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// SPDX-License-Identifier: Apache-2.0

// SPDX-FileCopyrightText: Copyright the Vortex contributors

use std::sync::Arc;

use arrow_array::RecordBatchReader;

use arrow_schema::SchemaRef;

use itertools::Itertools;

use pyo3::exceptions::PyTypeError;

use pyo3::exceptions::PyValueError;

use pyo3::prelude::*;

use pyo3::types::PyString;

use vortex::array::ArrayRef;

use vortex::array::ExecutionCtx;

use vortex::array::VortexSessionExecute;

use vortex::array::arrays::PrimitiveArray;

use vortex::array::iter::ArrayIteratorExt;

use vortex::dtype::FieldName;

use vortex::dtype::FieldNames;

use vortex::error::VortexResult;

use vortex::expr::Expression;

use vortex::expr::root;

use vortex::expr::select;

use vortex::file::OpenOptionsSessionExt;

use vortex::file::VortexFile;

use vortex::io::runtime::BlockingRuntime;

use vortex::layout::scan::split_by::SplitBy;

use crate::RUNTIME;

use crate::arrays::PyArrayRef;

use crate::arrow::IntoPyArrow;

use crate::arrow::ToPyArrow;

use crate::error::PyVortexResult;

use crate::expr::PyExpr;

use crate::install_module;

use crate::object_store::resolve::ResolvedStore;

use crate::object_store::resolve::resolve_store;

use crate::session::session;

pub(crate) fn init(py: Python, parent: &Bound<PyModule>) -> PyResult<()> {

let m = PyModule::new(py, "dataset")?;

parent.add_submodule(&m)?;

install_module("vortex._lib.dataset", &m)?;

m.add_class::<PyVortexDataset>()?;

m.add_function(wrap_pyfunction!(dataset_from_url, &m)?)?;

Ok(())

}

pub fn read_array_from_reader(

vortex_file: &VortexFile,

projection: Expression,

filter: Option<Expression>,

indices: Option<ArrayRef>,

row_range: Option<(u64, u64)>,

ctx: &mut ExecutionCtx,

) -> VortexResult<ArrayRef> {

let mut scan = vortex_file.scan()?.with_projection(projection);

if let Some(filter) = filter {

scan = scan.with_filter(filter);

}

if let Some(indices) = indices {

let primitive = indices.execute::<PrimitiveArray>(ctx)?;

let indices = primitive.into_buffer();

scan = scan.with_row_indices(indices);

}

if let Some((l, r)) = row_range {

scan = scan.with_row_range(l..r);

}

scan.into_array_iter(&*RUNTIME)?.read_all()

}

fn projection_from_python(columns: Option<Vec<Bound<PyAny>>>) -> PyResult<Expression> {

fn field_from_pyany(field: &Bound<PyAny>) -> PyResult<FieldName> {

if field.clone().is_instance_of::<PyString>() {

Ok(FieldName::from(field.cast::<PyString>()?.to_str()?))

} else {

Err(PyTypeError::new_err(format!(

"projection: expected list of strings or None, but found: {field}.",

)))

}

Ok(match columns {

None => root(),

Some(columns) => {

let fields: Vec<_> = columns

.iter()

.map(field_from_pyany)

.collect::<PyResult<_>>()?;

select(FieldNames::from(fields), root())

}

})

}

fn filter_from_python(row_filter: Option<&Bound<PyExpr>>) -> Option<Expression> {

row_filter.map(|x| x.borrow().inner().clone())

}

#[pyclass(name = "VortexDataset", module = "dataset")]

pub struct PyVortexDataset {

vxf: VortexFile,

schema: SchemaRef,

}

impl PyVortexDataset {

pub fn try_new(vxf: VortexFile) -> VortexResult<Self> {

let schema = Arc::new(vxf.dtype().to_arrow_schema()?);

Ok(Self { vxf, schema })

}

pub async fn from_url(

url: &str,

store: Option<Arc<dyn object_store::ObjectStore>>,

) -> VortexResult<Self> {

let session = session();

let vxf = match resolve_store(url, store)? {

ResolvedStore::ObjectStore(store, path) => {

session

.open_options()

.open_object_store(&store, path.as_ref())

.await?

}

ResolvedStore::Path(path) => session.open_options().open_path(path).await?,

};

PyVortexDataset::try_new(vxf)

}

pub(crate) fn to_array_inner<'py>(

&self,

py: Python<'py>,

columns: Option<Vec<Bound<'py, PyAny>>>,

row_filter: Option<&Bound<'py, PyExpr>>,

indices: Option<PyArrayRef>,

row_range: Option<(u64, u64)>,

) -> PyVortexResult<PyArrayRef> {

let vxf = self.vxf.clone();

let projection = projection_from_python(columns)?;

let filter = filter_from_python(row_filter);

let indices = indices.map(|i| i.into_inner());

let array = py.detach(move || {

let session = session();

let mut ctx = session.create_execution_ctx();

read_array_from_reader(&vxf, projection, filter, indices, row_range, &mut ctx)

})?;

Ok(PyArrayRef::from(array))

}

#[pymethods]

impl PyVortexDataset {

fn schema(self_: PyRef<Self>) -> PyResult<Py<PyAny>> {

Arc::clone(&self_.schema).to_pyarrow(self_.py())

}

#[pyo3(signature = (*, columns = None, row_filter = None, indices = None, row_range = None))]

pub fn to_array<'py>(

self_: PyRef<'py, Self>,

columns: Option<Vec<Bound<'py, PyAny>>>,

row_filter: Option<&Bound<'py, PyExpr>>,

indices: Option<PyArrayRef>,

row_range: Option<(u64, u64)>,

) -> PyVortexResult<PyArrayRef> {

self_.to_array_inner(self_.py(), columns, row_filter, indices, row_range)

}

#[pyo3(signature = (*, columns = None, row_filter = None, split_by = None, row_range = None))]

pub fn to_record_batch_reader(

self_: PyRef<Self>,

columns: Option<Vec<Bound<'_, PyAny>>>,

row_filter: Option<&Bound<'_, PyExpr>>,

split_by: Option<usize>,

row_range: Option<(u64, u64)>,

) -> PyVortexResult<Py<PyAny>> {

let vxf = self_.vxf.clone();

let projection = projection_from_python(columns)?;

let filter = filter_from_python(row_filter);

let reader = self_.py().detach(move || {

let mut scan = vxf

.scan()?

.with_projection(projection)

.with_some_filter(filter)

.with_split_by(split_by.map(SplitBy::RowCount).unwrap_or(SplitBy::Layout));

if let Some((l, r)) = row_range {

scan = scan.with_row_range(l..r);

}

let schema = Arc::new(scan.dtype()?.to_arrow_schema()?);

let reader: Box<dyn RecordBatchReader + Send> =

Box::new(scan.into_record_batch_reader(schema, &*RUNTIME)?);

VortexResult::Ok(reader)

})?;

Ok(reader.into_pyarrow(self_.py())?)

}

/// The number of rows matching the filter.

#[pyo3(signature = (*, row_filter = None, split_by = None, row_range = None))]

pub fn count_rows(

self_: PyRef<Self>,

row_filter: Option<&Bound<'_, PyExpr>>,

split_by: Option<usize>,

row_range: Option<(u64, u64)>,

) -> PyVortexResult<usize> {

if row_filter.is_none() {

let row_count = match row_range {

Some(range) => range.1 - range.0,

None => self_.vxf.row_count(),

};

return row_count

.try_into()

.map_err(|e| PyValueError::new_err(e).into());

}

let vxf = self_.vxf.clone();

let filter = filter_from_python(row_filter);

let n_rows: usize = self_.py().detach(move || {

let mut scan = vxf

.scan()?

.with_projection(select(FieldNames::empty(), root()))

.with_some_filter(filter)

.with_split_by(split_by.map(SplitBy::RowCount).unwrap_or(SplitBy::Layout));

if let Some((l, r)) = row_range {

scan = scan.with_row_range(l..r);

}

scan.into_array_iter(&*RUNTIME)?

.map_ok(|array| array.len())

.process_results(|iter| iter.sum())

})?;

Ok(n_rows)

}

/// The natural splits of this Dataset.

#[pyo3(signature = (*))]

pub fn splits(&self) -> PyVortexResult<Vec<(u64, u64)>> {

Ok(self

.vxf

.splits()?

.into_iter()

.map(|x| (x.start, x.end))

.collect())

}

#[pyfunction]

#[pyo3(signature = (url, *, store = None))]

pub fn dataset_from_url(

py: Python,

url: &str,

store: Option<Bound<PyAny>>,

) -> PyVortexResult<PyVortexDataset> {

let store_arc = if let Some(store_obj) = store {

let py_store: pyo3_object_store::PyObjectStore = store_obj.extract()?;

Some(py_store.into_inner())

} else {

None

};

Ok(py.detach(move || RUNTIME.block_on(PyVortexDataset::from_url(url, store_arc)))?)

}