import time

import numpy as np
import pandas as pd
import pytest

import ray
from ray._private.internal_api import memory_summary
from ray.data.block import BlockMetadata
from ray.data.datasource import Datasource, ReadTask
from ray.data.tests.conftest import *  # noqa
from ray.tests.conftest import *  # noqa


def test_input_backpressure_e2e(restore_data_context, shutdown_only):

    # Tests that backpressure applies even when reading directly from the input
    # datasource. This relies on datasource metadata size estimation.
    @ray.remote
    class Counter:
        def __init__(self):
            self.count = 0

        def increment(self):
            self.count += 1

        def get(self):
            return self.count

        def reset(self):
            self.count = 0

    class CountingRangeDatasource(Datasource):
        def __init__(self):
            self.counter = Counter.remote()

        def prepare_read(self, parallelism, n):
            def range_(i):
                ray.get(self.counter.increment.remote())
                return [
                    pd.DataFrame({"data": np.ones((n // parallelism * 1024 * 1024,))})
                ]

            sz = (n // parallelism) * 1024 * 1024 * 8
            print("Block size", sz)

            return [
                ReadTask(
                    lambda i=i: range_(i),
                    BlockMetadata(
                        num_rows=n // parallelism,
                        size_bytes=sz,
                        schema=None,
                        input_files=None,
                        exec_stats=None,
                    ),
                )
                for i in range(parallelism)
            ]

    source = CountingRangeDatasource()
    ctx = ray.data.DataContext.get_current()
    ctx.execution_options.resource_limits.object_store_memory = 10e6

    # 10GiB dataset.
    ds = ray.data.read_datasource(source, n=10000, parallelism=1000)
    it = ds.iter_batches(batch_size=None, prefetch_batches=0)
    next(it)
    time.sleep(3)
    launched = ray.get(source.counter.get.remote())

    # If backpressure is broken we'll launch 15+.
    assert launched < 5, launched


def test_streaming_backpressure_e2e(restore_data_context):

    # This test case is particularly challenging since there is a large input->output
    # increase in data size: https://github.com/ray-project/ray/issues/34041
    class TestSlow:
        def __call__(self, df: np.ndarray):
            time.sleep(2)
            return {"id": np.random.randn(1, 20, 1024, 1024)}

    class TestFast:
        def __call__(self, df: np.ndarray):
            time.sleep(0.5)
            return {"id": np.random.randn(1, 20, 1024, 1024)}

    ctx = ray.init(object_store_memory=4e9)
    ds = ray.data.range_tensor(20, shape=(3, 1024, 1024), parallelism=20)

    pipe = ds.map_batches(
        TestFast,
        batch_size=1,
        num_cpus=0.5,
        compute=ray.data.ActorPoolStrategy(size=2),
    ).map_batches(
        TestSlow,
        batch_size=1,
        compute=ray.data.ActorPoolStrategy(size=1),
    )

    for batch in pipe.iter_batches(batch_size=1, prefetch_batches=2):
        ...

    # If backpressure is not working right, we will spill.
    meminfo = memory_summary(ctx.address_info["address"], stats_only=True)
    assert "Spilled" not in meminfo, meminfo


if __name__ == "__main__":
    import sys

    sys.exit(pytest.main(["-v", __file__]))
