import secrets

import numpy as np
import psutil
import pytest
from scipy.stats import linregress

from khoj.processor.embeddings import EmbeddingsModel
from khoj.utils import helpers


def test_get_from_null_dict():
    # null handling
    assert helpers.get_from_dict(dict()) == dict()
    assert helpers.get_from_dict(dict(), None) == None

    # key present in nested dictionary
    # 1-level dictionary
    assert helpers.get_from_dict({"a": 1, "b": 2}, "a") == 1
    assert helpers.get_from_dict({"a": 1, "b": 2}, "c") == None

    # 2-level dictionary
    assert helpers.get_from_dict({"a": {"a_a": 1}, "b": 2}, "a") == {"a_a": 1}
    assert helpers.get_from_dict({"a": {"a_a": 1}, "b": 2}, "a", "a_a") == 1

    # key not present in nested dictionary
    # 2-level_dictionary
    assert helpers.get_from_dict({"a": {"a_a": 1}, "b": 2}, "b", "b_a") == None


def test_merge_dicts():
    # basic merge of dicts with non-overlapping keys
    assert helpers.merge_dicts(priority_dict={"a": 1}, default_dict={"b": 2}) == {"a": 1, "b": 2}

    # use default dict items when not present in priority dict
    assert helpers.merge_dicts(priority_dict={}, default_dict={"b": 2}) == {"b": 2}

    # do not override existing key in priority_dict with default dict
    assert helpers.merge_dicts(priority_dict={"a": 1}, default_dict={"a": 2}) == {"a": 1}


def test_lru_cache():
    # Test initializing cache
    cache = helpers.LRU({"a": 1, "b": 2}, capacity=2)
    assert cache == {"a": 1, "b": 2}

    # Test capacity overflow
    cache["c"] = 3
    assert cache == {"b": 2, "c": 3}

    # Test delete least recently used item from LRU cache on capacity overflow
    cache["b"]  # accessing 'b' makes it the most recently used item
    cache["d"] = 4  # so 'c' is deleted from the cache instead of 'b'
    assert cache == {"b": 2, "d": 4}


@pytest.mark.skip(reason="Memory leak exists on GPU, MPS devices")
def test_encode_docs_memory_leak():
    # Arrange
    iterations = 50
    batch_size = 20
    embeddings_model = EmbeddingsModel()
    memory_usage_trend = []
    device = f"{helpers.get_device()}".upper()

    # Act
    # Encode random strings repeatedly and record memory usage trend
    for iteration in range(iterations):
        random_docs = [" ".join(secrets.token_hex(5) for _ in range(10)) for _ in range(batch_size)]
        a = [embeddings_model.embed_documents(random_docs)]
        memory_usage_trend += [psutil.Process().memory_info().rss / (1024 * 1024)]
        print(f"{iteration:02d}, {memory_usage_trend[-1]:.2f}", flush=True)

    # Calculate slope of line fitting memory usage history
    memory_usage_trend = np.array(memory_usage_trend)
    slope, _, _, _, _ = linregress(np.arange(len(memory_usage_trend)), memory_usage_trend)
    print(f"Memory usage increased at ~{slope:.2f} MB per iteration on {device}")

    # Assert
    # If slope is positive memory utilization is increasing
    # Positive threshold of 2, from observing memory usage trend on MPS vs CPU device
    assert slope < 2, f"Memory leak suspected on {device}. Memory usage increased at ~{slope:.2f} MB per iteration"