import unittest

import numpy as np
import torch
from sentence_transformers import SentenceTransformer

from .encoder_models import SBertEncoder, get_encoder, get_sbert_encoder
from .semncg import (
    RankedGains,
    compute_cosine_similarity,
    compute_gain,
    score_ncg,
    compute_ncg,
    _validate_input_format,
    SemNCG
)
from .utils import (
    get_gpu,
    slice_embeddings,
    is_nested_list_of_type,
    flatten_list,
    prep_sentences,
    tokenize_and_prep_document
)


class TestUtils(unittest.TestCase):
    def test_get_gpu(self):
        gpu_count = torch.cuda.device_count()
        gpu_available = torch.cuda.is_available()

        # Test single boolean input
        self.assertEqual(get_gpu(True), 0 if gpu_available else "cpu")
        self.assertEqual(get_gpu(False), "cpu")

        # Test single string input
        self.assertEqual(get_gpu("cpu"), "cpu")
        self.assertEqual(get_gpu("gpu"), 0 if gpu_available else "cpu")
        self.assertEqual(get_gpu("cuda"), 0 if gpu_available else "cpu")

        # Test single integer input
        self.assertEqual(get_gpu(0), 0 if gpu_available else "cpu")
        self.assertEqual(get_gpu(1), 1 if gpu_available else "cpu")

        # Test list input with unique elements
        self.assertEqual(get_gpu([True, "cpu", 0]), [0, "cpu"] if gpu_available else ["cpu", "cpu", "cpu"])

        # Test list input with duplicate elements
        self.assertEqual(get_gpu([0, 0, "gpu"]), 0 if gpu_available else ["cpu", "cpu", "cpu"])

        # Test list input with duplicate elements of different types
        self.assertEqual(get_gpu([True, 0, "gpu"]), 0 if gpu_available else ["cpu", "cpu", "cpu"])

        # Test list input but only one element
        self.assertEqual(get_gpu([True]), 0 if gpu_available else "cpu")

        # Test list input with all integers
        self.assertEqual(get_gpu(list(range(gpu_count))),
                         list(range(gpu_count)) if gpu_available else gpu_count * ["cpu"])

        with self.assertRaises(ValueError):
            get_gpu("invalid")

        with self.assertRaises(ValueError):
            get_gpu(torch.cuda.device_count())

    def test_prep_sentences(self):
        # Test normal case
        self.assertEqual(prep_sentences(["Hello, world!", " This is a test. ", "!!!"]),
                         ['Hello, world!', 'This is a test.'])

        # Test case with only punctuations
        with self.assertRaises(ValueError):
            prep_sentences(["!!!", "..."])

        # Test case with empty list
        with self.assertRaises(ValueError):
            prep_sentences([])

    def test_tokenize_and_prep_document(self):
        # Test tokenize=True with string input
        self.assertEqual(tokenize_and_prep_document("Hello, world! This is a test.", True),
                         ['Hello, world!', 'This is a test.'])

        # Test tokenize=False with list of strings input
        self.assertEqual(tokenize_and_prep_document(["Hello, world!", "This is a test."], False),
                         ['Hello, world!', 'This is a test.'])

        # Test tokenize=True with empty document
        with self.assertRaises(ValueError):
            tokenize_and_prep_document("!!! ...", True)

    def test_slice_embeddings(self):
        # Case 1
        embeddings = np.random.rand(10, 5)
        num_sentences = [3, 2, 5]
        expected_output = [embeddings[:3], embeddings[3:5], embeddings[5:]]
        self.assertTrue(
            all(np.array_equal(a, b) for a, b in zip(slice_embeddings(embeddings, num_sentences),
                                                     expected_output))
        )

        # Case 2
        num_sentences_nested = [[2, 1], [3, 4]]
        expected_output_nested = [[embeddings[:2], embeddings[2:3]], [embeddings[3:6], embeddings[6:]]]
        self.assertTrue(
            slice_embeddings(embeddings, num_sentences_nested), expected_output_nested
        )

        # Case 3
        document_sentences_count = [10, 8, 7]
        reference_sentences_count = [5, 3, 2]
        pred_sentences_count = [2, 2, 1]
        all_embeddings = np.random.rand(
            sum(document_sentences_count + reference_sentences_count + pred_sentences_count), 5,
        )

        embeddings = all_embeddings
        expected_doc_embeddings = [embeddings[:10], embeddings[10:18], embeddings[18:25]]

        embeddings = all_embeddings[25:]
        expected_ref_embeddings = [embeddings[:5], embeddings[5:8], embeddings[8:10]]

        embeddings = all_embeddings[35:]
        expected_pred_embeddings = [embeddings[:2], embeddings[2:4], embeddings[4:5]]

        doc_embeddings = slice_embeddings(all_embeddings, document_sentences_count)
        ref_embeddings = slice_embeddings(all_embeddings[sum(document_sentences_count):], reference_sentences_count)
        pred_embeddings = slice_embeddings(
            all_embeddings[sum(document_sentences_count + reference_sentences_count):], pred_sentences_count
        )

        self.assertTrue(doc_embeddings, expected_doc_embeddings)
        self.assertTrue(ref_embeddings, expected_ref_embeddings)
        self.assertTrue(pred_embeddings, expected_pred_embeddings)

        with self.assertRaises(TypeError):
            slice_embeddings(embeddings, "invalid")

    def test_is_nested_list_of_type(self):
        # Test case: Depth 0, single element matching element_type
        self.assertEqual(is_nested_list_of_type("test", str, 0), (True, ""))

        # Test case: Depth 0, single element not matching element_type
        is_valid, err_msg = is_nested_list_of_type("test", int, 0)
        self.assertEqual(is_valid, False)

        # Test case: Depth 1, list of elements matching element_type
        self.assertEqual(is_nested_list_of_type(["apple", "banana"], str, 1), (True, ""))

        # Test case: Depth 1, list of elements not matching element_type
        is_valid, err_msg = is_nested_list_of_type([1, 2, 3], str, 1)
        self.assertEqual(is_valid, False)

        # Test case: Depth 0 (Wrong), list of elements matching element_type
        is_valid, err_msg = is_nested_list_of_type([1, 2, 3], str, 0)
        self.assertEqual(is_valid, False)

        # Depth 2
        self.assertEqual(is_nested_list_of_type([[1, 2], [3, 4]], int, 2), (True, ""))
        self.assertEqual(is_nested_list_of_type([['1', '2'], ['3', '4']], str, 2), (True, ""))
        is_valid, err_msg = is_nested_list_of_type([[1, 2], ["a", "b"]], int, 2)
        self.assertEqual(is_valid, False)

        # Depth 3
        is_valid, err_msg = is_nested_list_of_type([[[1], [2]], [[3], [4]]], list, 3)
        self.assertEqual(is_valid, False)
        self.assertEqual(is_nested_list_of_type([[[1], [2]], [[3], [4]]], int, 3), (True, ""))

        # Test case: Depth is negative, expecting ValueError
        with self.assertRaises(ValueError):
            is_nested_list_of_type([1, 2], int, -1)

    def test_flatten_list(self):
        self.assertEqual(flatten_list([1, [2, 3], [[4], 5]]), [1, 2, 3, 4, 5])
        self.assertEqual(flatten_list([]), [])
        self.assertEqual(flatten_list([1, 2, 3]), [1, 2, 3])
        self.assertEqual(flatten_list([[[[1]]]]), [1])


class TestSBertEncoder(unittest.TestCase):

    def setUp(self) -> None:
        # Set up a test SentenceTransformer model
        self.model_name = "paraphrase-distilroberta-base-v1"
        self.sbert_model = get_sbert_encoder(self.model_name)
        self.device = "cpu"  # For testing on CPU
        self.batch_size = 32
        self.verbose = False
        self.encoder = SBertEncoder(self.sbert_model, self.device, self.batch_size, self.verbose)

    def test_encode_single_sentence(self):
        sentence = "Hello, world!"
        embeddings = self.encoder.encode([sentence])
        self.assertEqual(embeddings.shape, (1, 768))  # Adjust shape based on your model's embedding dimension

    def test_encode_multiple_sentences(self):
        sentences = ["Hello, world!", "This is a test."]
        embeddings = self.encoder.encode(sentences)
        self.assertEqual(embeddings.shape, (2, 768))  # Adjust shape based on your model's embedding dimension

    def test_get_sbert_encoder(self):
        model_name = "paraphrase-distilroberta-base-v1"
        sbert_model = get_sbert_encoder(model_name)
        self.assertIsInstance(sbert_model, SentenceTransformer)

    def test_encode_with_gpu(self):
        if torch.cuda.is_available():
            device = "cuda"
            encoder = get_encoder(self.sbert_model, device, self.batch_size, self.verbose)
            sentences = ["Hello, world!", "This is a test."]
            embeddings = encoder.encode(sentences)
            self.assertEqual(embeddings.shape, (2, 768))  # Adjust shape based on your model's embedding dimension
        else:
            self.skipTest("CUDA not available, skipping GPU test.")

    def test_encode_multi_device(self):
        if torch.cuda.device_count() < 2:
            self.skipTest("Multi-GPU test requires at least 2 GPUs.")
        else:
            devices = ["cuda:0", "cuda:1"]
            encoder = get_encoder(self.sbert_model, devices, self.batch_size, self.verbose)
            sentences = ["This is a test sentence.", "Here is another sentence.", "This is a test sentence."]
            embeddings = encoder.encode(sentences)
            self.assertIsInstance(embeddings, np.ndarray)
            self.assertEqual(embeddings.shape[0], 3)
            self.assertEqual(embeddings.shape[1], self.encoder.model.get_sentence_embedding_dimension())


class TestGetEncoder(unittest.TestCase):
    def setUp(self):
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.batch_size = 8
        self.verbose = False

    def _base_test(self, model_name):
        sbert_model = get_sbert_encoder(model_name)
        encoder = get_encoder(sbert_model, self.device, self.batch_size, self.verbose)

        # Assert
        self.assertIsInstance(encoder, SBertEncoder)
        self.assertEqual(encoder.device, self.device)
        self.assertEqual(encoder.batch_size, self.batch_size)
        self.assertEqual(encoder.verbose, self.verbose)

    def test_get_sbert_encoder(self):
        model_name = "stsb-roberta-large"
        self._base_test(model_name)

    def test_sbert_model(self):
        model_name = "all-mpnet-base-v2"
        self._base_test(model_name)

    def test_huggingface_model(self):
        """Test Huggingface models which work with SBert library"""
        model_name = "roberta-base"
        self._base_test(model_name)

    def test_get_encoder_environment_error(self):  # This parameter is used when using patch decorator
        model_name = "abc"  # Wrong model_name
        with self.assertRaises(EnvironmentError):
            get_sbert_encoder(model_name)

    def test_get_encoder_other_exception(self):
        model_name = "apple/OpenELM-270M"  # This model is not supported by SentenceTransformer lib
        with self.assertRaises(RuntimeError):
            get_sbert_encoder(model_name)


class TestRankedGainsDataclass(unittest.TestCase):
    def test_ranked_gains_dataclass(self):
        # Test initialization and attribute access
        gt_gains = [("doc1", 0.8), ("doc2", 0.6)]
        pred_gains = [("doc2", 0.7), ("doc1", 0.5)]
        k = 2
        ncg = 0.75
        ranked_gains = RankedGains(gt_gains, pred_gains, k, ncg)

        self.assertEqual(ranked_gains.gt_gains, gt_gains)
        self.assertEqual(ranked_gains.pred_gains, pred_gains)
        self.assertEqual(ranked_gains.k, k)
        self.assertEqual(ranked_gains.ncg, ncg)


class TestComputeCosineSimilarity(unittest.TestCase):
    def test_compute_cosine_similarity(self):
        doc_embeds = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]])
        ref_embeds = np.array([[0.2, 0.3, 0.4], [0.5, 0.6, 0.7]])
        # Test compute_cosine_similarity function
        similarity_scores = compute_cosine_similarity(doc_embeds, ref_embeds)
        print(similarity_scores)

        # Example values, change as per actual function output
        expected_scores = [0.980, 0.997]

        self.assertAlmostEqual(similarity_scores[0], expected_scores[0], places=3)
        self.assertAlmostEqual(similarity_scores[1], expected_scores[1], places=3)


class TestComputeGain(unittest.TestCase):
    def test_compute_gain(self):
        # Test compute_gain function
        sim_scores = [0.8, 0.6, 0.7]
        gains = compute_gain(sim_scores)
        print(gains)

        # Example values, change as per actual function output
        expected_gains = [(0, 0.5), (2, 0.3333333333333333), (1, 0.16666666666666666)]

        self.assertEqual(gains, expected_gains)


class TestScoreNcg(unittest.TestCase):
    def test_score_ncg(self):
        # Test score_ncg function
        model_relevance = [0.8, 0.7, 0.6]
        gt_relevance = [1.0, 0.9, 0.8]
        ncg_score = score_ncg(model_relevance, gt_relevance)
        expected_ncg = 0.778  # Example value, change as per actual function output

        self.assertAlmostEqual(ncg_score, expected_ncg, places=3)


class TestComputeNcg(unittest.TestCase):
    def test_compute_ncg(self):
        # Test compute_ncg function
        pred_gains = [(0, 0.8), (2, 0.7), (1, 0.6)]
        gt_gains = [(0, 1.0), (1, 0.9), (2, 0.8)]
        k = 3
        ncg_score = compute_ncg(pred_gains, gt_gains, k)
        expected_ncg = 1.0  # TODO: Confirm this with Dr. Santu

        self.assertAlmostEqual(ncg_score, expected_ncg, places=6)


class TestValidateInputFormat(unittest.TestCase):
    def test_validate_input_format(self):
        # Test _validate_input_format function
        tokenize_sentences = True
        predictions = ["Prediction 1", "Prediction 2"]
        references = ["Reference 1", "Reference 2"]
        documents = ["Document 1", "Document 2"]

        # No exception should be raised for valid input
        try:
            _validate_input_format(tokenize_sentences, predictions, references, documents)
        except ValueError as e:
            self.fail(f"_validate_input_format raised ValueError unexpectedly: {str(e)}")

        # Test invalid input format
        predictions_invalid = [["Sentence 1 in prediction 1.", "Sentence 2 in prediction 1."],
                               ["Sentence 1 in prediction 2.", "Sentence 2 in prediction 2."]]
        references_invalid = [["Sentences in reference 1."], ["Sentences in reference 2."]]
        documents_invalid = [["Sentence 1 in document 1.", "Sentence 2 in document 1."],
                             ["Sentence 1 in document 2.", "Sentence 2 in document 2."]]

        with self.assertRaises(ValueError):
            _validate_input_format(tokenize_sentences, predictions_invalid, references, documents)

        with self.assertRaises(ValueError):
            _validate_input_format(tokenize_sentences, predictions, references_invalid, documents)

        with self.assertRaises(ValueError):
            _validate_input_format(tokenize_sentences, predictions, references, documents_invalid)


class TestSemNCG(unittest.TestCase):
    def setUp(self):
        self.model_name = "stsb-distilbert-base"
        self.metric = SemNCG(self.model_name)

    def _basic_assertion(self, result, debug: bool = False):
        self.assertIsInstance(result, tuple)
        self.assertEqual(len(result), 2)
        self.assertIsInstance(result[0], float)
        self.assertTrue(0.0 <= result[0] <= 1.0)
        self.assertIsInstance(result[1], list)
        if debug:
            for ranked_gain in result[1]:
                self.assertTrue(isinstance(ranked_gain, RankedGains))
                self.assertTrue(0.0 <= ranked_gain.ncg <= 1.0)
        else:
            for gain in result[1]:
                self.assertTrue(isinstance(gain, float))
                self.assertTrue(0.0 <= gain <= 1.0)

    def test_compute_basic(self):
        predictions = ["The cat sat on the mat.", "The quick brown fox jumps over the lazy dog."]
        references = ["A cat was sitting on a mat.", "A quick brown fox jumped over a lazy dog."]
        documents = ["There was a cat on a mat.", "The quick brown fox jumped over the lazy dog."]

        result = self.metric.compute(predictions=predictions, references=references, documents=documents)
        self._basic_assertion(result)

    def test_compute_with_tokenization(self):
        predictions = [["The cat sat on the mat."], ["The quick brown fox jumps over the lazy dog."]]
        references = [["A cat was sitting on a mat."], ["A quick brown fox jumped over a lazy dog."]]
        documents = [["There was a cat on a mat."], ["The quick brown fox jumped over the lazy dog."]]

        result = self.metric.compute(
            predictions=predictions, references=references, documents=documents, tokenize_sentences=False
        )
        self._basic_assertion(result)

    def test_compute_with_pre_compute_embeddings(self):
        predictions = ["The cat sat on the mat.", "The quick brown fox jumps over the lazy dog."]
        references = ["A cat was sitting on a mat.", "A quick brown fox jumped over a lazy dog."]
        documents = ["There was a cat on a mat.", "The quick brown fox jumped over the lazy dog."]

        result = self.metric.compute(
            predictions=predictions, references=references, documents=documents, pre_compute_embeddings=True
        )
        self._basic_assertion(result)

    def test_compute_with_debug(self):
        predictions = ["The cat sat on the mat.", "The quick brown fox jumps over the lazy dog."]
        references = ["A cat was sitting on a mat.", "A quick brown fox jumped over a lazy dog."]
        documents = ["There was a cat on a mat.", "The quick brown fox jumped over the lazy dog."]

        result = self.metric.compute(
            predictions=predictions, references=references, documents=documents, debug=True
        )
        self._basic_assertion(result, debug=True)

    def test_compute_invalid_input_format(self):
        predictions = "The cat sat on the mat."
        references = ["A cat was sitting on a mat."]
        documents = ["There was a cat on a mat."]

        with self.assertRaises(ValueError):
            self.metric.compute(predictions=predictions, references=references, documents=documents)

    def test_bad_inputs(self):
        def _call_metric(preds, refs, docs, tok):
            with self.assertRaises(Exception) as ctx:
                _ = self.metric.compute(
                    predictions=preds,
                    references=refs,
                    documents=docs,
                    tokenize_sentences=tok,
                    pre_compute_embeddings=True,
                )
            print(f"Raised Exception with message: {ctx.exception}")
            return ""

        # None Inputs
        # Case I
        tokenize_sentences = True
        predictions = [None]
        references = ["A cat was sitting on a mat."]
        documents = ["There was a cat on a mat."]
        print(f"Case I\n{_call_metric(predictions, references, documents, tokenize_sentences)}\n")

        # Case II
        tokenize_sentences = False
        predictions = [["A cat was sitting on a mat.", None]]
        references = [["A cat was sitting on a mat.", "A cat was sitting on a mat."]]
        documents = [["There was a cat on a mat.", "There was a cat on a mat."]]
        print(f"Case II\n{_call_metric(predictions, references, documents, tokenize_sentences)}\n")

        # Empty Input
        tokenize_sentences = True
        predictions = []
        references = ["A cat was sitting on a mat."]
        documents = ["There was a cat on a mat."]
        print(f"Case: Empty Input\n{_call_metric(predictions, references, documents, tokenize_sentences)}\n")

        # Empty String Input
        tokenize_sentences = True
        predictions = [""]
        references = ["A cat was sitting on a mat."]
        documents = ["There was a cat on a mat."]
        print(f"Case: Empty String Input\n{_call_metric(predictions, references, documents, tokenize_sentences)}\n")


if __name__ == '__main__':
    unittest.main(verbosity=2)