"""
Deep behavioral tests for topic_generator.py.
Tests _validate_topics logic, LLM response parsing patterns,
and generate_topics flow.
"""

import json


# --- _validate_topics logic ---


class TestValidateTopicsLogic:
    """Tests for _validate_topics extracted logic."""

    def _validate_topics(self, topics, max_topics):
        """Mirror of _validate_topics from topic_generator.py."""
        valid_topics = []
        seen = set()
        for topic in topics:
            if not topic:
                continue
            cleaned = topic.strip()
            if len(cleaned) < 2 or len(cleaned) > 30:
                continue
            normalized = cleaned.lower()
            if normalized in seen:
                continue
            seen.add(normalized)
            valid_topics.append(normalized)
            if len(valid_topics) >= max_topics:
                break
        if not valid_topics:
            valid_topics = ["[No valid topics]"]
        return valid_topics

    def test_empty_list(self):
        result = self._validate_topics([], 5)
        assert result == ["[No valid topics]"]

    def test_single_valid(self):
        result = self._validate_topics(["AI"], 5)
        assert result == ["ai"]

    def test_converts_to_lowercase(self):
        result = self._validate_topics(["Artificial Intelligence"], 5)
        assert result == ["artificial intelligence"]

    def test_strips_whitespace(self):
        result = self._validate_topics(["  AI  "], 5)
        assert result == ["ai"]

    def test_removes_too_short(self):
        result = self._validate_topics(["A", "AI", "ML"], 5)
        assert "a" not in result
        assert "ai" in result

    def test_removes_too_long(self):
        long_topic = "A" * 31
        result = self._validate_topics([long_topic, "AI"], 5)
        assert len(result) == 1
        assert result[0] == "ai"

    def test_exactly_30_chars_valid(self):
        topic = "A" * 30
        result = self._validate_topics([topic], 5)
        assert len(result) == 1

    def test_exactly_2_chars_valid(self):
        result = self._validate_topics(["AI"], 5)
        assert result == ["ai"]

    def test_removes_duplicates_case_insensitive(self):
        result = self._validate_topics(["AI", "ai", "Ai"], 5)
        assert result == ["ai"]

    def test_respects_max_topics(self):
        topics = [f"topic{i}" for i in range(10)]
        result = self._validate_topics(topics, 3)
        assert len(result) == 3

    def test_skips_empty_strings(self):
        result = self._validate_topics(["", "AI", ""], 5)
        assert result == ["ai"]

    def test_skips_none_values(self):
        result = self._validate_topics([None, "AI", None], 5)
        assert result == ["ai"]

    def test_all_invalid_returns_placeholder(self):
        result = self._validate_topics(["", "A", "B" * 31], 5)
        assert result == ["[No valid topics]"]

    def test_mixed_valid_invalid(self):
        result = self._validate_topics(["A", "Climate Change", "", "AI"], 5)
        assert "climate change" in result
        assert "ai" in result

    def test_preserves_order(self):
        result = self._validate_topics(
            ["climate", "economics", "technology"], 5
        )
        assert result == ["climate", "economics", "technology"]


# --- LLM response parsing patterns ---


class TestLLMResponseParsing:
    """Tests for LLM response parsing patterns used in topic_generator."""

    def test_parse_json_array(self):
        content = '["AI", "Climate", "Economy"]'
        topics = json.loads(content)
        assert topics == ["AI", "Climate", "Economy"]

    def test_parse_json_with_code_fence(self):
        content = '```json\n["AI", "Climate"]\n```'
        if content.startswith("```json"):
            content = content[7:]
        if content.endswith("```"):
            content = content[:-3]
        content = content.strip()
        topics = json.loads(content)
        assert topics == ["AI", "Climate"]

    def test_parse_comma_separated_fallback(self):
        content = "AI, Climate Change, Economy"
        topics = [t.strip().strip("\"'") for t in content.split(",")]
        filtered = [t for t in topics if t and len(t) <= 30]
        assert filtered == ["AI", "Climate Change", "Economy"]

    def test_invalid_json_triggers_fallback(self):
        content = "not valid json"
        try:
            topics = json.loads(content)
        except json.JSONDecodeError:
            if "," in content:
                topics = [t.strip().strip("\"'") for t in content.split(",")]
            else:
                topics = []
        assert topics == []

    def test_filter_non_string_items(self):
        topics = ["AI", 123, None, "Climate"]
        cleaned = [t for t in topics if isinstance(t, str)]
        assert cleaned == ["AI", "Climate"]

    def test_filter_too_long_items(self):
        topics = ["AI", "A" * 31, "Climate"]
        cleaned = [
            t.strip()
            for t in topics
            if isinstance(t, str) and len(t.strip()) <= 30
        ]
        assert cleaned == ["AI", "Climate"]

    def test_max_topics_limit(self):
        topics = ["T1", "T2", "T3", "T4", "T5", "T6"]
        max_topics = 3
        result = topics[:max_topics]
        assert len(result) == 3

    def test_empty_array_json(self):
        content = "[]"
        topics = json.loads(content)
        assert topics == []

    def test_nested_code_fence_handling(self):
        content = '```json\n["Topic 1"]\n```'
        if content.startswith("```json"):
            content = content[7:]
        if content.endswith("```"):
            content = content[:-3]
        result = json.loads(content.strip())
        assert result == ["Topic 1"]


# --- generate_topics flow ---


class TestGenerateTopicsFlow:
    """Tests for the generate_topics function flow patterns."""

    def test_llm_failure_returns_placeholder(self):
        """When LLM returns nothing, should get failure placeholder."""
        topics = []
        if not topics:
            topics = ["[Topic generation failed]"]
        assert topics == ["[Topic generation failed]"]

    def test_llm_success_validates(self):
        """When LLM returns topics, they should be validated."""
        llm_topics = ["AI", "Climate", "A"]  # "A" is too short
        # Simulate validation
        valid = [t.lower() for t in llm_topics if len(t) >= 2 and len(t) <= 30]
        assert valid == ["ai", "climate"]

    def test_query_preview_truncation(self):
        query = "A" * 600
        preview = query[:500] if len(query) > 500 else query
        assert len(preview) == 500

    def test_query_preview_no_truncation(self):
        query = "short query"
        preview = query[:500] if len(query) > 500 else query
        assert preview == "short query"

    def test_findings_preview_truncation(self):
        findings = "B" * 1500
        preview = (
            findings[:1000] if findings and len(findings) > 1000 else findings
        )
        assert len(preview) == 1000

    def test_findings_none_handled(self):
        findings = None
        preview = (
            findings[:1000] if findings and len(findings) > 1000 else findings
        )
        assert preview is None

    def test_findings_empty_handled(self):
        findings = ""
        preview = (
            findings[:1000] if findings and len(findings) > 1000 else findings
        )
        assert preview == ""


# --- Headline generation patterns ---


class TestHeadlineGenerationPatterns:
    """Tests for headline generation patterns from headline_generator.py."""

    def test_headline_cleanup_strip_quotes(self):
        headline = '"Breaking News About AI"'
        cleaned = headline.strip("\"'.,!?")
        assert cleaned == "Breaking News About AI"

    def test_headline_cleanup_strip_periods(self):
        headline = "AI Advances Today."
        cleaned = headline.strip("\"'.,!?")
        assert cleaned == "AI Advances Today"

    def test_headline_empty_after_strip(self):
        headline = '""'
        cleaned = headline.strip("\"'.,!?")
        assert cleaned == ""

    def test_headline_no_cleanup_needed(self):
        headline = "AI Makes Progress"
        cleaned = headline.strip("\"'.,!?")
        assert cleaned == "AI Makes Progress"

    def test_llm_returns_none_fallback(self):
        llm_headline = None
        if llm_headline:
            result = llm_headline
        else:
            result = "[Headline generation failed]"
        assert result == "[Headline generation failed]"

    def test_llm_returns_empty_fallback(self):
        llm_headline = ""
        if llm_headline:
            result = llm_headline
        else:
            result = "[Headline generation failed]"
        assert result == "[Headline generation failed]"

    def test_no_findings_returns_none(self):
        """Without findings, LLM headline should return None."""
        findings = ""
        if not findings:
            result = None
        else:
            result = "Some headline"
        assert result is None

    def test_findings_length_logging(self):
        findings = "A" * 500
        length = len(findings)
        assert length == 500
