import random  # used for generating large random test data

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

# unit tests

# 1. Basic Test Cases

def test_forward_with_simple_flat_list():
    # Simple input: flat list of numbers
    model = AlexNet(num_classes=10)
    x = [1, 2, 3, 4, 5]
    # Features should be normalized: [0,0.25,0.5,0.75,1.0]
    # Max is at index 4, so output should be [4,5,6,7,8]
    codeflash_output = model.forward(x); out = codeflash_output # 1.52μs -> 411ns (271% faster)

def test_forward_with_nested_list():
    # Input: nested list
    model = AlexNet(num_classes=10)
    x = [[1,2],[3,4],[5,6]]
    # Flattened: [1,2,3,4,5,6], normalized
    # Max is at index 5, so output: [5,6,7,8,9,0]
    codeflash_output = model.forward(x); out = codeflash_output # 1.37μs -> 370ns (271% faster)

def test_forward_with_all_same_values():
    # Input: all values the same
    model = AlexNet(num_classes=10)
    x = [7,7,7,7]
    # Normalized: [0.0,0.0,0.0,0.0], max at index 0
    codeflash_output = model.forward(x); out = codeflash_output # 1.30μs -> 330ns (295% faster)

def test_forward_with_negative_values():
    # Input: negative numbers
    model = AlexNet(num_classes=10)
    x = [-3, -2, -1, 0]
    # Normalized: [0,0.333...,0.666...,1.0], max at index 3
    codeflash_output = model.forward(x); out = codeflash_output # 1.30μs -> 300ns (334% faster)

def test_forward_with_single_element():
    # Input: single element
    model = AlexNet(num_classes=10)
    x = [42]
    # Normalized: [0.0], max at index 0
    codeflash_output = model.forward(x); out = codeflash_output # 1.27μs -> 321ns (296% faster)

# 2. Edge Test Cases

def test_forward_with_empty_list():
    # Input: empty list
    model = AlexNet(num_classes=10)
    x = []
    codeflash_output = model.forward(x); out = codeflash_output # 1.28μs -> 341ns (276% faster)

def test_forward_with_large_numbers():
    # Input: very large numbers
    model = AlexNet(num_classes=100)
    x = [10**10, 10**12, 10**15]
    # Normalized: [0.0,0.001,1.0], max at index 2
    codeflash_output = model.forward(x); out = codeflash_output # 1.20μs -> 321ns (275% faster)

def test_forward_with_minimum_num_classes():
    # num_classes=1, all outputs must be 0
    model = AlexNet(num_classes=1)
    x = [1,2,3]
    codeflash_output = model.forward(x); out = codeflash_output # 1.22μs -> 320ns (282% faster)

def test_forward_with_maximum_num_classes():
    # num_classes=1000, output should not exceed 999
    model = AlexNet(num_classes=1000)
    x = [1,2,3,4,5]
    codeflash_output = model.forward(x); out = codeflash_output # 1.28μs -> 320ns (301% faster)

def test_forward_with_floats_and_ints():
    # Input: mix of floats and ints
    model = AlexNet(num_classes=10)
    x = [1, 2.5, 3, 4.5]
    codeflash_output = model.forward(x); out = codeflash_output # 1.23μs -> 310ns (297% faster)

def test_forward_with_repeated_max():
    # Input: multiple elements with same max value
    model = AlexNet(num_classes=10)
    x = [1,2,3,3]
    # Normalized: [0,0.5,1,1], first max at index 2
    codeflash_output = model.forward(x); out = codeflash_output # 1.22μs -> 311ns (293% faster)

# 3. Large Scale Test Cases

def test_forward_with_large_flat_list():
    # Large input: flat list, length 1000
    model = AlexNet(num_classes=1000)
    x = list(range(1000))
    codeflash_output = model.forward(x); out = codeflash_output # 1.30μs -> 351ns (271% faster)
    # Max at index 999, so output: [999,0,1,...,997,998]
    expected = [(999 + i) % 1000 for i in range(1000)]

def test_forward_with_large_nested_list():
    # Large input: nested list, 100 sublists of 10 elements
    model = AlexNet(num_classes=1000)
    x = [list(range(i*10, (i+1)*10)) for i in range(100)]
    codeflash_output = model.forward(x); out = codeflash_output # 1.35μs -> 350ns (286% faster)
    # Flattened: [0,1,2,...,999], max at index 999
    expected = [(999 + i) % 1000 for i in range(1000)]

def test_forward_with_large_random_values():
    # Large input: random values
    model = AlexNet(num_classes=500)
    random.seed(42)
    x = [random.randint(-10000, 10000) for _ in range(500)]
    codeflash_output = model.forward(x); out = codeflash_output # 1.25μs -> 331ns (278% faster)

def test_forward_with_large_all_same():
    # Large input: all values the same
    model = AlexNet(num_classes=100)
    x = [7]*1000
    codeflash_output = model.forward(x); out = codeflash_output # 1.26μs -> 320ns (294% faster)
    # Normalized: all 0.0, max at index 0
    expected = [i%100 for i in range(1000)]

def test_forward_with_large_negative_values():
    # Large input: all negative values
    model = AlexNet(num_classes=100)
    x = [-i for i in range(1000)]
    codeflash_output = model.forward(x); out = codeflash_output # 1.26μs -> 341ns (270% faster)
    # Max at index 0 (since -0 is max), output: [0,1,2,...,99,0,1,...]
    expected = [i%100 for i in range(1000)]

# 4. Additional edge: invalid input types



def test_forward_with_single_nested_element():
    model = AlexNet(num_classes=10)
    x = [[42]]
    codeflash_output = model.forward(x); out = codeflash_output # 1.54μs -> 441ns (250% faster)

# 6. Additional: input with only zeros

def test_forward_with_all_zeros():
    model = AlexNet(num_classes=10)
    x = [0,0,0,0]
    codeflash_output = model.forward(x); out = codeflash_output # 1.30μs -> 370ns (252% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.