necessary to work with power series
and Dirichlet series (our primary use case). However, this can be generalized by replacing the ball
with any connected, bounded, open set and replacing uniform convergence with local uniform
convergence. See `cauchy_map_of_uniformCauchySeqOn_fderiv`.
-/
theorem uniformCauchySeqOn_ball_of_fderiv {r : ℝ} (hf' : UniformCauchySeqOn f' l (Metric.ball x r))
    (hf : ∀ n : ι, ∀ y : E, y ∈ Metric.ball x r → HasFDerivAt (f n) (f' n y) y)
    (hfg : Cauchy (map (fun n => f n x) l)) : UniformCauchySeqOn f l (Metric.ball x r) := by
  letI : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 _
  have : NeBot l := (cauchy_map_iff.1 hfg).1
  rcases le_or_lt r 0 with (hr | hr)
  · 

Proof state:
case inl
ι : Type u_1
l : Filter ι
E : Type u_2
inst✝⁴ : NormedAddCommGroup E
𝕜 : Type u_3
inst✝³ : IsROrC 𝕜
inst✝² : NormedSpace 𝕜 E
G : Type u_4
inst✝¹ : NormedAddCommGroup G
inst✝ : NormedSpace 𝕜 G
f : ι → E → G
g : E → G
f' : ι → E → E →L[𝕜] G
g' : E → E →L[𝕜] G
x : E
r : ℝ
hf' : UniformCauchySeqOn f' l (Metric.ball x r)
hf : ∀ (n : ι), ∀ y ∈ Metric.ball x r, HasFDerivAt (f n) (f' n y) y
hfg : Cauchy (map (fun n => f n x) l)
this✝ : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 E
this : NeBot l
hr : r ≤ 0
⊢ UniformCauchySeqOn f l (Metric.ball x r)

Next tactic:
simp only [Metric.ball_eq_empty.2 hr, UniformCauchySeqOn, Set.mem_empty_iff_false,
      IsEmpty.forall_iff, eventually_const, imp_true_iff]
/-- A variant of the second fundamental theorem of calculus (FTC-2): If a sequence of functions
between real or complex normed spaces are differentiable on a ball centered at `x`, they
form a Cauchy sequence _at_ `x`, and their derivatives are Cauchy uniformly on the ball, then the
functions form a uniform Cauchy sequence on the ball.

NOTE: The fact that we work on a ball is typically all that is necessary to work with power series
and Dirichlet series (our primary use case). However, this can be generalized by replacing the ball
with any connected, bounded, open set and replacing uniform convergence with local uniform
convergence. See `cauchy_map_of_uniformCauchySeqOn_fderiv`.
-/
theorem uniformCauchySeqOn_ball_of_fderiv {r : ℝ} (hf' : UniformCauchySeqOn f' l (Metric.ball x r))
    (hf : ∀ n : ι, ∀ y : E, y ∈ Metric.ball x r → HasFDerivAt (f n) (f' n y) y)
    (hfg : Cauchy (map (fun n => f n x) l)) : UniformCauchySeqOn f l (Metric.ball x r) := by
  letI : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 _
  have : NeBot l := (cauchy_map_iff.1 hfg).1
  rcases le_or_lt r 0 with (hr | hr)
  · simp only [Metric.ball_eq_empty.2 hr, UniformCauchySeqOn, Set.mem_empty_iff_false,
      IsEmpty.forall_iff, eventually_const, imp_true_iff]
  

Proof state:
case inr
ι : Type u_1
l : Filter ι
E : Type u_2
inst✝⁴ : NormedAddCommGroup E
𝕜 : Type u_3
inst✝³ : IsROrC 𝕜
inst✝² : NormedSpace 𝕜 E
G : Type u_4
inst✝¹ : NormedAddCommGroup G
inst✝ : NormedSpace 𝕜 G
f : ι → E → G
g : E → G
f' : ι → E → E →L[𝕜] G
g' : E → E →L[𝕜] G
x : E
r : ℝ
hf' : UniformCauchySeqOn f' l (Metric.ball x r)
hf : ∀ (n : ι), ∀ y ∈ Metric.ball x r, HasFDerivAt (f n) (f' n y) y
hfg : Cauchy (map (fun n => f n x) l)
this✝ : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 E
this : NeBot l
hr : 0 < r
⊢ UniformCauchySeqOn f l (Metric.ball x r)

Next tactic:
rw [SeminormedAddGroup.uniformCauchySeqOn_iff_tendstoUniformlyOn_zero] at hf' ⊢
/-- A variant of the second fundamental theorem of calculus (FTC-2): If a sequence of functions
between real or complex normed spaces are differentiable on a ball centered at `x`, they
form a Cauchy sequence _at_ `x`, and their derivatives are Cauchy uniformly on the ball, then the
functions form a uniform Cauchy sequence on the ball.

NOTE: The fact that we work on a ball is typically all that is necessary to work with power series
and Dirichlet series (our primary use case). However, this can be generalized by replacing the ball
with any connected, bounded, open set and replacing uniform convergence with local uniform
convergence. See `cauchy_map_of_uniformCauchySeqOn_fderiv`.
-/
theorem uniformCauchySeqOn_ball_of_fderiv {r : ℝ} (hf' : UniformCauchySeqOn f' l (Metric.ball x r))
    (hf : ∀ n : ι, ∀ y : E, y ∈ Metric.ball x r → HasFDerivAt (f n) (f' n y) y)
    (hfg : Cauchy (map (fun n => f n x) l)) : UniformCauchySeqOn f l (Metric.ball x r) := by
  letI : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 _
  have : NeBot l := (cauchy_map_iff.1 hfg).1
  rcases le_or_lt r 0 with (hr | hr)
  · simp only [Metric.ball_eq_empty.2 hr, UniformCauchySeqOn, Set.mem_empty_iff_false,
      IsEmpty.forall_iff, eventually_const, imp_true_iff]
  rw [SeminormedAddGroup.uniformCauchySeqOn_iff_tendstoUniformlyOn_zero] at hf' ⊢
  

Proof state:
case inr
ι : Type u_1
l : Filter ι
E : Type u_2
inst✝⁴ : NormedAddCommGroup E
𝕜 : Type u_3
inst✝³ : IsROrC 𝕜
inst✝² : NormedSpace 𝕜 E
G : Type u_4
inst✝¹ : NormedAddCommGroup G
inst✝ : NormedSpace 𝕜 G
f : ι → E → G
g : E → G
f' : ι → E → E →L[𝕜] G
g' : E → E →L[𝕜] G
x : E
r : ℝ
hf' : TendstoUniformlyOn (fun n z => f' n.1 z - f' n.2 z) 0 (l ×ˢ l) (Metric.ball x r)
hf : ∀ (n : ι), ∀ y ∈ Metric.ball x r, HasFDerivAt (f n) (f' n y) y
hfg : Cauchy (map (fun n => f n x) l)
this✝ : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 E
this : NeBot l
hr : 0 < r
⊢ TendstoUniformlyOn (fun n z => f n.1 z - f n.2 z) 0 (l ×ˢ l) (Metric.ball x r)

Next tactic:
suffices
    TendstoUniformlyOn (fun (n : ι × ι) (z : E) => f n.1 z - f n.2 z - (f n.1 x - f n.2 x)) 0
        (l ×ˢ l) (Metric.ball x r) ∧
      TendstoUniformlyOn (fun (n : ι × ι) (_ : E) => f n.1 x - f n.2 x) 0
        (l ×ˢ l) (Metric.ball x r) by
    have := this.1.add this.2
    rw [add_zero] at this
    refine' this.congr _
    apply eventually_of_forall
    intro n z _
    simp
/-- A variant of the second fundamental theorem of calculus (FTC-2): If a sequence of functions
between real or complex normed spaces are differentiable on a ball centered at `x`, they
form a Cauchy sequence _at_ `x`, and their derivatives are Cauchy uniformly on the ball, then the
functions form a uniform Cauchy sequence on the ball.

NOTE: The fact that we work on a ball is typically all that is necessary to work with power series
and Dirichlet series (our primary use case). However, this can be generalized by replacing the ball
with any connected, bounded, open set and replacing uniform convergence with local uniform
convergence. See `cauchy_map_of_uniformCauchySeqOn_fderiv`.
-/
theorem uniformCauchySeqOn_ball_of_fderiv {r : ℝ} (hf' : UniformCauchySeqOn f' l (Metric.ball x r))
    (hf : ∀ n : ι, ∀ y : E, y ∈ Metric.ball x r → HasFDerivAt (f n) (f' n y) y)
    (hfg : Cauchy (map (fun n => f n x) l)) : UniformCauchySeqOn f l (Metric.ball x r) := by
  letI : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 _
  have : NeBot l := (cauchy_map_iff.1 hfg).1
  rcases le_or_lt r 0 with (hr | hr)
  · simp only [Metric.ball_eq_empty.2 hr, UniformCauchySeqOn, Set.mem_empty_iff_false,
      IsEmpty.forall_iff, eventually_const, imp_true_iff]
  rw [SeminormedAddGroup.uniformCauchySeqOn_iff_tendstoUniformlyOn_zero] at hf' ⊢
  suffices
    TendstoUniformlyOn (fun (n : ι × ι) (z : E) => f n.1 z - f n.2 z - (f n.1 x - f n.2 x)) 0
        (l ×ˢ l) (Metric.ball x r) ∧
      TendstoUniformlyOn (fun (n : ι × ι) (_ : E) => f n.1 x - f n.2 x) 0
        (l ×ˢ l) (Metric.ball x r) by
    

Proof state:
ι : Type u_1
l : Filter ι
E : Type u_2
inst✝⁴ : NormedAddCommGroup E
𝕜 : Type u_3
inst✝³ : IsROrC 𝕜
inst✝² : NormedSpace 𝕜 E
G : Type u_4
inst✝¹ : NormedAddCommGroup G
inst✝ : NormedSpace 𝕜 G
f : ι → E → G
g : E → G
f' : ι → E → E →L[𝕜] G
g' : E → E →L[𝕜] G
x : E
r : ℝ
hf' : TendstoUniformlyOn (fun n z => f' n.1 z - f' n.2 z) 0 (l ×ˢ l) (Metric.ball x r)
hf : ∀ (n : ι), ∀ y ∈ Metric.ball x r, HasFDerivAt (f n) (f' n y) y
hfg : Cauchy (map (fun n => f n x) l)
this✝¹ : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 E
this✝ : NeBot l
hr : 0 < r
this :
  TendstoUniformlyOn (fun n z => f n.1 z - f n.2 z - (f n.1 x - f n.2 x)) 0 (l ×ˢ l) (Metric.ball x r) ∧
    TendstoUniformlyOn (fun n x_1 => f n.1 x - f n.2 x) 0 (l ×ˢ l) (Metric.ball x r)
⊢ TendstoUniformlyOn (fun n z => f n.1 z - f n.2 z) 0 (l ×ˢ l) (Metric.ball x r)

Next tactic:
have := this.1.add this.2
/-- A variant of the second fundamental theorem of calculus (FTC-2): If a sequence of functions
between real or complex normed spaces are differentiable on a ball centered at `x`, they
form a Cauchy sequence _at_ `x`, and their derivatives are Cauchy uniformly on the ball, then the
functions form a uniform Cauchy sequence on the ball.

NOTE: The fact that we work on a ball is typically all that is necessary to work with power series
and Dirichlet series (our primary use case). However, this can be generalized by replacing the ball
with any connected, bounded, open set and replacing uniform convergence with local uniform
convergence. See `cauchy_map_of_uniformCauchySeqOn_fderiv`.
-/
theorem uniformCauchySeqOn_ball_of_fderiv {r : ℝ} (hf' : UniformCauchySeqOn f' l (Metric.ball x r))
    (hf : ∀ n : ι, ∀ y : E, y ∈ Metric.ball x r → HasFDerivAt (f n) (f' n y) y)
    (hfg : Cauchy (map (fun n => f n x) l)) : UniformCauchySeqOn f l (Metric.ball x r) := by
  letI : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 _
  have : NeBot l := (cauchy_map_iff.1 hfg).1
  rcases le_or_lt r 0 with (hr | hr)
  · simp only [Metric.ball_eq_empty.2 hr, UniformCauchySeqOn, Set.mem_empty_iff_false,
      IsEmpty.forall_iff, eventually_const, imp_true_iff]
  rw [SeminormedAddGroup.uniformCauchySeqOn_iff_tendstoUniformlyOn_zero] at hf' ⊢
  suffices
    TendstoUniformlyOn (fun (n : ι × ι) (z : E) => f n.1 z - f n.2 z - (f n.1 x - f n.2 x)) 0
        (l ×ˢ l) (Metric.ball x r) ∧
      TendstoUniformlyOn (fun (n : ι × ι) (_ : E) => f n.1 x - f n.2 x) 0
        (l ×ˢ l) (Metric.ball x r) by
    have := this.1.add this.2
    

Proof state:
ι : Type u_1
l : Filter ι
E : Type u_2
inst✝⁴ : NormedAddCommGroup E
𝕜 : Type u_3
inst✝³ : IsROrC 𝕜
inst✝² : NormedSpace 𝕜 E
G : Type u_4
inst✝¹ : NormedAddCommGroup G
inst✝ : NormedSpace 𝕜 G
f : ι → E → G
g : E → G
f' : ι → E → E →L[𝕜] G
g' : E → E →L[𝕜] G
x : E
r : ℝ
hf' : TendstoUniformlyOn (fun n z => f' n.1 z - f' n.2 z) 0 (l ×ˢ l) (Metric.ball x r)
hf : ∀ (n : ι), ∀ y ∈ Metric.ball x r, HasFDerivAt (f n) (f' n y) y
hfg : Cauchy (map (fun n => f n x) l)
this✝² : NormedSpace ℝ E := NormedSpace.restrictScalars ℝ 𝕜 E
this✝¹ : NeBot l
hr : 0 < r
this✝ :
  TendstoUniformlyOn (fun n z => f n.1 z - f n.2 z - (f n.1 x - f n.2 x)) 0 (l ×ˢ l) (Metric.ball x r) ∧
    TendstoUniformlyOn (fun n x_1 => f n.1 x - f n.2 x) 0 (l ×ˢ l) (Metric.ball x r)
this :
  TendstoUniformlyOn ((fun n z => f n.1 z - f n.2 z - (f n.1 x - f n.2 x)) + fun n x_1 => f n.1 x - f n.2 x) (0 + 0)
    (l ×ˢ l) (Metric.ball x r)
⊢ TendstoUniformlyOn (fun n z => f n.1 z - f n.2 z) 0 (l ×ˢ l) (Metric.ball x r)

Next tactic:
rw [add_zero] at this