"""End-to-end test: verify limitations flow through the entire pipeline."""
from __future__ import annotations

import json
from pathlib import Path

from zotero_kb.cards import CardBuilder
from zotero_kb.config import AppConfig
from zotero_kb.llm import CardGenerationClient
from zotero_kb.projects import ProjectService
from zotero_kb.workspace import Workspace
from zotero_kb.zotero_reader import ZoteroItemRecord


class FakeLlmClientWithLimitations:
    """Returns card data including limitations."""

    def generate_card(self, source_bundle: dict[str, object]) -> dict[str, object]:
        print("\n" + "=" * 60)
        print("STEP 1: Source bundle received by LLM client")
        print("=" * 60)
        print(json.dumps(source_bundle, ensure_ascii=False, indent=2))

        language = str(source_bundle.get("card_language", "en"))
        limitations = [
            "Assumes all demonstration trajectories are successful and noise-free.",
            "Requires an expensive online planning step at test time.",
            "Evaluated only on a single robotic manipulation domain with limited diversity.",
        ] if language == "en" else [
            "假设所有演示轨迹都是成功且无噪声的。",
            "在测试时需要昂贵的在线规划步骤。",
            "仅在单一机器人操作领域上评估，多样性有限。",
        ]

        result = {
            "summary": "A method for robot control using hierarchical policies." if language == "en" else "一种使用分层策略进行机器人控制的方法。",
            "core_claims": ["Claim A", "Claim B"],
            "methods": ["Hierarchical policy framework", "Experience retrieval mechanism"],
            "limitations": limitations,
            "evidence": ["Ablation shows 78% → 31% drop"],
            "citations": [
                {
                    "claim": "Retrieval scales better than parametric memory.",
                    "quote": "Our retrieval-based approach outperforms...",
                    "paraphrase": "Retrieval works better.",
                    "quote_source": "attachment_texts",
                    "use_case": "direct_quote",
                }
            ],
            "quotable_passages": ["Short excerpt about retrieval."],
            "writing_hints": ["Cite when arguing about retrieval vs parametric."],
            "keywords": ["robotics", "memory"],
        }

        print("\n" + "=" * 60)
        print("STEP 2: LLM response (parsed JSON)")
        print("=" * 60)
        print(json.dumps(result, ensure_ascii=False, indent=2))
        return result


def test_limitations_end_to_end(tmp_path: Path) -> None:
    workspace_dir = tmp_path / "workspace"
    workspace_dir.mkdir()

    # Setup workspace + project
    config = AppConfig(workspace_dir=workspace_dir, zotero_data_dir=tmp_path / "zotero")
    workspace = Workspace(config)
    project = workspace.create_project(
        project_id="test-project",
        name="Test Project",
        llm_provider="deepseek",
        llm_model="deepseek-chat",
        card_language="en",
    )
    project_id = project.project_id

    # Build a fake item
    item = ZoteroItemRecord(
        item_key="MEMER2025",
        title="MemER: Scaling Up Memory for Robot Control",
        creators=["Sridhar et al."],
        year=2025,
        item_type="journalArticle",
        abstract="We propose MemER, a hierarchical policy framework.",
        tags=["robotics", "memory"],
        collection_paths=[["Robotics", "Manipulation"]],
        notes=["Important note about method."],
        attachments=[
            {
                "filename": "paper.pdf",
                "path": "/tmp/paper.pdf",
                "content_type": "application/pdf",
                "is_pdf": True,
            }
        ],
        attachment_texts=[
            "Our retrieval-based approach outperforms prior parametric methods...",
            "Limitations: We assume noise-free demonstrations.",
        ],
    )

    # STEP 3: CardBuilder generates the card
    print("\n" + "=" * 60)
    print("STEP 3: CardBuilder.build_or_update()")
    print("=" * 60)
    builder = CardBuilder(workspace_dir, FakeLlmClientWithLimitations())
    result = builder.build_or_update(item, language="en")

    # STEP 4: Read generated Markdown
    print("\n" + "=" * 60)
    print("STEP 4: Generated Markdown card")
    print("=" * 60)
    card_md = result.card_path.read_text(encoding="utf-8")
    print(card_md)

    # Verify Markdown contains Limitations
    assert "# Limitations" in card_md
    assert "noise-free" in card_md
    assert "online planning" in card_md

    # STEP 5: ProjectService rebuilds project-index
    print("\n" + "=" * 60)
    print("STEP 5: ProjectService._rebuild_project_index()")
    print("=" * 60)
    ps = ProjectService(workspace_dir)
    ps.add_items(project_id, ["MEMER2025"])
    project_index = ps.get_project_view(project_id)

    print(json.dumps(project_index, ensure_ascii=False, indent=2))

    # Verify project-index carries limitations
    items = project_index["items"]
    assert len(items) == 1
    project_item = items[0]
    assert "limitations" in project_item
    assert len(project_item["limitations"]) == 3
    assert "noise-free" in str(project_item["limitations"])

    print("\n" + "=" * 60)
    print("ALL CHECKS PASSED")
    print("=" * 60)