import Mathlib
import OAI.Computability.MaxCut.PCP.CayleySpectral

namespace OAI

section

/-!
Overlay a regular constraint graph with an actual regular port graph whose
new constraints always accept. The port sets are disjoint, so all original
rejected darts are retained exactly. The averaging operator is the actual
degree-weighted mixture of the two graph averages.
-/

open scoped BigOperators

namespace MaxCutGames.Foundations.PCP.Overlay

open PoweringWalks SpectralReturn

variable {V D E A : Type*}

def originalPortGraph (G : ConstraintGraph V (V × D) A) : PortGraph V D where
  rot := G.reverse
  rot_involutive := G.reverse_involutive

def rotate (G : PortGraph V D) (H : PortGraph V E) :
    V × (D ⊕ E) → V × (D ⊕ E)
  | (v, Sum.inl d) => ((G.rot (v, d)).1, Sum.inl (G.rot (v, d)).2)
  | (v, Sum.inr e) => ((H.rot (v, e)).1, Sum.inr (H.rot (v, e)).2)

theorem rotate_involutive (G : PortGraph V D) (H : PortGraph V E) :
    Function.Involutive (rotate G H) := by
  rintro ⟨v, p⟩
  rcases p with d | e
  · exact congrArg (fun a : V × D => (a.1, (Sum.inl a.2 : D ⊕ E)))
      (G.rot_involutive (v, d))
  · exact congrArg (fun a : V × E => (a.1, (Sum.inr a.2 : D ⊕ E)))
      (H.rot_involutive (v, e))

def portGraph (G : PortGraph V D) (H : PortGraph V E) : PortGraph V (D ⊕ E) where
  rot :=
    { toFun := rotate G H
      invFun := rotate G H
      left_inv := rotate_involutive G H
      right_inv := rotate_involutive G H }
  rot_involutive := rotate_involutive G H

@[simp] theorem portGraph_rot_inl (G : PortGraph V D) (H : PortGraph V E)
    (v : V) (d : D) :
    (portGraph G H).rot (v, Sum.inl d) =
      ((G.rot (v, d)).1, Sum.inl (G.rot (v, d)).2) := rfl

@[simp] theorem portGraph_rot_inr (G : PortGraph V D) (H : PortGraph V E)
    (v : V) (e : E) :
    (portGraph G H).rot (v, Sum.inr e) =
      ((H.rot (v, e)).1, Sum.inr (H.rot (v, e)).2) := rfl

/-- Keep the original tests and add always-accepting tests on the overlay ports. -/
def constraintGraph (G : ConstraintGraph V (V × D) A) (H : PortGraph V E) :
    ConstraintGraph V (V × (D ⊕ E)) A where
  reverse := (portGraph (originalPortGraph G) H).rot
  reverse_involutive := (portGraph (originalPortGraph G) H).rot_involutive
  tail := Prod.fst
  accepts p a b := match p.2 with
    | Sum.inl d => G.accepts (p.1, d) a b
    | Sum.inr _ => true
  reverse_accepts := by
    rintro ⟨v, p⟩ a b
    rcases p with d | e
    · exact G.reverse_accepts (v, d) a b
    · rfl

@[simp] theorem constraintGraph_tail (G : ConstraintGraph V (V × D) A)
    (H : PortGraph V E) : (constraintGraph G H).tail = Prod.fst := rfl

@[simp] theorem constraintGraph_portGraph (G : ConstraintGraph V (V × D) A)
    (H : PortGraph V E) :
    originalPortGraph (constraintGraph G H) = portGraph (originalPortGraph G) H := rfl

@[simp] theorem edgeSatisfied_inl (G : ConstraintGraph V (V × D) A)
    (H : PortGraph V E) (htail : G.tail = Prod.fst)
    (labeling : V → A) (v : V) (d : D) :
    (constraintGraph G H).edgeSatisfied labeling (v, Sum.inl d) =
      G.edgeSatisfied labeling (v, d) := by
  change G.accepts (v, d) (labeling v) (labeling (G.reverse (v, d)).1) =
    G.accepts (v, d) (labeling (G.tail (v, d)))
      (labeling (G.tail (G.reverse (v, d))))
  rw [htail]

@[simp] theorem edgeSatisfied_inr (G : ConstraintGraph V (V × D) A)
    (H : PortGraph V E) (labeling : V → A) (v : V) (e : E) :
    (constraintGraph G H).edgeSatisfied labeling (v, Sum.inr e) = true := rfl

theorem complete (G : ConstraintGraph V (V × D) A) (H : PortGraph V E)
    (htail : G.tail = Prod.fst) (labeling : V → A)
    (h : ∀ a, G.edgeSatisfied labeling a = true) :
    ∀ a, (constraintGraph G H).edgeSatisfied labeling a = true := by
  rintro ⟨v, p⟩
  rcases p with d | e
  · rw [edgeSatisfied_inl G H htail]
    exact h (v, d)
  · rfl

theorem satisfiable_iff (G : ConstraintGraph V (V × D) A) (H : PortGraph V E)
    (htail : G.tail = Prod.fst) :
    (constraintGraph G H).Satisfiable ↔ G.Satisfiable := by
  constructor
  · rintro ⟨labeling, h⟩
    refine ⟨labeling, ?_⟩
    rintro ⟨v, d⟩
    rw [← edgeSatisfied_inl G H htail]
    exact h (v, Sum.inl d)
  · rintro ⟨labeling, h⟩
    exact ⟨labeling, complete G H htail labeling h⟩

/-- Every labeling rejects exactly the same number of darts before and after overlay. -/
theorem rejectionCount_eq [Fintype V] [Fintype D] [Fintype E]
    (G : ConstraintGraph V (V × D) A) (H : PortGraph V E)
    (htail : G.tail = Prod.fst) (labeling : V → A) :
    (constraintGraph G H).rejectionCount labeling = G.rejectionCount labeling := by
  classical
  simp only [DegreeReplacement.rejectionCount_eq_sum, Fintype.sum_prod_type,
    Fintype.sum_sum_type]
  simp [edgeSatisfied_inl G H htail]

theorem card_ports [Fintype D] [Fintype E] :
    Fintype.card (D ⊕ E) = Fintype.card D + Fintype.card E := Fintype.card_sum

theorem card_darts [Fintype V] [Fintype D] [Fintype E] :
    Fintype.card (V × (D ⊕ E)) =
      Fintype.card (V × D) + Fintype.card (V × E) := by
  simp only [Fintype.card_prod, Fintype.card_sum, Nat.mul_add]

variable [Fintype V] [Fintype D] [Fintype E]

omit [Fintype V] in
/-- The normalized graph average weights the components by their actual port counts. -/
theorem operator_mix [Nonempty D] [Nonempty E]
    (G : PortGraph V D) (H : PortGraph V E) (f : V → ℝ) (v : V) :
    averagingOperator (portGraph G H) f v =
      (Fintype.card D : ℝ) / ((Fintype.card D : ℝ) + Fintype.card E) *
        averagingOperator G f v +
      (Fintype.card E : ℝ) / ((Fintype.card D : ℝ) + Fintype.card E) *
        averagingOperator H f v := by
  have hd : (0 : ℝ) < Fintype.card D := by
    exact_mod_cast (Fintype.card_pos : 0 < Fintype.card D)
  have he : (0 : ℝ) < Fintype.card E := by
    exact_mod_cast (Fintype.card_pos : 0 < Fintype.card E)
  change mean (fun p : D ⊕ E => f ((portGraph G H).rot (v, p)).1) = _
  rw [mean_eq_sum_div_card]
  simp only [Fintype.sum_sum_type, Fintype.card_sum, Nat.cast_add,
    portGraph_rot_inl, portGraph_rot_inr]
  unfold averagingOperator
  rw [Fintype.expect_eq_sum_div_card, Fintype.expect_eq_sum_div_card]
  field_simp [hd.ne', he.ne', (add_pos hd he).ne']

/-- Convexity of the squared energy, proved pointwise before taking the finite mean. -/
theorem energy_convex_mix (a b : ℝ) (ha : 0 ≤ a) (hb : 0 ≤ b) (hab : a + b = 1)
    (f g : V → ℝ) :
    energy (fun v => a * f v + b * g v) ≤ a * energy f + b * energy g := by
  calc
    energy (fun v => a * f v + b * g v) ≤
        mean (fun v => a * (f v) ^ 2 + b * (g v) ^ 2) := by
      apply mean_mono
      intro v
      have hnormalize := congrArg (fun t : ℝ => t * (a * (f v) ^ 2 + b * (g v) ^ 2)) hab
      have hnonneg := mul_nonneg (mul_nonneg ha hb) (sq_nonneg (f v - g v))
      nlinarith
    _ = a * energy f + b * energy g := by
      rw [mean_add, mean_mul_left, mean_mul_left]
      rfl

/-- Actual overlay contraction from the supplied graph's spectral bound and
the proved energy contraction of an arbitrary reversible regular graph. -/
theorem energy_bound [Nonempty V] [Nonempty D] [Nonempty E]
    (G : PortGraph V D) (H : PortGraph V E) (lambda : ℝ)
    (certificate : SpectralCertificate H lambda) (f : V → ℝ) (hf : mean f = 0) :
    energy (averagingOperator (portGraph G H) f) ≤
      ((Fintype.card D : ℝ) + (Fintype.card E : ℝ) * lambda ^ 2) /
        ((Fintype.card D : ℝ) + Fintype.card E) * energy f := by
  let a : ℝ := (Fintype.card D : ℝ) / ((Fintype.card D : ℝ) + Fintype.card E)
  let b : ℝ := (Fintype.card E : ℝ) / ((Fintype.card D : ℝ) + Fintype.card E)
  have hd : (0 : ℝ) < Fintype.card D := by
    exact_mod_cast (Fintype.card_pos : 0 < Fintype.card D)
  have he : (0 : ℝ) < Fintype.card E := by
    exact_mod_cast (Fintype.card_pos : 0 < Fintype.card E)
  have hden : (0 : ℝ) < (Fintype.card D : ℝ) + Fintype.card E := add_pos hd he
  have ha : 0 ≤ a := div_nonneg hd.le hden.le
  have hb : 0 ≤ b := div_nonneg he.le hden.le
  have hab : a + b = 1 := by
    dsimp [a, b]
    rw [← add_div, div_self hden.ne']
  have hop : averagingOperator (portGraph G H) f =
      fun v => a * averagingOperator G f v + b * averagingOperator H f v := by
    funext v
    exact operator_mix G H f v
  calc
    energy (averagingOperator (portGraph G H) f) ≤
        a * energy (averagingOperator G f) + b * energy (averagingOperator H f) := by
      rw [hop]
      exact energy_convex_mix a b ha hb hab _ _
    _ ≤ a * energy f + b * (lambda ^ 2 * energy f) :=
      add_le_add
        (mul_le_mul_of_nonneg_left (ZigzagSpectral.averaging_energy_le G f) ha)
        (mul_le_mul_of_nonneg_left (certificate.contraction f hf) hb)
    _ = _ := by
      dsimp [a, b]
      ring

/-- With one more original port than overlay ports, a half-spectral overlay
has squared contraction at most three quarters. -/
theorem energy_bound_three_quarters [Nonempty V]
    (G : PortGraph V D) (H : PortGraph V E)
    (hdegree : Fintype.card D = Fintype.card E + 1) (hpositive : 1 ≤ Fintype.card E)
    (certificate : SpectralCertificate H (1 / 2 : ℝ))
    (f : V → ℝ) (hf : mean f = 0) :
    energy (averagingOperator (portGraph G H) f) ≤ (3 / 4 : ℝ) * energy f := by
  let _ : Nonempty E := Fintype.card_pos_iff.mp (lt_of_lt_of_le Nat.zero_lt_one hpositive)
  let _ : Nonempty D := Fintype.card_pos_iff.mp (by omega)
  have he : (1 : ℝ) ≤ Fintype.card E := by exact_mod_cast hpositive
  have hd : (Fintype.card D : ℝ) = (Fintype.card E : ℝ) + 1 := by exact_mod_cast hdegree
  have hden : (0 : ℝ) < (Fintype.card D : ℝ) + Fintype.card E := by linarith
  have hcoefficient :
      ((Fintype.card D : ℝ) + (Fintype.card E : ℝ) * (1 / 2 : ℝ) ^ 2) /
        ((Fintype.card D : ℝ) + Fintype.card E) ≤ (3 / 4 : ℝ) := by
    apply (div_le_iff₀ hden).mpr
    nlinarith
  exact (energy_bound G H (1 / 2) certificate f hf).trans
    (mul_le_mul_of_nonneg_right hcoefficient (energy_nonnegative f))

theorem spectralCertificate_seven_eighths [Nonempty V]
    (G : PortGraph V D) (H : PortGraph V E)
    (hdegree : Fintype.card D = Fintype.card E + 1) (hpositive : 1 ≤ Fintype.card E)
    (certificate : SpectralCertificate H (1 / 2 : ℝ)) :
    SpectralCertificate (portGraph G H) (7 / 8 : ℝ) where
  nonnegative := by norm_num
  lt_one := by norm_num
  contraction := by
    intro f hf
    exact (energy_bound_three_quarters G H hdegree hpositive certificate f hf).trans
      (mul_le_mul_of_nonneg_right (by norm_num : (3 / 4 : ℝ) ≤ (7 / 8 : ℝ) ^ 2)
        (energy_nonnegative f))

/-- The certificate belongs to the exact underlying graph of the overlaid constraints. -/
theorem constraintGraph_spectralCertificate [Nonempty V]
    (G : ConstraintGraph V (V × D) A) (H : PortGraph V E)
    (hdegree : Fintype.card D = Fintype.card E + 1) (hpositive : 1 ≤ Fintype.card E)
    (certificate : SpectralCertificate H (1 / 2 : ℝ)) :
    SpectralCertificate (originalPortGraph (constraintGraph G H)) (7 / 8 : ℝ) := by
  rw [constraintGraph_portGraph]
  exact spectralCertificate_seven_eighths (originalPortGraph G) H
    hdegree hpositive certificate

end MaxCutGames.Foundations.PCP.Overlay

end

/-! Additive smoothing of the actual fair Boolean-tape length law.  This is
the finite-probability input to the lazy-walk amplification argument. -/

namespace MaxCutGames.Foundations.PCP.LazySmoothing

open scoped BigOperators

def bitCount {n : ℕ} (tape : Fin n → Bool) : ℕ :=
  ∑ i, if tape i then 1 else 0

noncomputable def binomialMean (n : ℕ) (g : ℕ → ℝ) : ℝ :=
  Finset.univ.expect (fun tape : Fin n → Bool => g (bitCount tape))

def splitTape (n : ℕ) : (Fin (n + 1) → Bool) ≃ (Bool × (Fin n → Bool)) where
  toFun x := (x 0, fun i => x i.succ)
  invFun x := Fin.cases x.1 x.2
  left_inv x := by
    funext i
    exact Fin.cases rfl (fun _ => rfl) i
  right_inv _ := rfl

theorem bitCount_succ {n : ℕ} (x : Fin (n + 1) → Bool) :
    bitCount x = (if x 0 then 1 else 0) + bitCount (fun i => x i.succ) := by
  exact Fin.sum_univ_succ _

theorem mean_congr {n : ℕ} {f g : ℕ → ℝ} (h : ∀ k, f k = g k) :
    binomialMean n f = binomialMean n g := by
  unfold binomialMean
  exact Finset.expect_congr rfl (fun x _ => h (bitCount x))

@[simp] theorem mean_const (n : ℕ) (c : ℝ) :
    binomialMean n (fun _ => c) = c := Fintype.expect_const c

theorem mean_add (n : ℕ) (f g : ℕ → ℝ) :
    binomialMean n (fun k => f k + g k) = binomialMean n f + binomialMean n g :=
  Finset.expect_add_distrib _ _ _

theorem mean_sub (n : ℕ) (f g : ℕ → ℝ) :
    binomialMean n (fun k => f k - g k) = binomialMean n f - binomialMean n g :=
  Finset.expect_sub_distrib _ _ _

theorem mean_mul (n : ℕ) (c : ℝ) (f : ℕ → ℝ) :
    binomialMean n (fun k => c * f k) = c * binomialMean n f := by
  exact (Finset.mul_expect Finset.univ (fun x : Fin n → Bool => f (bitCount x)) c).symm

@[simp] theorem mean_zero (g : ℕ → ℝ) : binomialMean 0 g = g 0 := by
  simp [binomialMean, bitCount]

theorem expect_bool (f : Bool → ℝ) : Finset.univ.expect f = (f false + f true) / 2 := by
  rw [Finset.expect_eq_sum_div_card]
  simp [Fintype.univ_bool, add_comm]

theorem mean_succ (n : ℕ) (g : ℕ → ℝ) :
    binomialMean (n + 1) g =
      (binomialMean n g + binomialMean n (fun k => g (k + 1))) / 2 := by
  unfold binomialMean
  rw [Fintype.expect_equiv (splitTape n)
    (fun x => g (bitCount x))
    (fun x => g ((if x.1 then 1 else 0) + bitCount x.2))
    (fun x => congrArg g (bitCount_succ x))]
  rw [← Finset.univ_product_univ, Finset.expect_product, expect_bool]
  simp [Nat.add_comm]

def score (n k : ℕ) : ℝ := (n : ℝ) - 2 * (k : ℝ)

noncomputable def scoreMean (n : ℕ) (g : ℕ → ℝ) : ℝ :=
  binomialMean n (fun k => score n k * g k)

@[simp] theorem scoreMean_zero (g : ℕ → ℝ) : scoreMean 0 g = 0 := by
  simp [scoreMean, score]

theorem scoreMean_succ (n : ℕ) (g : ℕ → ℝ) :
    scoreMean (n + 1) g =
      ((scoreMean n g + binomialMean n g) +
       (scoreMean n (fun k => g (k + 1)) -
        binomialMean n (fun k => g (k + 1)))) / 2 := by
  unfold scoreMean
  rw [mean_succ]
  have h₁ : (fun k => score (n + 1) k * g k) =
      (fun k => score n k * g k + g k) := by
    funext k
    simp only [score, Nat.cast_add, Nat.cast_one]
    ring
  have h₂ : (fun k => score (n + 1) (k + 1) * g (k + 1)) =
      (fun k => score n k * g (k + 1) - g (k + 1)) := by
    funext k
    simp only [score, Nat.cast_add, Nat.cast_one]
    ring
  rw [h₁, h₂, mean_add, mean_sub]

/-- Exact finite integration by parts for the fair Boolean-tape law. -/
theorem score_identity (n : ℕ) (g : ℕ → ℝ) :
    scoreMean (n + 1) g = ((n : ℝ) + 1) / 2 *
      (binomialMean n g - binomialMean n (fun k => g (k + 1))) := by
  induction n generalizing g with
  | zero => simp [scoreMean_succ]; ring
  | succ n ih =>
    rw [scoreMean_succ, ih g, ih (fun k => g (k + 1))]
    simp only [mean_succ, Nat.cast_add, Nat.cast_one]
    ring

theorem score_mean_zero (n : ℕ) : binomialMean n (score n) = 0 := by
  cases n with
  | zero => simp [score]
  | succ n =>
    have h := score_identity n (fun _ => 1)
    simpa [scoreMean] using h

/-- The score variance is proved from the actual tape distribution. -/
theorem score_second_moment (n : ℕ) :
    binomialMean n (fun k => score n k ^ 2) = (n : ℝ) := by
  cases n with
  | zero => simp [score]
  | succ n =>
    have h := score_identity n (score (n + 1))
    have hd : binomialMean n (score (n + 1)) -
        binomialMean n (fun k => score (n + 1) (k + 1)) = 2 := by
      rw [← mean_sub]
      calc
        _ = binomialMean n (fun _ => 2) := by
          apply mean_congr
          intro k
          simp only [score, Nat.cast_add, Nat.cast_one]
          ring
        _ = 2 := mean_const _ _
    rw [hd] at h
    simpa [scoreMean, pow_two] using h

theorem score_difference (n : ℕ) (g : ℕ → ℝ) :
    scoreMean (n + 1) g = ((n : ℝ) + 1) *
      (binomialMean n g - binomialMean (n + 1) g) := by
  rw [score_identity, mean_succ]
  ring

/-- Cauchy--Schwarz yields an adjacent-length bound without any support or
small-parameter caveat.  The square form avoids introducing square roots. -/
theorem adjacent_difference_sq (n : ℕ) (g : ℕ → ℝ)
    (hg : ∀ k, 0 ≤ g k ∧ g k ≤ 1) :
    4 * ((n : ℝ) + 1) *
      (binomialMean n g - binomialMean (n + 1) g) ^ 2 ≤ 1 := by
  have hc : binomialMean (n + 1)
      (fun k => score (n + 1) k * (g k - 1 / 2)) = scoreMean (n + 1) g := by
    calc
      _ = binomialMean (n + 1) (fun k =>
          score (n + 1) k * g k - (1 / 2) * score (n + 1) k) := by
        apply mean_congr
        intro k
        ring
      _ = scoreMean (n + 1) g := by
        rw [mean_sub, mean_mul, score_mean_zero]
        simp [scoreMean]
  have hv : binomialMean (n + 1) (fun k => (g k - 1 / 2) ^ 2) ≤ 1 / 4 := by
    apply Finset.expect_le Finset.univ_nonempty
    intro tape _
    have h₀ := (hg (bitCount tape)).1
    have h₁ := (hg (bitCount tape)).2
    have hp := mul_nonneg h₀ (sub_nonneg.mpr h₁)
    nlinarith
  have hcs : (binomialMean (n + 1)
        (fun k => score (n + 1) k * (g k - 1 / 2))) ^ 2 ≤
      binomialMean (n + 1) (fun k => score (n + 1) k ^ 2) *
        binomialMean (n + 1) (fun k => (g k - 1 / 2) ^ 2) :=
    Finset.expect_mul_sq_le_sq_mul_sq Finset.univ _ _
  rw [hc, score_difference, score_second_moment] at hcs
  have hn : 0 < (n : ℝ) + 1 := by positivity
  have hm := mul_le_mul_of_nonneg_left hv (le_of_lt hn)
  simp only [Nat.cast_add, Nat.cast_one] at hcs
  apply (mul_le_mul_iff_left₀ hn).mp
  nlinarith

theorem adjacent_difference_le (n : ℕ) (g : ℕ → ℝ)
    (hg : ∀ k, 0 ≤ g k ∧ g k ≤ 1) (r : ℝ) (hr : 0 < r)
    (hrn : r ^ 2 ≤ 4 * ((n : ℝ) + 1)) :
    |binomialMean n g - binomialMean (n + 1) g| ≤ 1 / r := by
  have hs := adjacent_difference_sq n g hg
  have hd := sq_nonneg (binomialMean n g - binomialMean (n + 1) g)
  have hh := mul_le_mul_of_nonneg_right hrn hd
  rw [le_div_iff₀ hr]
  have ha := sq_abs (binomialMean n g - binomialMean (n + 1) g)
  have hp : 0 ≤ |binomialMean n g - binomialMean (n + 1) g| * r :=
    mul_nonneg (abs_nonneg _) (le_of_lt hr)
  nlinarith [sq_nonneg (|binomialMean n g - binomialMean (n + 1) g| * r - 1)]

theorem mean_shift_le (n d : ℕ) (g : ℕ → ℝ)
    (hg : ∀ k, 0 ≤ g k ∧ g k ≤ 1) (r : ℝ) (hr : 0 < r)
    (hrn : r ^ 2 ≤ 4 * ((n : ℝ) + 1)) :
    |binomialMean (n + d) g - binomialMean n g| ≤ (d : ℝ) / r := by
  induction d with
  | zero => simp
  | succ d ih =>
    have hrnd : r ^ 2 ≤ 4 * ((n + d : ℕ) + 1 : ℝ) := by
      push_cast
      have : (0 : ℝ) ≤ d := Nat.cast_nonneg _
      linarith
    have ha := adjacent_difference_le (n + d) g hg r hr hrnd
    rw [abs_sub_comm] at ha
    calc
      _ ≤ |binomialMean (n + d + 1) g - binomialMean (n + d) g| +
          |binomialMean (n + d) g - binomialMean n g| := abs_sub_le _ _ _
      _ ≤ 1 / r + (d : ℝ) / r := add_le_add ha ih
      _ = ((d + 1 : ℕ) : ℝ) / r := by push_cast; ring

theorem mean_ordered_difference_le (m n : ℕ) (hmn : m ≤ n) (g : ℕ → ℝ)
    (hg : ∀ k, 0 ≤ g k ∧ g k ≤ 1) (r : ℝ) (hr : 0 < r)
    (hrm : r ^ 2 ≤ 4 * ((m : ℝ) + 1)) :
    |binomialMean n g - binomialMean m g| ≤ ((n - m : ℕ) : ℝ) / r := by
  simpa [Nat.add_sub_of_le hmn] using mean_shift_le m (n - m) g hg r hr hrm

theorem mean_window_le (N M m : ℕ) (hlo : N - M ≤ m) (hhi : m ≤ N + M)
    (g : ℕ → ℝ) (hg : ∀ k, 0 ≤ g k ∧ g k ≤ 1)
    (r : ℝ) (hr : 0 < r) (hbase : r ^ 2 ≤ 4 * (((N - M : ℕ) : ℝ) + 1)) :
    |binomialMean m g - binomialMean N g| ≤ (M : ℝ) / r := by
  by_cases hmN : m ≤ N
  · have hrm : r ^ 2 ≤ 4 * ((m : ℝ) + 1) := by
      have : ((N - M : ℕ) : ℝ) ≤ (m : ℝ) := by exact_mod_cast hlo
      linarith
    have h := mean_ordered_difference_le m N hmN g hg r hr hrm
    rw [abs_sub_comm] at h
    refine h.trans (div_le_div_of_nonneg_right ?_ (le_of_lt hr))
    have : N - m ≤ M := by omega
    exact_mod_cast this
  · have hNm : N ≤ m := Nat.le_of_lt (Nat.lt_of_not_ge hmN)
    have hrN : r ^ 2 ≤ 4 * ((N : ℝ) + 1) := by
      have : ((N - M : ℕ) : ℝ) ≤ (N : ℝ) := by exact_mod_cast Nat.sub_le N M
      linarith
    have h := mean_ordered_difference_le N m hNm g hg r hr hrN
    refine h.trans (div_le_div_of_nonneg_right ?_ (le_of_lt hr))
    have : m - N ≤ M := by omega
    exact_mod_cast this

/-- A convenient square center for a fixed alphabet. -/
theorem square_window_le (q M m : ℕ) (hq : 1 ≤ q) (hM : 1 ≤ M)
    (hlo : (4 * q * M) ^ 2 - M ≤ m) (hhi : m ≤ (4 * q * M) ^ 2 + M)
    (g : ℕ → ℝ) (hg : ∀ k, 0 ≤ g k ∧ g k ≤ 1) :
    |binomialMean m g - binomialMean ((4 * q * M) ^ 2) g| ≤ 1 / (4 * (q : ℝ)) := by
  have hqR : (1 : ℝ) ≤ q := by exact_mod_cast hq
  have hMR : (1 : ℝ) ≤ M := by exact_mod_cast hM
  have hR : (4 : ℝ) * M ≤ ((4 * q * M : ℕ) : ℝ) := by
    push_cast
    nlinarith
  have hRM : (M : ℝ) ≤ (((4 * q * M : ℕ) : ℝ)) ^ 2 := by nlinarith
  have hMN : M ≤ (4 * q * M) ^ 2 := by exact_mod_cast hRM
  have hr : (0 : ℝ) < ((4 * q * M : ℕ) : ℝ) := by linarith
  have hb : (((4 * q * M : ℕ) : ℝ)) ^ 2 ≤
      4 * (((((4 * q * M) ^ 2 - M : ℕ)) : ℝ) + 1) := by
    rw [Nat.cast_sub hMN, Nat.cast_pow]
    nlinarith
  have h := mean_window_le ((4 * q * M) ^ 2) M m hlo hhi g hg
    ((4 * q * M : ℕ) : ℝ) hr hb
  have he : (M : ℝ) / ((4 * q * M : ℕ) : ℝ) = 1 / (4 * (q : ℝ)) := by
    push_cast
    have hq0 : (q : ℝ) ≠ 0 := by linarith
    have hM0 : (M : ℝ) ≠ 0 := by linarith
    field_simp
  rw [he] at h
  exact h

/-- A modal event of probability at least `1/q` retains probability `1/(2q)`
throughout the middle window used in lazy-walk powering. -/
theorem modal_transfer (q M m : ℕ) (hq : 1 ≤ q) (hM : 1 ≤ M)
    (hlo : (4 * q * M) ^ 2 - M ≤ m) (hhi : m ≤ (4 * q * M) ^ 2 + M)
    (g : ℕ → ℝ) (hg : ∀ k, 0 ≤ g k ∧ g k ≤ 1)
    (hmodal : 1 / (q : ℝ) ≤ binomialMean ((4 * q * M) ^ 2) g) :
    1 / (2 * (q : ℝ)) ≤ binomialMean m g := by
  have h := square_window_le q M m hq hM hlo hhi g hg
  have hqR : (0 : ℝ) < q := by exact_mod_cast (Nat.zero_lt_of_lt hq)
  have hrec : 0 < 1 / (q : ℝ) := one_div_pos.mpr hqR
  have he₁ : 1 / (4 * (q : ℝ)) = (1 / (q : ℝ)) / 4 := by ring
  have he₂ : 1 / (2 * (q : ℝ)) = (1 / (q : ℝ)) / 2 := by ring
  rw [he₁] at h
  rw [he₂]
  have hl := (abs_le.mp h).1
  linarith

end MaxCutGames.Foundations.PCP.LazySmoothing

section

/-! The actual half-lazy port graph retains a Boolean stay/step flag and an
ordinary port. This supplies the concrete graph underlying Boolean-tape
length smoothing. -/

namespace MaxCutGames.Foundations.PCP.PoweringWalks

variable {V D : Type*}

def lazyRotate (G : PortGraph V D) : Edge V (Bool × D) → Edge V (Bool × D)
  | (v, (false, d)) => (v, (false, d))
  | (v, (true, d)) => ((G.rot (v, d)).1, (true, (G.rot (v, d)).2))

theorem lazyRotate_involutive (G : PortGraph V D) :
    Function.Involutive (lazyRotate G) := by
  rintro ⟨v, b, d⟩
  cases b with
  | false => rfl
  | true =>
    exact congrArg (fun e : V × D => (e.1, (true, e.2))) (rot_rot G (v, d))

def lazyGraph (G : PortGraph V D) : PortGraph V (Bool × D) where
  rot := {
    toFun := lazyRotate G
    invFun := lazyRotate G
    left_inv := lazyRotate_involutive G
    right_inv := lazyRotate_involutive G }
  rot_involutive := lazyRotate_involutive G

@[simp] theorem lazyGraph_rot_false (G : PortGraph V D) (v : V) (d : D) :
    (lazyGraph G).rot (v, (false, d)) = (v, (false, d)) := rfl

@[simp] theorem lazyGraph_rot_true (G : PortGraph V D) (v : V) (d : D) :
    (lazyGraph G).rot (v, (true, d)) = ((G.rot (v, d)).1, (true, (G.rot (v, d)).2)) := rfl

end MaxCutGames.Foundations.PCP.PoweringWalks

namespace MaxCutGames.Foundations.PCP.PoweringLazy

open PoweringWalks SpectralReturn

variable {V D : Type*}

theorem operator_binomialMean [Fintype D] (G : PortGraph V D)
    (n : Nat) (g : Nat → V → ℝ) (v : V) :
    averagingOperator G (fun w => LazySmoothing.binomialMean n (fun k => g k w)) v =
      LazySmoothing.binomialMean n (fun k => averagingOperator G (g k) v) := by
  unfold averagingOperator LazySmoothing.binomialMean
  exact Finset.expect_comm Finset.univ Finset.univ
    (fun d tape => g (LazySmoothing.bitCount tape) (G.rot (v, d)).1)

theorem averagingOperator_lazy [Fintype D] [Nonempty D]
    (G : PortGraph V D) (f : V → ℝ) (v : V) :
    averagingOperator (lazyGraph G) f v = (f v + averagingOperator G f v) / 2 := by
  change mean (fun bd : Bool × D => f ((lazyGraph G).rot (v, bd)).1) = _
  rw [mean_prod]
  change Finset.univ.expect (fun b : Bool =>
    mean (fun d : D => f ((lazyGraph G).rot (v, (b, d))).1)) = _
  rw [LazySmoothing.expect_bool]
  simp only [lazyGraph_rot_false, lazyGraph_rot_true]
  rw [mean_const]
  rfl

/-- The exact Boolean-tape mixture for the actual lazy transition operator. -/
theorem iterate_lazy_eq_binomialMean [Fintype D] [Nonempty D]
    (G : PortGraph V D) (n : Nat) (f : V → ℝ) :
    iterateOperator (lazyGraph G) n f =
      fun v => LazySmoothing.binomialMean n (fun k => iterateOperator G k f v) := by
  induction n with
  | zero =>
    funext v
    simp [iterateOperator]
  | succ n ih =>
    funext v
    change averagingOperator (lazyGraph G) (iterateOperator (lazyGraph G) n f) v = _
    rw [ih, averagingOperator_lazy, operator_binomialMean, LazySmoothing.mean_succ]
    rfl

theorem iterate_mem_Icc [Fintype D] [Nonempty D]
    (G : PortGraph V D) (f : V → ℝ) (hf : ∀ w, f w ∈ Set.Icc (0 : ℝ) 1)
    (n : Nat) (v : V) : iterateOperator G n f v ∈ Set.Icc (0 : ℝ) 1 := by
  induction n generalizing v with
  | zero => exact hf v
  | succ n ih =>
    change mean (fun d : D => iterateOperator G n f (G.rot (v, d)).1) ∈ Set.Icc (0 : ℝ) 1
    constructor
    · exact mean_nonnegative _ (fun d => (ih ((G.rot (v, d)).1)).1)
    · calc
        mean (fun d : D => iterateOperator G n f (G.rot (v, d)).1) ≤
            mean (fun _ : D => (1 : ℝ)) :=
          mean_mono (fun d => (ih ((G.rot (v, d)).1)).2)
        _ = 1 := mean_const _

/-- The modal lower bound survives every allowed nearby actual lazy length. -/
theorem lazy_endpoint_modal_transfer [Fintype D] [Nonempty D]
    (G : PortGraph V D) (q M m : Nat) (hq : 1 ≤ q) (hM : 1 ≤ M)
    (hlo : (4 * q * M) ^ 2 - M ≤ m) (hhi : m ≤ (4 * q * M) ^ 2 + M)
    (f : V → ℝ) (hf : ∀ w, f w ∈ Set.Icc (0 : ℝ) 1) (v : V)
    (hmodal : 1 / (q : ℝ) ≤ iterateOperator (lazyGraph G) ((4 * q * M) ^ 2) f v) :
    1 / (2 * (q : ℝ)) ≤ iterateOperator (lazyGraph G) m f v := by
  rw [iterate_lazy_eq_binomialMean] at hmodal ⊢
  exact LazySmoothing.modal_transfer q M m hq hM hlo hhi
    (fun k => iterateOperator G k f v) (fun k => iterate_mem_Icc G f hf k v) hmodal

end MaxCutGames.Foundations.PCP.PoweringLazy
end

/-! Lazification of the actual constraint graph. Original ports retain their
tests; the equally many stay-put ports always accept. The reverse is precisely
the existing lazy port-graph rotation used by the spectral and walk lemmas. -/
namespace MaxCutGames.Foundations.PCP.LazyConstraint

open scoped BigOperators
open PoweringWalks

variable {V D A : Type*}

def constraintGraph (G : ConstraintGraph V (V × D) A) :
    ConstraintGraph V (V × (Bool × D)) A where
  reverse := (lazyGraph (Overlay.originalPortGraph G)).rot
  reverse_involutive := (lazyGraph (Overlay.originalPortGraph G)).rot_involutive
  tail := Prod.fst
  accepts e a b := if e.2.1 then G.accepts (e.1,e.2.2) a b else true
  reverse_accepts := by
    rintro ⟨v,b,d⟩ a c
    cases b with
    | false => rfl
    | true => exact G.reverse_accepts (v,d) a c

@[simp] theorem constraintGraph_tail (G : ConstraintGraph V (V × D) A) :
    (constraintGraph G).tail = Prod.fst := rfl

@[simp] theorem constraintGraph_reverse (G : ConstraintGraph V (V × D) A) :
    (constraintGraph G).reverse = (lazyGraph (Overlay.originalPortGraph G)).rot := rfl

@[simp] theorem constraintGraph_portGraph (G : ConstraintGraph V (V × D) A) :
    Overlay.originalPortGraph (constraintGraph G) =
      lazyGraph (Overlay.originalPortGraph G) := rfl

@[simp] theorem edgeSatisfied_false (G : ConstraintGraph V (V × D) A)
    (labeling : V → A) (v : V) (d : D) :
    (constraintGraph G).edgeSatisfied labeling (v,(false,d)) = true := rfl

@[simp] theorem edgeSatisfied_true (G : ConstraintGraph V (V × D) A)
    (htail : G.tail = Prod.fst) (labeling : V → A) (v : V) (d : D) :
    (constraintGraph G).edgeSatisfied labeling (v,(true,d)) =
      G.edgeSatisfied labeling (v,d) := by
  change G.accepts (v,d) (labeling v) (labeling (G.reverse (v,d)).1) =
    G.accepts (v,d) (labeling (G.tail (v,d)))
      (labeling (G.tail (G.reverse (v,d))))
  rw [htail]

theorem complete (G : ConstraintGraph V (V × D) A) (htail : G.tail = Prod.fst)
    (labeling : V → A) (h : ∀ e, G.edgeSatisfied labeling e = true) :
    ∀ e, (constraintGraph G).edgeSatisfied labeling e = true := by
  rintro ⟨v,b,d⟩
  cases b with
  | false => rfl
  | true =>
    rw [edgeSatisfied_true G htail]
    exact h (v,d)

theorem complete_iff (G : ConstraintGraph V (V × D) A) (htail : G.tail = Prod.fst)
    (labeling : V → A) :
    (∀ e, (constraintGraph G).edgeSatisfied labeling e = true) ↔
      ∀ e, G.edgeSatisfied labeling e = true := by
  constructor
  · intro h
    rintro ⟨v,d⟩
    rw [← edgeSatisfied_true G htail]
    exact h (v,(true,d))
  · exact complete G htail labeling

theorem satisfiable_iff (G : ConstraintGraph V (V × D) A) (htail : G.tail = Prod.fst) :
    (constraintGraph G).Satisfiable ↔ G.Satisfiable := by
  constructor
  · rintro ⟨labeling,h⟩
    exact ⟨labeling, (complete_iff G htail labeling).mp h⟩
  · rintro ⟨labeling,h⟩
    exact ⟨labeling, complete G htail labeling h⟩

/-- The equality holds for every labeling, including nonsatisfying ones. -/
theorem rejectionCount_eq [Fintype V] [Fintype D]
    (G : ConstraintGraph V (V × D) A) (htail : G.tail = Prod.fst)
    (labeling : V → A) :
    (constraintGraph G).rejectionCount labeling = G.rejectionCount labeling := by
  classical
  simp only [DegreeReplacement.rejectionCount_eq_sum, Fintype.sum_prod_type,
    Fintype.sum_bool]
  simp [edgeSatisfied_true G htail]

theorem card_ports [Fintype D] :
    Fintype.card (Bool × D) = 2 * Fintype.card D := by
  simp

theorem card_darts [Fintype V] [Fintype D] :
    Fintype.card (V × (Bool × D)) = 2 * Fintype.card (V × D) := by
  simp [Fintype.card_prod, Nat.mul_left_comm]

/-- The actual normalized rejection probability is halved. The identity also
holds for empty dart types, using the field's totalized division. -/
theorem rejection_density_eq_half [Fintype V] [Fintype D]
    (G : ConstraintGraph V (V × D) A) (htail : G.tail = Prod.fst)
    (labeling : V → A) :
    ((constraintGraph G).rejectionCount labeling : ℝ) /
        (Fintype.card (V × (Bool × D)) : ℝ) =
      ((G.rejectionCount labeling : ℝ) / (Fintype.card (V × D) : ℝ)) / 2 := by
  rw [rejectionCount_eq G htail, card_darts]
  simp only [Nat.cast_mul, Nat.cast_ofNat, div_eq_mul_inv, mul_inv_rev]
  ring

end MaxCutGames.Foundations.PCP.LazyConstraint

end OAI