from pytorch_caney.loss.utils import to_tensor

import unittest
import numpy as np
import torch


class TestToTensorFunction(unittest.TestCase):

    def test_tensor_input(self):
        tensor = torch.tensor([1, 2, 3])
        result = to_tensor(tensor)
        self.assertTrue(torch.equal(result, tensor))

    def test_tensor_input_with_dtype(self):
        tensor = torch.tensor([1, 2, 3])
        result = to_tensor(tensor, dtype=torch.float32)
        self.assertTrue(torch.equal(result, tensor.float()))

    def test_numpy_array_input(self):
        numpy_array = np.array([1, 2, 3])
        expected_tensor = torch.tensor([1, 2, 3])
        result = to_tensor(numpy_array)
        self.assertTrue(torch.equal(result, expected_tensor))

    def test_numpy_array_input_with_dtype(self):
        numpy_array = np.array([1, 2, 3])
        expected_tensor = torch.tensor([1, 2, 3], dtype=torch.float32)
        result = to_tensor(numpy_array, dtype=torch.float32)
        self.assertTrue(torch.equal(result, expected_tensor))

    def test_list_input(self):
        input_list = [1, 2, 3]
        expected_tensor = torch.tensor([1, 2, 3])
        result = to_tensor(input_list)
        self.assertTrue(torch.equal(result, expected_tensor))

    def test_list_input_with_dtype(self):
        input_list = [1, 2, 3]
        expected_tensor = torch.tensor([1, 2, 3], dtype=torch.float32)
        result = to_tensor(input_list, dtype=torch.float32)
        self.assertTrue(torch.equal(result, expected_tensor))


if __name__ == '__main__':
    unittest.main()