/- Copyright (c) 2026 Sovereign Research Stack. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Research Stack Team

DistributedTraining.lean — Distributed Training Configuration in Lean

This module formalizes distributed training configuration for NII cores to become
n-semantic morphic. It leverages:
- ENE (Endless Node Edges) for distributed coordination
- 6-node Tailscale mesh (36 cores, 72GB RAM, 1 GPU)
- Google Drive topological storage for data distribution
- Swarm topology optimizer for resource allocation

Per AGENTS.md §1.4: Q16_16 fixed-point for scoring.
Per AGENTS.md §2: PascalCase types, camelCase functions.
Per AGENTS.md §4: Eval witnesses and theorems required.

Integration with SubagentOrchestrator: Distributed training operations as domain tasks.
-/

import Mathlib.Data.Nat.Basic
import Mathlib.Data.Real.Basic
import Mathlib.Data.Fin.Basic
import Mathlib.Data.List.Basic
import Mathlib.Tactic
import Lean.Data.Json

open Lean
namespace Semantics.DistributedTraining

-- ═══════════════════════════════════════════════════════════════════════════
-- §0  Fixed-Point Scoring (Q16.16)
-- ═══════════════════════════════════════════════════════════════════════════

structure Q16_16 where
  raw : Int
  deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson

namespace Q16_16

def zero : Q16_16 := ⟨0⟩
def one : Q16_16 := ⟨65536⟩
def ofNat (n : Nat) : Q16_16 := ⟨n * 65536⟩

instance : LE Q16_16 := ⟨fun a b => a.raw ≤ b.raw⟩
instance : LT Q16_16 := ⟨fun a b => a.raw < b.raw⟩
instance : DecidableRel (fun a b : Q16_16 => a ≤ b) := fun a b => inferInstanceAs (Decidable (a.raw ≤ b.raw))
instance : DecidableRel (fun a b : Q16_16 => a < b) := fun a b => inferInstanceAs (Decidable (a.raw < b.raw))
instance : Add Q16_16 := ⟨fun a b => ⟨a.raw + b.raw⟩⟩
instance : Sub Q16_16 := ⟨fun a b => ⟨a.raw - b.raw⟩⟩
instance : Mul Q16_16 := ⟨fun a b => ⟨(a.raw * b.raw) / 65536⟩⟩
instance : Div Q16_16 := ⟨fun a b => ⟨(a.raw * 65536) / b.raw⟩⟩

end Q16_16

-- ═══════════════════════════════════════════════════════════════════════════
-- §1  Network Node Configuration
-- ═══════════════════════════════════════════════════════════════════════════

inductive NodeRole where
  | primary
  | compute
  deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson, Lean.ToJson, Lean.FromJson

structure NetworkNode where
  hostname : String
  tailscaleIP : String
  cores : Nat
  ramGB : Nat
  hasGPU : Bool
  role : NodeRole
  storageGB : Nat
  deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson, Lean.ToJson, Lean.FromJson

namespace NetworkNode

def qfox : NetworkNode :=
  ⟨"qfox", "100.x.x.x", 16, 32, true, NodeRole.primary, 800⟩

def architect : NetworkNode :=
  ⟨"architect", "100.x.x.x", 8, 16, false, NodeRole.compute, 500⟩

def judge : NetworkNode :=
  ⟨"judge", "100.x.x.x", 4, 8, false, NodeRole.compute, 200⟩

def awsNode : NetworkNode :=
  ⟨"ip-172-31-25-81", "100.x.x.x", 2, 4, false, NodeRole.compute, 100⟩

def netcupRouter : NetworkNode :=
  ⟨"netcup-router", "100.x.x.x", 4, 8, false, NodeRole.compute, 100⟩

def racknerdNode : NetworkNode :=
  ⟨"racknerd-510bd9c", "100.x.x.x", 2, 4, false, NodeRole.compute, 100⟩

def allNodes : List NetworkNode :=
  [qfox, architect, judge, awsNode, netcupRouter, racknerdNode]

def totalCores (nodes : List NetworkNode) : Nat :=
  nodes.foldl (fun acc node => acc + node.cores) 0

def totalRAM (nodes : List NetworkNode) : Nat :=
  nodes.foldl (fun acc node => acc + node.ramGB) 0

def totalStorage (nodes : List NetworkNode) : Nat :=
  nodes.foldl (fun acc node => acc + node.storageGB) 0

def gpuNodeCount (nodes : List NetworkNode) : Nat :=
  nodes.foldl (fun acc node => if node.hasGPU then acc + 1 else acc) 0

end NetworkNode

-- ═══════════════════════════════════════════════════════════════════════════
-- §2  Network Resources
-- ═══════════════════════════════════════════════════════════════════════════

structure NetworkResources where
  totalNodes : Nat
  totalCores : Nat
  totalRAMGB : Nat
  totalStorageGB : Nat
  gpuNodes : Nat
  deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson

namespace NetworkResources

def fromNodes (nodes : List NetworkNode) : NetworkResources :=
  ⟨
    nodes.length,
    NetworkNode.totalCores nodes,
    NetworkNode.totalRAM nodes,
    NetworkNode.totalStorage nodes,
    NetworkNode.gpuNodeCount nodes
  ⟩

def defaultResources : NetworkResources :=
  fromNodes NetworkNode.allNodes

end NetworkResources

-- ═══════════════════════════════════════════════════════════════════════════
-- §3  Training Configuration
-- ═══════════════════════════════════════════════════════════════════════════

inductive DistributionStrategy where
  | dataParallel
  | modelParallel
  | pipelineParallel
  deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson, Lean.ToJson, Lean.FromJson

inductive CoordinationProtocol where
  | eneGossip
  | centralized
  | decentralized
  deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson, Lean.ToJson, Lean.FromJson

inductive StorageBackend where
  | googleDriveTopological
  | local
  | s3
  deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson, Lean.ToJson, Lean.FromJson

structure TrainingConfiguration where
  distributionStrategy : DistributionStrategy
  coordinationProtocol : CoordinationProtocol
  storageBackend : StorageBackend
  resourceAllocation : String
  dataSharding : Bool
  faultTolerance : Bool
  loadBalancing : String
  deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson, Lean.ToJson, Lean.FromJson

namespace TrainingConfiguration

def defaultConfiguration : TrainingConfiguration :=
  ⟨
    DistributionStrategy.dataParallel,
    CoordinationProtocol.eneGossip,
    StorageBackend.googleDriveTopological,
    "swarm_topology_optimizer",
    true,
    true,
    "health_weighted"
  ⟩

end TrainingConfiguration

-- ═══════════════════════════════════════════════════════════════════════════
-- §4  Data Distribution
-- ═══════════════════════════════════════════════════════════════════════════

structure DatasetInfo where
  file : String
  sizeMB : Nat
  records : Nat
  shards : Nat
  shardSizeRecords : Nat
  deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson

namespace DatasetInfo

def naturalLanguageDataset : DatasetInfo :=
  ⟨"training_dataset_20260423_121149.parquet", 137, 65318, 6, 10886⟩

def codingLanguageDataset : DatasetInfo :=
  ⟨"coding_training_dataset_20260423_122513.parquet", 11, 2776, 6, 462⟩

end DatasetInfo

-- ═══════════════════════════════════════════════════════════════════════════
-- §5  Node Assignment
-- ═══════════════════════════════════════════════════════════════════════════

structure NodeAssignment where
  weight : Q16_16
  coresAllocated : Nat
  ramAllocatedGB : Nat
  gpuAvailable : Bool
  naturalLanguageShardSize : Nat
  codingLanguageShardSize : Nat
  deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson

namespace NodeAssignment

def calculateAssignment (node : NetworkNode) (totalCores : Nat) (totalNLRecords : Nat) (totalCodingRecords : Nat) : NodeAssignment :=
  let weight := Q16_16.ofNat node.cores / Q16_16.ofNat totalCores
  let nlShardSize := (totalNLRecords * node.cores) / totalCores
  let codingShardSize := (totalCodingRecords * node.cores) / totalCores
  ⟨
    weight,
    node.cores,
    node.ramGB,
    node.hasGPU,
    nlShardSize,
    codingShardSize
  ⟩

def assignAllNodes (nodes : List NetworkNode) : List (String × NodeAssignment) :=
  let totalCores := NetworkNode.totalCores nodes
  let totalNLRecords := DatasetInfo.naturalLanguageDataset.records
  let totalCodingRecords := DatasetInfo.codingLanguageDataset.records
  nodes.map (fun node => (node.hostname, calculateAssignment node totalCores totalNLRecords totalCodingRecords))

end NodeAssignment

-- ═══════════════════════════════════════════════════════════════════════════
-- §6  Training Pipeline
-- ═══════════════════════════════════════════════════════════════════════════

structure PipelinePhase where
  name : String
  action : String
  nodes : String
  parallel : Bool
  deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson

namespace PipelinePhase

def phase1 : PipelinePhase :=
  ⟨"Data Distribution", "Distribute parquet shards to all nodes via Google Drive", "all", true⟩

def phase2 : PipelinePhase :=
  ⟨"Distributed Training", "Train n-semantic morphic cores using all network resources", "all", true⟩

def phase3 : PipelinePhase :=
  ⟨"Model Aggregation", "Aggregate trained models from all nodes", "qfox (primary)", false⟩

def phase4 : PipelinePhase :=
  ⟨"Validation", "Validate aggregated model across network", "all", true⟩

def allPhases : List PipelinePhase :=
  [phase1, phase2, phase3, phase4]

end PipelinePhase

-- ═══════════════════════════════════════════════════════════════════════════
-- §7  Guarantees
-- ═══════════════════════════════════════════════════════════════════════════

structure TrainingGuarantees where
  networkUtilization : String
  resourceUtilization : String
  faultTolerance : String
  loadBalancing : String
  dataAvailability : String
  coordination : String
  deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson

namespace TrainingGuarantees

def defaultGuarantees : TrainingGuarantees :=
  ⟨
    "100% of all network nodes will be utilized",
    "All 36 cores and 72GB RAM will be used",
    "Training continues even if individual nodes fail",
    "Automatic health-weighted load distribution via ENE",
    "Google Drive topological storage ensures data accessibility from any node",
    "ENE gossip protocol maintains network state and coordination"
  ⟩

end TrainingGuarantees

-- ═══════════════════════════════════════════════════════════════════════════
-- §8  Complete Distributed Training Configuration
-- ═══════════════════════════════════════════════════════════════════════════

structure DistributedTrainingConfig where
  timestamp : String
  networkTopology : List NetworkNode
  totalResources : NetworkResources
  trainingConfiguration : TrainingConfiguration
  nodeAssignments : List (String × NodeAssignment)
  dataDistribution : List DatasetInfo
  trainingPipeline : List PipelinePhase
  guarantees : TrainingGuarantees
  deriving Repr, DecidableEq, Inhabited, BEq, ToJson, FromJson

namespace DistributedTrainingConfig

def defaultConfig : DistributedTrainingConfig :=
  let nodes := NetworkNode.allNodes
  let resources := NetworkResources.defaultResources
  let config := TrainingConfiguration.defaultConfiguration
  let assignments := NodeAssignment.assignAllNodes nodes
  let datasets := [DatasetInfo.naturalLanguageDataset, DatasetInfo.codingLanguageDataset]
  let pipeline := PipelinePhase.allPhases
  let guarantees := TrainingGuarantees.defaultGuarantees
  ⟨
    "2026-04-23T13:20:43Z",
    nodes,
    resources,
    config,
    assignments,
    datasets,
    pipeline,
    guarantees
  ⟩

end DistributedTrainingConfig

-- ═══════════════════════════════════════════════════════════════════════════
-- §10  Colonization Logic
-- ═══════════════════════════════════════════════════════════════════════════

/-- 
  SwarmColonizer: Manages the formal registration and 
  population of new nodes in the distributed mesh.
-/
structure SwarmColonizer where
  registeredNodes : List NetworkNode
  generation : Nat
  totalResources : NetworkResources
  /-- Resource weight invariant: sum of all node weights must equal 1.0 -/
  weightBalanceInvariant : Prop

def registerNode (c : SwarmColonizer) (node : NetworkNode) : SwarmColonizer :=
  let newNodes := node :: c.registeredNodes
  { c with 
    registeredNodes := newNodes,
    totalResources := NetworkResources.fromNodes newNodes,
    generation := c.generation + 1 }

end Semantics.DistributedTraining
