using namespace datasketches;

// generic tuple_policy:

// * update sketch policy uses create_summary and update_summary

// * set operation policies all use __call__

nb::class_<tuple_policy, TuplePolicy>(m, "TuplePolicy",

nb::intrusive_ptr<tuple_policy>(

[](tuple_policy *tp, PyObject *po) noexcept { tp->set_self_py(po); }),

"An abstract base class for Tuple Policy objects. All custom policies must extend this class.")

.def(nb::init())

.def("create_summary", &tuple_policy::create_summary,

"Creates a new Summary object\n\n"

":return: a Summary object\n:rtype: :class:`object`"

)

.def("update_summary", &tuple_policy::update_summary, nb::arg("summary"), nb::arg("update"),

"Applies the relevant policy to update the provided summary with the data in update.\n\n"

":param summary: An existing Summary\n:type summary: :class:`object`\n"

":param update: An update to apply to the Summary\n:type update: :class:`object`\n"

":return: The updated Summary\n:rtype: :class:`object`"

)

.def("__call__", &tuple_policy::operator(), nb::arg("summary"), nb::arg("update"),

"Similar to update_summary but allows a different implementation for set operations (union and intersection)\n\n"

":param summary: An existing Summary\n:type summary: :class:`object`\n"

":param update: An update to apply to the Summary\n:type update: :class:`object`\n"

":return: The updated Summary\n:rtype: :class:`object`"

)

;

using py_tuple_sketch = tuple_sketch<nb::object>;

using py_update_tuple = update_tuple_sketch<nb::object, nb::object, tuple_policy_holder>;

using py_compact_tuple = compact_tuple_sketch<nb::object>;

using py_tuple_union = tuple_union<nb::object, tuple_policy_holder>;

using py_tuple_intersection = tuple_intersection<nb::object, tuple_policy_holder>;

using py_tuple_a_not_b = tuple_a_not_b<nb::object>;

using py_tuple_jaccard_similarity = jaccard_similarity_base<tuple_union<nb::object, dummy_jaccard_policy>, tuple_intersection<nb::object, dummy_jaccard_policy>, pair_extract_key<uint64_t, nb::object>>;

nb::class_<py_tuple_sketch>(m, "tuple_sketch", "An abstract base class for tuple sketches.")

.def("__str__", [](const py_tuple_sketch& sk) { return sk.to_string(); },

"Produces a string summary of the sketch")

.def("to_string", &py_tuple_sketch::to_string, nb::arg("print_items")=false,

"Produces a string summary of the sketch")

.def("is_empty", &py_tuple_sketch::is_empty,

"Returns True if the sketch is empty, otherwise False")

.def("get_estimate", &py_tuple_sketch::get_estimate,

"Estimate of the distinct count of the input stream")

.def("get_upper_bound", static_cast<double (py_tuple_sketch::*)(uint8_t) const>(&py_tuple_sketch::get_upper_bound), nb::arg("num_std_devs"),

"Returns an approximate upper bound on the estimate at standard deviations in {1, 2, 3}")

.def("get_lower_bound", static_cast<double (py_tuple_sketch::*)(uint8_t) const>(&py_tuple_sketch::get_lower_bound), nb::arg("num_std_devs"),

"Returns an approximate lower bound on the estimate at standard deviations in {1, 2, 3}")

.def("is_estimation_mode", &py_tuple_sketch::is_estimation_mode,

"Returns True if sketch is in estimation mode, otherwise False")

.def_prop_ro("theta", &py_tuple_sketch::get_theta,

"Theta (effective sampling rate) as a fraction from 0 to 1")

.def_prop_ro("theta64", &py_tuple_sketch::get_theta64,

"Theta as 64-bit value")

.def_prop_ro("num_retained", &py_tuple_sketch::get_num_retained,

"The number of items currently in the sketch")

.def("get_seed_hash", [](const py_tuple_sketch& sk) { return sk.get_seed_hash(); }, // why does regular call not work??

"Returns a hash of the seed used in the sketch")

.def("is_ordered", &py_tuple_sketch::is_ordered,

"Returns True if the sketch entries are sorted, otherwise False")

.def("__iter__",

[](const py_tuple_sketch& s) {

return nb::make_iterator(nb::type<py_tuple_sketch>(),

"tuple_iterator",

s.begin(),

s.end());

}, nb::keep_alive<0,1>()

)

.def_prop_ro_static("DEFAULT_SEED", [](nb::object /* self */) { return DEFAULT_SEED; });

;

nb::class_<py_compact_tuple, py_tuple_sketch>(m, "compact_tuple_sketch")

.def(nb::init<const py_tuple_sketch&, bool>(), nb::arg("other"), nb::arg("ordered")=true,

"Creates a compact_tuple_sketch from an existing tuple_sketch.\n\n"

":param other: a sourch tuple_sketch\n:type other: tuple_sketch\n"

":param ordered: whether the incoming sketch entries are sorted. Default True\n"

":type ordered: bool, optional"

)

.def(nb::init<const theta_sketch&, nb::object&>(), nb::arg("other"), nb::arg("summary"),

"Creates a compact_tuple_sketch from a theta_sketch using a fixed summary value.\n\n"

":param other: a source theta sketch\n:type other: theta_sketch\n"

":param summary: a summary to use for every sketch entry\n:type summary: object"

)

.def("__copy__", [](const py_compact_tuple& sk){ return py_compact_tuple(sk); })

.def(

"serialize",

[](const py_compact_tuple& sk, py_object_serde& serde) {

auto bytes = sk.serialize(0, serde);

return nb::bytes(reinterpret_cast<const char*>(bytes.data()), bytes.size());

}, nb::arg("serde"),

"Serializes the sketch into a bytes object"

)

.def("filter",

[](const py_compact_tuple& sk, const std::function<bool(const nb::object&)> func) {

return sk.filter(func);

}, nb::arg("predicate"),

"Produces a compact_tuple_sketch from the given sketch by applying a predicate to "

"the summary in each entry.\n\n"

":param predicate: A function returning true or value evaluated on each tuple summary\n"

":return: A compact_tuple_sketch with the selected entries\n:rtype: :class:`compact_tuple_sketch`")

.def_static(

"deserialize",

[](const nb::bytes& bytes, py_object_serde& serde, uint64_t seed) {

return py_compact_tuple::deserialize(bytes.c_str(), bytes.size(), seed, serde);

},

nb::arg("bytes"), nb::arg("serde"), nb::arg("seed")=DEFAULT_SEED,

"Reads a bytes object and returns the corresponding compact_tuple_sketch"

);

nb::class_<py_update_tuple, py_tuple_sketch>(m, "update_tuple_sketch")

.def("__init__",

[](py_update_tuple* sk, tuple_policy* policy, uint8_t lg_k, double p, uint64_t seed) {

tuple_policy_holder holder(policy);

new (sk) py_update_tuple(py_update_tuple::builder(holder).set_lg_k(lg_k).set_p(p).set_seed(seed).build());

},

nb::arg("policy"), nb::arg("lg_k")=theta_constants::DEFAULT_LG_K, nb::arg("p")=1.0, nb::arg("seed")=DEFAULT_SEED,

"Creates an update_tuple_sketch using the provided parameters\n\n"

":param policy: a policy to use when updating\n:type policy: TuplePolicy\n"

":param lg_k: base 2 logarithm of the maximum size of the sketch. Default 12.\n:type lg_k: int, optional\n"

":param p: an initial sampling rate to use. Default 1.0\n:type p: float, optional\n"

":param seed: the seed to use when hashing values\n:type seed: int, optional"

)

.def("__copy__", [](const py_update_tuple& sk){ return py_update_tuple(sk); })

.def("update", static_cast<void (py_update_tuple::*)(int64_t, nb::object&)>(&py_update_tuple::update),

nb::arg("datum"), nb::arg("value"),

"Updates the sketch with the given integral item and summary value")

.def("update", static_cast<void (py_update_tuple::*)(double, nb::object&)>(&py_update_tuple::update),

nb::arg("datum"), nb::arg("value"),

"Updates the sketch with the given floating point item and summary value")

.def("update", static_cast<void (py_update_tuple::*)(const std::string&, nb::object&)>(&py_update_tuple::update),

nb::arg("datum"), nb::arg("value"),

"Updates the sketch with the given string item and summary value")

.def("compact", &py_update_tuple::compact, nb::arg("ordered")=true,

"Returns a compacted form of the sketch, optionally sorting it")

.def("trim", &py_update_tuple::trim, "Removes retained entries in excess of the nominal size k (if any)")

.def("reset", &py_update_tuple::reset, "Resets the sketch to the initial empty state")

.def("filter",

[](const py_update_tuple& sk, const std::function<bool(const nb::object&)> func) {

return sk.filter(func);

}, nb::arg("predicate"),

"Produces a compact_tuple_sketch from the given sketch by applying a predicate to "

"the summary in each entry.\n\n"

":param predicate: A function returning true or value evaluated on each tuple summary\n"

":return: A compact_tuple_sketch with the selected entries\n:rtype: :class:`compact_tuple_sketch`")

;

nb::class_<py_tuple_union>(m, "tuple_union")

.def("__init__",

[](py_tuple_union* u, tuple_policy* policy, uint8_t lg_k, double p, uint64_t seed) {

tuple_policy_holder holder(policy);

new (u) py_tuple_union(py_tuple_union::builder(holder).set_lg_k(lg_k).set_p(p).set_seed(seed).build());

},

nb::arg("policy"), nb::arg("lg_k")=theta_constants::DEFAULT_LG_K, nb::arg("p")=1.0, nb::arg("seed")=DEFAULT_SEED,

"Creates a tuple_union using the provided parameters\n\n"

":param policy: a policy to use when unioning entries\n:type policy: TuplePolicy\n"

":param lg_k: base 2 logarithm of the maximum size of the union. Default 12.\n:type lg_k: int, optional\n"

":param p: an initial sampling rate to use. Default 1.0\n:type p: float, optional\n"

":param seed: the seed to use when hashing values. Must match any sketch seeds.\n:type seed: int, optional"

)

.def("update", &py_tuple_union::update<const py_tuple_sketch&>, nb::arg("sketch"),

"Updates the union with the given sketch")

.def("get_result", &py_tuple_union::get_result, nb::arg("ordered")=true,

"Returns the sketch corresponding to the union result")

.def("reset", &py_tuple_union::reset,

"Resets the sketch to the initial empty")

;

nb::class_<py_tuple_intersection>(m, "tuple_intersection")

.def("__init__",

[](py_tuple_intersection* sk, tuple_policy* policy, uint64_t seed) {

tuple_policy_holder holder(policy);

new (sk) py_tuple_intersection(seed, holder);

},

nb::arg("policy"), nb::arg("seed")=DEFAULT_SEED,

"Creates a tuple_intersection using the provided parameters\n\n"

":param policy: a policy to use when intersecting entries\n:type policy: TuplePolicy\n"

":param seed: the seed to use when hashing values. Must match any sketch seeds\n:type seed: int, optional"

)

.def("update", &py_tuple_intersection::update<const py_tuple_sketch&>, nb::arg("sketch"),

"Intersects the provided sketch with the current intersection state")

.def("get_result", &py_tuple_intersection::get_result, nb::arg("ordered")=true,

"Returns the sketch corresponding to the intersection result")

.def("has_result", &py_tuple_intersection::has_result,

"Returns True if the intersection has a valid result, otherwise False")

;

nb::class_<py_tuple_a_not_b>(m, "tuple_a_not_b")

.def(nb::init<uint64_t>(), nb::arg("seed")=DEFAULT_SEED,

"Creates a tuple_a_not_b object\n\n"

":param seed: the seed to use when hashing values. Must match any sketch seeds.\n:type seed: int, optional"

)

.def(

"compute",

&py_tuple_a_not_b::compute<const py_tuple_sketch&, const py_tuple_sketch&>,

nb::arg("a"), nb::arg("b"), nb::arg("ordered")=true,

"Returns a sketch with the result of applying the A-not-B operation on the given inputs"

)

;

nb::class_<py_tuple_jaccard_similarity>(m, "tuple_jaccard_similarity",

"An object to help compute Jaccard similarity between tuple sketches.")

.def_static(

"jaccard",

[](const py_tuple_sketch& sketch_a, const py_tuple_sketch& sketch_b, uint64_t seed) {

return py_tuple_jaccard_similarity::jaccard(sketch_a, sketch_b, seed);

},

nb::arg("sketch_a"), nb::arg("sketch_b"), nb::arg("seed")=DEFAULT_SEED,

"Returns a list with {lower_bound, estimate, upper_bound} of the Jaccard similarity between sketches"

)

.def_static(

"exactly_equal",

&py_tuple_jaccard_similarity::exactly_equal<const py_tuple_sketch&, const py_tuple_sketch&>,

nb::arg("sketch_a"), nb::arg("sketch_b"), nb::arg("seed")=DEFAULT_SEED,

"Returns True if sketch_a and sketch_b are equivalent, otherwise False"

)

.def_static(

"similarity_test",

&py_tuple_jaccard_similarity::similarity_test<const py_tuple_sketch&, const py_tuple_sketch&>,

nb::arg("actual"), nb::arg("expected"), nb::arg("threshold"), nb::arg("seed")=DEFAULT_SEED,

"Tests similarity of an actual sketch against an expected sketch. Computes the lower bound of the Jaccard "

"index J_{LB} of the actual and expected sketches. If J_{LB} >= threshold, then the sketches are considered "

"to be similar with a confidence of 97.7% and returns True, otherwise False.")

.def_static(

"dissimilarity_test",

&py_tuple_jaccard_similarity::dissimilarity_test<const py_tuple_sketch&, const py_tuple_sketch&>,

nb::arg("actual"), nb::arg("expected"), nb::arg("threshold"), nb::arg("seed")=DEFAULT_SEED,

"Tests dissimilarity of an actual sketch against an expected sketch. Computes the upper bound of the Jaccard "

"index J_{UB} of the actual and expected sketches. If J_{UB} <= threshold, then the sketches are considered "

"to be dissimilar with a confidence of 97.7% and returns True, otherwise False."

)

;