from typing import Any, Protocol, cast

import pytest
from base.client_v2_base import TestMilvusClientV2Base
from common.common_type import CaseLabel
from common.text_generator import generate_text_by_analyzer


class AnalyzerResult(Protocol):
    """Protocol for analyzer result to help with type inference"""

    tokens: list[dict[str, Any]]


class TestMilvusClientAnalyzer(TestMilvusClientV2Base):
    @staticmethod
    def get_expected_jieba_tokens(text, analyzer_params):
        """
        Generate expected tokens using rjieba based on analyzer parameters.
        """
        import rjieba

        tokenizer_config = analyzer_params.get("tokenizer", {})
        if isinstance(tokenizer_config, str):
            tokenizer_config = {}

        # rjieba does not expose jieba-rs dynamic dictionary APIs. Fall back
        # to targeted assertions in custom-dictionary cases.
        if "dict" in tokenizer_config and tokenizer_config["dict"] != ["_default_"]:
            return None

        mode = tokenizer_config.get("mode", "search")
        hmm = tokenizer_config.get("hmm", True)

        if mode == "exact":
            tokens = list(rjieba.cut(text, hmm))
        elif mode == "search":
            tokens = list(rjieba.cut_for_search(text, hmm))
        else:
            tokens = list(rjieba.cut(text, hmm))

        # Filter out empty tokens
        tokens = [token for token in tokens if token.strip()]

        return tokens

    analyzer_params_list = [
        {
            "tokenizer": "standard",
            "filter": [
                {
                    "type": "stop",
                    "stop_words": ["is", "the", "this", "a", "an", "and", "or"],
                }
            ],
        },
        {
            "tokenizer": "jieba",
            "filter": [
                {
                    "type": "stop",
                    "stop_words": ["is", "the", "this", "a", "an", "and", "or", "是", "的", "这", "一个", "和", "或"],
                }
            ],
        },
        {"tokenizer": "icu"},
        # {
        #     "tokenizer": {"type": "lindera", "dict_kind": "ipadic"},
        #     "filter": [
        #         {
        #             "type": "stop",
        #             "stop_words": ["は", "が", "の", "に", "を", "で", "と", "た"],
        #         }
        #     ],
        # },
        # {"tokenizer": {"type": "lindera", "dict_kind": "ko-dic"}},
        # {"tokenizer": {"type": "lindera", "dict_kind": "cc-cedict"}},
    ]

    jieba_custom_analyzer_params_list = [
        # # Test dict parameter with custom dictionary
        {"tokenizer": {"type": "jieba", "dict": ["结巴分词器"], "mode": "exact", "hmm": False}},
        # Test dict parameter with default dict and custom dict
        {"tokenizer": {"type": "jieba", "dict": ["_default_", "结巴分词器"], "mode": "search", "hmm": False}},
        # Test exact mode with hmm enabled
        {"tokenizer": {"type": "jieba", "dict": ["结巴分词器"], "mode": "exact", "hmm": True}},
        # Test search mode with hmm enabled
        {"tokenizer": {"type": "jieba", "dict": ["结巴分词器"], "mode": "search", "hmm": True}},
        # Test with only mode configuration
        {"tokenizer": {"type": "jieba", "mode": "exact"}},
        # Test with only hmm configuration
        {"tokenizer": {"type": "jieba", "hmm": False}},
    ]

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("analyzer_params", analyzer_params_list)
    def test_analyzer(self, analyzer_params):
        """
        target: test analyzer
        method: use different analyzer params, then run analyzer to get the tokens
        expected: verify the tokens
        """
        client = self._client()
        text = generate_text_by_analyzer(analyzer_params)
        res, _ = self.run_analyzer(client, text, analyzer_params, with_detail=True, with_hash=True)
        res_2, _ = self.run_analyzer(client, text, analyzer_params, with_detail=True, with_hash=True)

        # Cast to help type inference for gRPC response
        analyzer_res = cast(AnalyzerResult, res)
        analyzer_res_2 = cast(AnalyzerResult, res_2)

        # verify the result are the same when run analyzer twice
        for i in range(len(analyzer_res.tokens)):
            assert analyzer_res.tokens[i]["token"] == analyzer_res_2.tokens[i]["token"]
            assert analyzer_res.tokens[i]["hash"] == analyzer_res_2.tokens[i]["hash"]
            assert analyzer_res.tokens[i]["start_offset"] == analyzer_res_2.tokens[i]["start_offset"]
            assert analyzer_res.tokens[i]["end_offset"] == analyzer_res_2.tokens[i]["end_offset"]
            assert analyzer_res.tokens[i]["position"] == analyzer_res_2.tokens[i]["position"]
            assert analyzer_res.tokens[i]["position_length"] == analyzer_res_2.tokens[i]["position_length"]

        tokens = analyzer_res.tokens
        token_list = [r["token"] for r in tokens]
        # Check tokens are not empty
        assert len(token_list) > 0, "No tokens were generated"

        # Check tokens are related to input text (all token should be a substring of the text)
        assert all(token.lower() in text.lower() for token in token_list), (
            "some of the tokens do not appear in the original text"
        )

        if "filter" in analyzer_params:
            for filter in analyzer_params["filter"]:
                if filter["type"] == "stop":
                    stop_words = filter["stop_words"]
                    assert not any(token in stop_words for token in tokens), "some of the tokens are stop words"

        # Check hash value and detail
        for r in tokens:
            assert isinstance(r["hash"], int)
            assert isinstance(r["start_offset"], int)
            assert isinstance(r["end_offset"], int)
            assert isinstance(r["position"], int)
            assert isinstance(r["position_length"], int)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize("analyzer_params", jieba_custom_analyzer_params_list)
    def test_jieba_custom_analyzer(self, analyzer_params):
        """
        target: test jieba analyzer with custom configurations
        method: use different jieba analyzer params with dict, mode, and hmm configurations
        expected: verify the tokens are generated correctly based on configuration
        """
        client = self._client()
        text = "milvus结巴分词器中文测试"
        res, _ = self.run_analyzer(client, text, analyzer_params, with_detail=True)

        analyzer_res = cast(AnalyzerResult, res)
        tokens = analyzer_res.tokens
        token_list = [r["token"] for r in tokens]

        # Check tokens are not empty
        assert len(token_list) > 0, "No tokens were generated"

        # Generate expected tokens using rjieba and compare when the Python
        # binding exposes the required tokenizer configuration.
        expected_tokens = self.get_expected_jieba_tokens(text, analyzer_params)
        if expected_tokens is None:
            custom_words = [
                word
                for word in analyzer_params["tokenizer"].get("dict", [])
                if word not in ("", "_default_", "_extend_default_")
            ]
            assert all(word in token_list for word in custom_words), (
                f"Expected custom words {custom_words}, but got {token_list}"
            )
        else:
            assert sorted(token_list) == sorted(expected_tokens), f"Expected {expected_tokens}, but got {token_list}"

        # Verify token details
        for r in tokens:
            assert isinstance(r["token"], str)
            assert isinstance(r["start_offset"], int)
            assert isinstance(r["end_offset"], int)
            assert isinstance(r["position"], int)
            assert isinstance(r["position_length"], int)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize(
        "invalid_analyzer_params",
        [
            {"tokenizer": "invalid_tokenizer"},
            {"tokenizer": 123},
            {"tokenizer": None},
            {"tokenizer": []},
            {"tokenizer": {"type": "invalid_type"}},
            {"tokenizer": {"type": None}},
            {"filter": "invalid_filter"},
            {"filter": [{"type": None}]},
            {"filter": [{"invalid_key": "value"}]},
        ],
    )
    def test_analyzer_with_invalid_params(self, invalid_analyzer_params):
        """
        target: test analyzer with invalid parameters
        method: use invalid analyzer params and expect errors
        expected: analyzer should raise appropriate exceptions
        """
        client = self._client()
        text = "test text for invalid analyzer"

        with pytest.raises(Exception):
            self.run_analyzer(client, text, invalid_analyzer_params)

    @pytest.mark.tags(CaseLabel.L1)
    def test_analyzer_with_empty_params(self):
        """
        target: test analyzer with empty parameters (uses default)
        method: use empty analyzer params
        expected: analyzer should use default configuration and work normally
        """
        client = self._client()
        text = "test text for empty analyzer"

        # Empty params should use default configuration
        res, _ = self.run_analyzer(client, text, {})
        analyzer_res = cast(AnalyzerResult, res)
        assert len(analyzer_res.tokens) > 0

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize(
        "invalid_text",
        [
            None,
            123,
            True,
            False,
        ],
    )
    def test_analyzer_with_invalid_text(self, invalid_text):
        """
        target: test analyzer with invalid text input
        method: use valid analyzer params but invalid text
        expected: analyzer should handle invalid text appropriately
        """
        client = self._client()
        analyzer_params = {"tokenizer": "standard"}

        with pytest.raises(Exception):
            self.run_analyzer(client, invalid_text, analyzer_params)

    @pytest.mark.tags(CaseLabel.L1)
    def test_analyzer_with_empty_text(self):
        """
        target: test analyzer with empty text
        method: use empty text input
        expected: analyzer should return empty tokens
        """
        client = self._client()
        analyzer_params = {"tokenizer": "standard"}

        res, _ = self.run_analyzer(client, "", analyzer_params)
        analyzer_res = cast(AnalyzerResult, res)
        assert len(analyzer_res.tokens) == 0

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize(
        "text_input",
        [
            [],
            {},
            ["list", "of", "strings"],
            {"key": "value"},
        ],
    )
    def test_analyzer_with_structured_text(self, text_input):
        """
        target: test analyzer with structured text input (list/dict)
        method: use list or dict as text input
        expected: analyzer should handle structured input and return tokens
        """
        client = self._client()
        analyzer_params = {"tokenizer": "standard"}

        res, _ = self.run_analyzer(client, text_input, analyzer_params)
        # For structured input, API returns direct list format
        assert isinstance(res, list)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize(
        "invalid_jieba_params",
        [
            {"tokenizer": {"type": "jieba", "dict": "not_a_list"}},
            {"tokenizer": {"type": "jieba", "dict": [123, 456]}},
            {"tokenizer": {"type": "jieba", "mode": "invalid_mode"}},
            {"tokenizer": {"type": "jieba", "mode": 123}},
            {"tokenizer": {"type": "jieba", "hmm": "not_boolean"}},
            {"tokenizer": {"type": "jieba", "hmm": 123}},
        ],
    )
    def test_jieba_analyzer_with_invalid_config(self, invalid_jieba_params):
        """
        target: test jieba analyzer with invalid configurations
        method: use jieba analyzer with invalid dict, mode, or hmm values
        expected: analyzer should raise appropriate exceptions
        """
        client = self._client()
        text = "测试文本 for jieba analyzer"

        with pytest.raises(Exception):
            self.run_analyzer(client, text, invalid_jieba_params)

    @pytest.mark.tags(CaseLabel.L1)
    def test_jieba_analyzer_with_empty_dict(self):
        """
        target: test jieba analyzer with empty dictionary
        method: use jieba analyzer with empty dict list
        expected: analyzer should work with empty dict (uses default)
        """
        client = self._client()
        text = "测试文本 for jieba analyzer"
        jieba_params = {"tokenizer": {"type": "jieba", "dict": []}}

        res, _ = self.run_analyzer(client, text, jieba_params)
        analyzer_res = cast(AnalyzerResult, res)
        assert len(analyzer_res.tokens) > 0

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize(
        "invalid_dict_config",
        [
            {"tokenizer": {"type": "jieba", "dict": None}},
            {"tokenizer": {"type": "jieba", "dict": "invalid_string"}},
            {"tokenizer": {"type": "jieba", "dict": 123}},
            {"tokenizer": {"type": "jieba", "dict": True}},
            {"tokenizer": {"type": "jieba", "dict": {"invalid": "dict"}}},
        ],
    )
    def test_jieba_analyzer_with_invalid_dict_values(self, invalid_dict_config):
        """
        target: test jieba analyzer with invalid dict configurations
        method: use jieba analyzer with invalid dict values
        expected: analyzer should raise appropriate exceptions
        """
        client = self._client()
        text = "测试文本 for jieba analyzer"

        with pytest.raises(Exception):
            self.run_analyzer(client, text, invalid_dict_config)

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize(
        "edge_case_dict_config",
        [
            {"tokenizer": {"type": "jieba", "dict": ["", "valid_word"]}},  # Empty string in list
            {"tokenizer": {"type": "jieba", "dict": ["valid_word", "valid_word"]}},  # Duplicate words
            {"tokenizer": {"type": "jieba", "dict": ["_default_"]}},  # Only default dict
        ],
    )
    def test_jieba_analyzer_with_edge_case_dict_values(self, edge_case_dict_config):
        """
        target: test jieba analyzer with edge case dict configurations
        method: use jieba analyzer with edge case dict values
        expected: analyzer should handle these cases gracefully
        """
        client = self._client()
        text = "测试文本 for jieba analyzer"

        res, _ = self.run_analyzer(client, text, edge_case_dict_config, with_detail=True)
        analyzer_res = cast(AnalyzerResult, res)
        # These should work but might not be recommended usage
        assert len(analyzer_res.tokens) >= 0

    @pytest.mark.tags(CaseLabel.L1)
    def test_jieba_analyzer_with_unknown_param(self):
        """
        target: test jieba analyzer with unknown parameter
        method: use jieba analyzer with invalid parameter name
        expected: analyzer should ignore unknown parameters and work normally
        """
        client = self._client()
        text = "测试文本 for jieba analyzer"
        jieba_params = {"tokenizer": {"type": "jieba", "invalid_param": "value"}}

        res, _ = self.run_analyzer(client, text, jieba_params)
        analyzer_res = cast(AnalyzerResult, res)
        assert len(analyzer_res.tokens) > 0

    @pytest.mark.tags(CaseLabel.L1)
    @pytest.mark.parametrize(
        "invalid_filter_params",
        [
            {"tokenizer": "standard", "filter": [{"type": "stop", "stop_words": "not_a_list"}]},
            {"tokenizer": "standard", "filter": [{"type": "stop", "stop_words": [123, 456]}]},
            {"tokenizer": "standard", "filter": [{"type": "invalid_filter_type"}]},
        ],
    )
    def test_analyzer_with_invalid_filter(self, invalid_filter_params):
        """
        target: test analyzer with invalid filter configurations
        method: use analyzer with invalid filter parameters
        expected: analyzer should handle invalid filters appropriately
        """
        client = self._client()
        text = "This is a test text with stop words"

        with pytest.raises(Exception):
            self.run_analyzer(client, text, invalid_filter_params)

    @pytest.mark.tags(CaseLabel.L1)
    def test_analyzer_with_empty_stop_words(self):
        """
        target: test analyzer with empty stop words list
        method: use stop filter with empty stop_words list
        expected: analyzer should work normally with empty stop words (no filtering)
        """
        client = self._client()
        text = "This is a test text with stop words"
        filter_params = {"tokenizer": "standard", "filter": [{"type": "stop", "stop_words": []}]}

        res, _ = self.run_analyzer(client, text, filter_params, with_detail=True)
        analyzer_res = cast(AnalyzerResult, res)
        tokens = analyzer_res.tokens
        token_list = [r["token"] for r in tokens]

        assert len(token_list) > 0
        # With empty stop words, no filtering should occur
        assert "is" in token_list  # Common stop word should still be present