import os
import unittest

import numpy as np

import gradio.interpretation
import gradio.test_data
from gradio import Interface
from gradio.processing_utils import decode_base64_to_image

os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"


class TestDefault(unittest.TestCase):
    def test_default_text(self):
        max_word_len = lambda text: max([len(word) for word in text.split(" ")])
        text_interface = Interface(
            max_word_len, "textbox", "label", interpretation="default"
        )
        interpretation = text_interface.interpret(["quickest brown fox"])[0][0]
        self.assertGreater(
            interpretation[0][1], 0
        )  # Checks to see if the first word has >0 score.
        self.assertEqual(
            interpretation[-1][1], 0
        )  # Checks to see if the last word has 0 score.


class TestShapley(unittest.TestCase):
    def test_shapley_text(self):
        max_word_len = lambda text: max([len(word) for word in text.split(" ")])
        text_interface = Interface(
            max_word_len, "textbox", "label", interpretation="shapley"
        )
        interpretation = text_interface.interpret(["quickest brown fox"])[0][0]
        self.assertGreater(
            interpretation[0][1], 0
        )  # Checks to see if the first word has >0 score.
        self.assertEqual(
            interpretation[-1][1], 0
        )  # Checks to see if the last word has 0 score.


class TestCustom(unittest.TestCase):
    def test_custom_text(self):
        max_word_len = lambda text: max([len(word) for word in text.split(" ")])
        custom = lambda text: [(char, 1) for char in text]
        text_interface = Interface(
            max_word_len, "textbox", "label", interpretation=custom
        )
        result = text_interface.interpret(["quickest brown fox"])[0][0]
        self.assertEqual(
            result[0][1], 1
        )  # Checks to see if the first letter has score of 1.

    def test_custom_img(self):
        max_pixel_value = lambda img: img.max()
        custom = lambda img: img.tolist()
        img_interface = Interface(
            max_pixel_value, "image", "label", interpretation=custom
        )
        result = img_interface.interpret([gradio.test_data.BASE64_IMAGE])[0][0]
        expected_result = np.asarray(
            decode_base64_to_image(gradio.test_data.BASE64_IMAGE).convert("RGB")
        ).tolist()
        self.assertEqual(result, expected_result)


class TestHelperMethods(unittest.TestCase):
    def test_diff(self):
        diff = gradio.interpretation.diff(13, "2")
        self.assertEquals(diff, 11)
        diff = gradio.interpretation.diff("cat", "dog")
        self.assertEquals(diff, 1)
        diff = gradio.interpretation.diff("cat", "cat")
        self.assertEquals(diff, 0)

    def test_quantify_difference_with_number(self):
        iface = Interface(lambda text: text, ["textbox"], ["number"])
        diff = gradio.interpretation.quantify_difference_in_label(iface, [4], [6])
        self.assertEquals(diff, -2)

    def test_quantify_difference_with_label(self):
        iface = Interface(lambda text: len(text), ["textbox"], ["label"])
        diff = gradio.interpretation.quantify_difference_in_label(iface, ["3"], ["10"])
        self.assertEquals(diff, -7)
        diff = gradio.interpretation.quantify_difference_in_label(iface, ["0"], ["100"])
        self.assertEquals(diff, -100)

    def test_quantify_difference_with_confidences(self):
        iface = Interface(lambda text: len(text), ["textbox"], ["label"])
        output_1 = {"cat": 0.9, "dog": 0.1}
        output_2 = {"cat": 0.6, "dog": 0.4}
        output_3 = {"cat": 0.1, "dog": 0.6}
        diff = gradio.interpretation.quantify_difference_in_label(
            iface, [output_1], [output_2]
        )
        self.assertAlmostEquals(diff, 0.3)
        diff = gradio.interpretation.quantify_difference_in_label(
            iface, [output_1], [output_3]
        )
        self.assertAlmostEquals(diff, 0.8)

    def test_get_regression_value(self):
        iface = Interface(lambda text: text, ["textbox"], ["label"])
        output_1 = {"cat": 0.9, "dog": 0.1}
        output_2 = {"cat": float("nan"), "dog": 0.4}
        output_3 = {"cat": 0.1, "dog": 0.6}
        diff = gradio.interpretation.get_regression_or_classification_value(
            iface, [output_1], [output_2]
        )
        self.assertEquals(diff, 0)
        diff = gradio.interpretation.get_regression_or_classification_value(
            iface, [output_1], [output_3]
        )
        self.assertAlmostEquals(diff, 0.1)

    def test_get_classification_value(self):
        iface = Interface(lambda text: text, ["textbox"], ["label"])
        diff = gradio.interpretation.get_regression_or_classification_value(
            iface, ["cat"], ["test"]
        )
        self.assertEquals(diff, 1)
        diff = gradio.interpretation.get_regression_or_classification_value(
            iface, ["test"], ["test"]
        )
        self.assertEquals(diff, 0)


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