geolog-zeta-fork/tests/generators.rs

//! Proptest generators for geolog data structures
//!
//! Provides `Strategy` implementations for generating valid instances
//! of core data types used in property tests.

#![allow(dead_code)]

use geolog::core::{SortId, Structure};
use geolog::id::{NumericId, Slid, Uuid};
use geolog::naming::NamingIndex;
use geolog::universe::Universe;
use proptest::collection::vec;
use proptest::prelude::*;
use std::collections::HashSet;

// ============================================================================
// UUID Generation
// ============================================================================

/// Generate arbitrary UUIDs (using v7 format)
pub fn arb_uuid() -> impl Strategy<Value = Uuid> {
    // Generate random bytes for the UUID
    prop::array::uniform16(any::<u8>()).prop_map(|bytes| {
        // Create a valid v7-ish UUID from random bytes
        Uuid::from_bytes(bytes)
    })
}

/// Generate a vector of unique UUIDs
pub fn arb_unique_uuids(count: usize) -> impl Strategy<Value = Vec<Uuid>> {
    vec(arb_uuid(), count..=count).prop_filter_map("unique uuids", |uuids| {
        let set: HashSet<_> = uuids.iter().collect();
        if set.len() == uuids.len() {
            Some(uuids)
        } else {
            None
        }
    })
}

// ============================================================================
// Name Generation
// ============================================================================

/// Generate a valid identifier (alphanumeric, starting with letter)
pub fn arb_identifier() -> impl Strategy<Value = String> {
    "[a-zA-Z][a-zA-Z0-9_]{0,15}".prop_map(String::from)
}

/// Generate a qualified name path (non-empty vector of identifiers)
pub fn arb_qualified_name() -> impl Strategy<Value = Vec<String>> {
    vec(arb_identifier(), 1..=3)
}

// ============================================================================
// Structure Generation
// ============================================================================

/// Parameters for structure generation
#[derive(Debug, Clone)]
pub struct StructureParams {
    pub num_sorts: usize,
    pub max_elements_per_sort: usize,
}

impl Default for StructureParams {
    fn default() -> Self {
        Self {
            num_sorts: 3,
            max_elements_per_sort: 5,
        }
    }
}

/// Generate a valid Structure with elements distributed across sorts
pub fn arb_structure(params: StructureParams) -> impl Strategy<Value = (Structure, Universe)> {
    // Generate element counts for each sort
    vec(0..=params.max_elements_per_sort, params.num_sorts)
        .prop_flat_map(move |element_counts| {
            let num_sorts = params.num_sorts;
            Just((element_counts, num_sorts))
        })
        .prop_map(|(element_counts, num_sorts)| {
            let mut universe = Universe::new();
            let mut structure = Structure::new(num_sorts);

            for (sort_id, &count) in element_counts.iter().enumerate() {
                for _ in 0..count {
                    structure.add_element(&mut universe, sort_id as SortId);
                }
            }

            (structure, universe)
        })
}

/// Generate a structure with specific element count
pub fn arb_structure_with_elements(
    num_sorts: usize,
    total_elements: usize,
) -> impl Strategy<Value = (Structure, Universe)> {
    // Distribute elements randomly across sorts
    vec(0..num_sorts, total_elements).prop_map(move |sort_assignments| {
        let mut universe = Universe::new();
        let mut structure = Structure::new(num_sorts);

        for sort_id in sort_assignments {
            structure.add_element(&mut universe, sort_id as SortId);
        }

        (structure, universe)
    })
}

// ============================================================================
// NamingIndex Generation
// ============================================================================

/// Generate a NamingIndex with random entries
pub fn arb_naming_index(max_entries: usize) -> impl Strategy<Value = NamingIndex> {
    vec((arb_uuid(), arb_qualified_name()), 0..=max_entries).prop_filter_map(
        "unique uuids in naming",
        |entries| {
            // Ensure UUIDs are unique
            let uuids: HashSet<_> = entries.iter().map(|(u, _)| u).collect();
            if uuids.len() == entries.len() {
                let mut index = NamingIndex::new();
                for (uuid, name) in entries {
                    index.insert(uuid, name);
                }
                Some(index)
            } else {
                None
            }
        },
    )
}

/// Generate a NamingIndex that matches a Universe (same UUIDs)
pub fn arb_naming_for_universe(universe: &Universe) -> impl Strategy<Value = NamingIndex> {
    let uuids: Vec<Uuid> = universe.iter().map(|(_, uuid)| uuid).collect();
    let count = uuids.len();

    vec(arb_qualified_name(), count).prop_map(move |names| {
        let mut index = NamingIndex::new();
        for (uuid, name) in uuids.iter().zip(names.into_iter()) {
            index.insert(*uuid, name);
        }
        index
    })
}

// ============================================================================
// Element Operations (for testing add/remove sequences)
// ============================================================================

/// An operation on a structure
#[derive(Debug, Clone)]
pub enum StructureOp {
    AddElement { sort_id: SortId },
}

/// Generate a sequence of structure operations
pub fn arb_structure_ops(
    num_sorts: usize,
    max_ops: usize,
) -> impl Strategy<Value = Vec<StructureOp>> {
    vec(
        (0..num_sorts).prop_map(|sort_id| StructureOp::AddElement { sort_id }),
        0..=max_ops,
    )
}

// ============================================================================
// Test Helpers
// ============================================================================

/// Check that a Structure maintains its internal invariants
pub fn check_structure_invariants(structure: &Structure) -> Result<(), String> {
    // Invariant 1: luids and sorts have same length
    if structure.luids.len() != structure.sorts.len() {
        return Err(format!(
            "luids.len({}) != sorts.len({})",
            structure.luids.len(),
            structure.sorts.len()
        ));
    }

    // Invariant 2: luid_to_slid is inverse of luids
    for (slid_idx, &luid) in structure.luids.iter().enumerate() {
        let slid = Slid::from_usize(slid_idx);
        match structure.luid_to_slid.get(&luid) {
            Some(&mapped_slid) if mapped_slid == slid => {}
            Some(&mapped_slid) => {
                return Err(format!(
                    "luid_to_slid[{}] = {}, but luids[{}] = {}",
                    luid, mapped_slid, slid, luid
                ));
            }
            None => {
                return Err(format!(
                    "luid {} at slid {} not in luid_to_slid",
                    luid, slid
                ));
            }
        }
    }

    // Invariant 3: Each element appears in exactly one carrier, matching its sort
    for (slid, &sort_id) in structure.sorts.iter().enumerate() {
        if sort_id >= structure.carriers.len() {
            return Err(format!(
                "sort_id {} at slid {} >= carriers.len({})",
                sort_id,
                slid,
                structure.carriers.len()
            ));
        }

        if !structure.carriers[sort_id].contains(slid as u64) {
            return Err(format!(
                "slid {} with sort {} not in carriers[{}]",
                slid, sort_id, sort_id
            ));
        }

        // Check it's not in any other carrier
        for (other_sort, carrier) in structure.carriers.iter().enumerate() {
            if other_sort != sort_id && carrier.contains(slid as u64) {
                return Err(format!(
                    "slid {} appears in carrier {} but has sort {}",
                    slid, other_sort, sort_id
                ));
            }
        }
    }

    // Invariant 4: Total carrier size equals number of elements
    let total_carrier_size: usize = structure.carriers.iter().map(|c| c.len() as usize).sum();
    if total_carrier_size != structure.luids.len() {
        return Err(format!(
            "total carrier size {} != luids.len({})",
            total_carrier_size,
            structure.luids.len()
        ));
    }

    Ok(())
}

/// Check that two structures are equivalent (same elements and functions)
pub fn structures_equivalent(s1: &Structure, s2: &Structure, u1: &Universe, u2: &Universe) -> bool {
    // Same number of sorts
    if s1.num_sorts() != s2.num_sorts() {
        return false;
    }

    // Same number of elements
    if s1.len() != s2.len() {
        return false;
    }

    // Same UUIDs (via Luid lookup)
    let uuids1: HashSet<_> = s1.luids.iter().filter_map(|&luid| u1.get(luid)).collect();
    let uuids2: HashSet<_> = s2.luids.iter().filter_map(|&luid| u2.get(luid)).collect();

    uuids1 == uuids2
}

// ============================================================================
// Tensor Generation
// ============================================================================

use geolog::tensor::SparseTensor;
use std::collections::BTreeSet;

/// Parameters for sparse tensor generation
#[derive(Debug, Clone)]
pub struct TensorParams {
    pub max_dims: usize,
    pub max_dim_size: usize,
    pub max_tuples: usize,
}

impl Default for TensorParams {
    fn default() -> Self {
        Self {
            max_dims: 4,
            max_dim_size: 10,
            max_tuples: 20,
        }
    }
}

/// Generate a random sparse tensor
pub fn arb_sparse_tensor(params: TensorParams) -> impl Strategy<Value = SparseTensor> {
    // First generate dimensions
    vec(1..=params.max_dim_size, 0..=params.max_dims).prop_flat_map(move |dims| {
        let dims_clone = dims.clone();
        let max_tuples = params.max_tuples;

        // Generate tuples within the dimension bounds
        if dims.is_empty() {
            // Scalar tensor - either true or false
            any::<bool>()
                .prop_map(|value| {
                    let mut extent = BTreeSet::new();
                    if value {
                        extent.insert(vec![]);
                    }
                    SparseTensor { dims: vec![], extent }
                })
                .boxed()
        } else {
            // Generate random tuples
            let tuple_gen = dims
                .iter()
                .map(|&d| 0..d)
                .collect::<Vec<_>>();

            vec(tuple_gen.prop_map(|indices| indices), 0..=max_tuples)
                .prop_map(move |tuples| {
                    let extent: BTreeSet<Vec<usize>> = tuples.into_iter().collect();
                    SparseTensor {
                        dims: dims_clone.clone(),
                        extent,
                    }
                })
                .boxed()
        }
    })
}

/// Generate a sparse tensor with specific dimensions
pub fn arb_sparse_tensor_with_dims(dims: Vec<usize>, max_tuples: usize) -> impl Strategy<Value = SparseTensor> {
    if dims.is_empty() {
        any::<bool>()
            .prop_map(|value| {
                let mut extent = BTreeSet::new();
                if value {
                    extent.insert(vec![]);
                }
                SparseTensor { dims: vec![], extent }
            })
            .boxed()
    } else {
        let tuple_gen: Vec<_> = dims.iter().map(|&d| 0..d).collect();
        let dims_clone = dims.clone();

        vec(tuple_gen.prop_map(|indices| indices), 0..=max_tuples)
            .prop_map(move |tuples| {
                let extent: BTreeSet<Vec<usize>> = tuples.into_iter().collect();
                SparseTensor {
                    dims: dims_clone.clone(),
                    extent,
                }
            })
            .boxed()
    }
}

/// Generate a pair of tensors with matching dimensions (for disjunction tests)
pub fn arb_tensor_pair_same_dims(params: TensorParams) -> impl Strategy<Value = (SparseTensor, SparseTensor)> {
    vec(1..=params.max_dim_size, 0..=params.max_dims).prop_flat_map(move |dims| {
        let max_tuples = params.max_tuples;
        let t1 = arb_sparse_tensor_with_dims(dims.clone(), max_tuples);
        let t2 = arb_sparse_tensor_with_dims(dims, max_tuples);
        (t1, t2)
    })
}

/// Generate variable names
pub fn arb_var_names(count: usize) -> impl Strategy<Value = Vec<String>> {
    Just((0..count).map(|i| format!("v{}", i)).collect())
}