import random
import string

# imports
import pytest  # used for our unit tests
from workload import AlexNet

# unit tests

# BASIC TEST CASES

def test_extract_features_basic_positive_integers():
    # Test with a simple list of lists of positive integers
    model = AlexNet()
    x = [[1, 2, 3], [4, 5], [6]]
    expected = [6, 9, 6]  # sums of each sublist
    codeflash_output = model._extract_features(x) # 1.34μs -> 390ns (244% faster)

def test_extract_features_basic_negative_and_zero():
    # Test with negative numbers and zeros
    model = AlexNet()
    x = [[0, -1, -2], [3, 0, 0], [-5, 5]]
    expected = [-3, 3, 0]
    codeflash_output = model._extract_features(x) # 1.17μs -> 340ns (245% faster)

def test_extract_features_basic_floats():
    # Test with floating point numbers
    model = AlexNet()
    x = [[1.5, 2.5], [0.1, 0.2, 0.3]]
    expected = [4.0, 0.6]
    codeflash_output = model._extract_features(x) # 1.22μs -> 330ns (271% faster)

def test_extract_features_basic_empty_lists():
    # Test with empty sublists
    model = AlexNet()
    x = [[], [1, 2], []]
    expected = [0, 3, 0]
    codeflash_output = model._extract_features(x) # 1.24μs -> 330ns (276% faster)

def test_extract_features_basic_single_element_lists():
    # Test with single-element sublists
    model = AlexNet()
    x = [[42], [-7], [0]]
    expected = [42, -7, 0]
    codeflash_output = model._extract_features(x) # 1.22μs -> 331ns (269% faster)

# EDGE TEST CASES

def test_extract_features_edge_empty_input():
    # Test with completely empty input
    model = AlexNet()
    x = []
    expected = []
    codeflash_output = model._extract_features(x) # 961ns -> 320ns (200% faster)

def test_extract_features_edge_non_list_elements():
    # Test with non-list elements in input
    model = AlexNet()
    x = [[1, 2], "not a list", 123, None, [3, 4]]
    expected = [3, 0, 0, 0, 7]
    codeflash_output = model._extract_features(x) # 1.32μs -> 321ns (312% faster)

def test_extract_features_edge_nested_lists():
    # Test with nested lists (lists inside lists inside lists)
    model = AlexNet()
    x = [[1, 2], [[3, 4]], [5, [6]], [7]]
    # Only [1,2] and [7] are valid (flat lists of numbers), rest get 0
    expected = [3, 0, 0, 7]
    codeflash_output = model._extract_features(x) # 1.22μs -> 310ns (294% faster)

def test_extract_features_edge_non_numeric_elements():
    # Test with non-numeric elements inside sublists
    model = AlexNet()
    x = [[1, 2, "a"], [3, None], [4.0, 5.0], ["x", "y"]]
    # Only [4.0, 5.0] is valid (all numbers), rest get 0
    expected = [0, 0, 9.0, 0]
    codeflash_output = model._extract_features(x) # 1.25μs -> 310ns (304% faster)

def test_extract_features_edge_large_and_small_numbers():
    # Test with very large and very small numbers
    model = AlexNet()
    x = [[1e100, -1e100], [1e-100, -1e-100], [1, 2, 3]]
    expected = [0.0, 0.0, 6]
    codeflash_output = model._extract_features(x) # 1.21μs -> 301ns (303% faster)

def test_extract_features_edge_mixed_types():
    # Test with a mix of valid and invalid types
    model = AlexNet()
    x = [[1, 2], [], None, 5, [3.5, 4.5], ["a", 1], [0]]
    expected = [3, 0, 0, 0, 8.0, 0, 0]
    codeflash_output = model._extract_features(x) # 1.30μs -> 301ns (333% faster)

def test_extract_features_edge_all_invalid():
    # Test with all elements invalid
    model = AlexNet()
    x = [None, "abc", 123, {}, set()]
    expected = [0, 0, 0, 0, 0]
    codeflash_output = model._extract_features(x) # 1.24μs -> 310ns (301% faster)

# LARGE SCALE TEST CASES

def test_extract_features_large_scale_all_valid():
    # Test with a large input where all sublists are valid
    model = AlexNet()
    x = [[i, i+1, i+2] for i in range(0, 1000, 3)]
    expected = [sum([i, i+1, i+2]) for i in range(0, 1000, 3)]
    codeflash_output = model._extract_features(x) # 5.09μs -> 521ns (877% faster)

def test_extract_features_large_scale_all_empty():
    # Test with a large input of empty sublists
    model = AlexNet()
    x = [[] for _ in range(1000)]
    expected = [0] * 1000
    codeflash_output = model._extract_features(x) # 16.2μs -> 351ns (4527% faster)

def test_extract_features_large_scale_mixed():
    # Test with a large input mixing valid, invalid, and empty sublists
    model = AlexNet()
    x = []
    expected = []
    for i in range(1000):
        if i % 3 == 0:
            x.append([i, i+1])  # valid
            expected.append(i + i + 1)
        elif i % 3 == 1:
            x.append([])        # empty
            expected.append(0)
        else:
            x.append("bad")     # invalid
            expected.append(0)
    codeflash_output = model._extract_features(x)

def test_extract_features_large_scale_randomized():
    # Test with a large input of randomized valid and invalid sublists
    model = AlexNet()
    x = []
    expected = []
    for _ in range(1000):
        r = random.random()
        if r < 0.4:
            vals = [random.randint(-100, 100) for _ in range(random.randint(1, 5))]
            x.append(vals)
            expected.append(sum(vals))
        elif r < 0.7:
            x.append([])
            expected.append(0)
        elif r < 0.85:
            x.append("".join(random.choices(string.ascii_letters, k=5)))
            expected.append(0)
        else:
            x.append([random.randint(-100, 100), "bad", 42])
            expected.append(0)
    codeflash_output = model._extract_features(x)

def test_extract_features_large_scale_performance():
    # Test that the function completes for large input (performance test)
    model = AlexNet()
    x = [[i] * 10 for i in range(1000)]  # 1000 sublists, each with 10 elements
    expected = [i * 10 for i in range(1000)]
    codeflash_output = model._extract_features(x) # 16.0μs -> 381ns (4102% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import random
import string

# imports
import pytest  # used for our unit tests
from workload import AlexNet

# unit tests

# 1. Basic Test Cases

def test_single_flat_list():
    # Single list of numbers, should return the same list wrapped in a list
    net = AlexNet()
    x = [[1, 2, 3, 4]]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.25μs -> 311ns (303% faster)

def test_multiple_flat_lists():
    # Multiple lists of numbers
    net = AlexNet()
    x = [[1, 2], [3, 4], [5, 6]]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.21μs -> 300ns (304% faster)

def test_empty_outer_list():
    # Empty input list should return empty list
    net = AlexNet()
    x = []
    codeflash_output = net._extract_features(x); features = codeflash_output # 902ns -> 300ns (201% faster)

def test_empty_inner_list():
    # Inner empty lists should be preserved
    net = AlexNet()
    x = [[], [1, 2, 3], []]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.27μs -> 281ns (353% faster)

def test_float_and_int_mixture():
    # Accepts both ints and floats
    net = AlexNet()
    x = [[1, 2.5, 3], [4.0, 5]]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.16μs -> 280ns (315% faster)

# 2. Edge Test Cases




def test_negative_and_zero_values():
    # Should handle negative and zero values correctly
    net = AlexNet()
    x = [[0, -1, -2.5, 3]]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.34μs -> 421ns (219% faster)

def test_large_numbers():
    # Should handle very large numbers
    net = AlexNet()
    x = [[1e10, -1e12, 999999999]]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.27μs -> 370ns (244% faster)

def test_single_element_lists():
    # Should handle lists with a single element
    net = AlexNet()
    x = [[42], [0], [-3.14]]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.15μs -> 340ns (239% faster)

def test_all_empty_inner_lists():
    # All inner lists are empty
    net = AlexNet()
    x = [[], [], []]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.24μs -> 331ns (275% faster)


def test_large_number_of_lists():
    # Test with a large number of lists (e.g., 1000)
    net = AlexNet()
    x = [[i] for i in range(1000)]
    codeflash_output = net._extract_features(x); features = codeflash_output # 15.3μs -> 471ns (3156% faster)

def test_large_inner_lists():
    # Test with large inner lists (each up to 1000 elements)
    net = AlexNet()
    x = [list(range(1000)) for _ in range(5)]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.02μs -> 401ns (155% faster)

def test_large_mixed_values():
    # Test with large lists and mixed int/float values
    net = AlexNet()
    x = [[i if i % 2 == 0 else float(i) for i in range(500)] for _ in range(2)]
    codeflash_output = net._extract_features(x); features = codeflash_output # 842ns -> 370ns (128% faster)
    for sublist in features:
        for idx, val in enumerate(sublist):
            if idx % 2 == 0:
                pass
            else:
                pass

def test_large_empty_and_nonempty_mix():
    # Mix of empty and large lists
    net = AlexNet()
    x = [[], list(range(1000)), [], [1, 2, 3], []]
    codeflash_output = net._extract_features(x); features = codeflash_output # 962ns -> 341ns (182% faster)

def test_large_random_data():
    # Randomized data, but all numeric
    net = AlexNet()
    x = [[random.randint(-1000, 1000) for _ in range(100)] for _ in range(10)]
    codeflash_output = net._extract_features(x); features = codeflash_output # 862ns -> 341ns (153% faster)

# Additional edge: very large numbers of empty lists
def test_many_empty_lists():
    net = AlexNet()
    x = [[] for _ in range(1000)]
    codeflash_output = net._extract_features(x); features = codeflash_output # 15.5μs -> 371ns (4091% faster)

# Additional edge: single very large list
def test_single_very_large_list():
    net = AlexNet()
    x = [list(range(1000))]
    codeflash_output = net._extract_features(x); features = codeflash_output # 1.01μs -> 371ns (173% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.