"""Offline unit tests for moldovan-qwen/workflows OmniRoute workflows.

Uses a scripted fake OmniRouteClient; never touches network or real data
dirs (writes only to pytest tmp_path).
"""

from __future__ import annotations

import json
import math
import os
import sys
from typing import Any, Dict, List

import pytest

sys.path.insert(
    0, os.path.join(os.path.dirname(__file__), "..", "..", "moldovan-qwen")
)

from workflows.providers import (  # noqa: E402
    EMBEDDING_DIM,
    OmniRouteClient,
    OmniRouteError,
    strip_reasoning,
)
from workflows.summarizer import (  # noqa: E402
    _chunk_text,
    summarize_document,
)
from workflows.transcriber import transcribe_meeting  # noqa: E402
from workflows.semantic_search import (  # noqa: E402
    SemanticSearchIndex,
    _cosine,
)
from workflows.extractor import (  # noqa: E402
    extract_structured_data,
    _find_json_block,
)


class FakeClient(OmniRouteClient):
    """Scripted client: records calls, returns queued deterministic outputs."""

    def __init__(self, chat_outputs: List[str] = None, embed_fn=None):
        super().__init__(base_url="http://fake", api_key="test")
        self.chat_outputs = list(chat_outputs or [])
        self.chat_calls: List[List[Dict[str, str]]] = []
        self.embed_fn = embed_fn

    def chat(self, messages, model="1zero", max_tokens=512, temperature=0.7):
        self.chat_calls.append(messages)
        if not self.chat_outputs:
            raise OmniRouteError("no scripted chat outputs left")
        return self.chat_outputs.pop(0)

    def embed(self, texts, model="infinity/BAAI/bge-m3"):
        if self.embed_fn is None:
            raise OmniRouteError("no embed_fn configured")
        return [self.embed_fn(t) for t in texts]

    def transcribe(self, audio_path, model="whisperfw/Systran/faster-whisper-small"):
        with open(audio_path, "rb") as f:
            header = f.read(12)
        if header[:4] != b"RIFF":
            raise OmniRouteError("not a wav")
        return f"transcribed:{os.path.basename(audio_path)}"


def fake_embed(text: str) -> List[float]:
    """Deterministic 1024-dim vector keyed by first char of text."""
    vec = [0.0] * EMBEDDING_DIM
    key = sum(ord(c) for c in text.lower()) % EMBEDDING_DIM
    vec[key] = 1.0
    return vec


# ----------------------------------------------------------------------
# providers
# ----------------------------------------------------------------------
def test_embedding_dim_constant():
    assert EMBEDDING_DIM == 1024


def test_strip_reasoning_think_tags():
    raw = "<think>internal notes</think>Final answer here."
    assert strip_reasoning(raw) == "Final answer here."


def test_strip_reasoning_leaves_plain_text():
    assert strip_reasoning("Simple raspuns.") == "Simple raspuns."


def test_strip_reasoning_empty():
    assert strip_reasoning("") == ""
    assert strip_reasoning(None) is None


def test_strip_reasoning_thinking_process_with_answer():
    raw = (
        "Thinking Process:\n\n"
        "1.  **Analyze the Request:**\n"
        "    *   Task: Summarize.\n\n"
        "Final summary text: Raspunsul final aici."
    )
    assert strip_reasoning(raw) == "Final summary text: Raspunsul final aici."


def test_strip_reasoning_truncated_thinking_only():
    raw = (
        "Thinking Process:\n\n"
        "1.  **Analyze the Request:**\n"
        '    *   Sentence 2: "Orasul peste 500 mii locuitori" (missing verbs)...'
    )
    assert strip_reasoning(raw) == ""


def test_chat_error_includes_status(monkeypatch):
    client = OmniRouteClient(base_url="http://127.0.0.1:1", api_key="k", timeout=1.0)

    import urllib.request
    import urllib.error

    def raise_http_error(req, timeout):
        raise urllib.error.URLError("unreachable")

    monkeypatch.setattr(urllib.request, "urlopen", raise_http_error)
    with pytest.raises(OmniRouteError):
        client.chat([{"role": "user", "content": "hi"}])


# ----------------------------------------------------------------------
# summarizer
# ----------------------------------------------------------------------
def test_chunk_text_short_single_chunk():
    assert _chunk_text("Salut, aceasta este o fraza scurta.") == [
        "Salut, aceasta este o fraza scurta."
    ]


def test_chunk_text_empty_returns_empty_list():
    assert _chunk_text("") == []
    assert _chunk_text("   \n  ") == []


def test_chunk_text_long_splits_by_paragraph():
    text = "\n\n".join(f"Paragraf {i} " + "x" * 200 for i in range(10))
    chunks = _chunk_text(text, max_chars=500)
    assert len(chunks) >= 2
    assert all(len(c) <= 500 for c in chunks)
    joined = " ".join(chunks)
    for i in range(10):
        assert f"Paragraf {i}" in joined


def test_summarize_single_chunk():
    client = FakeClient(chat_outputs=["Rezumat scurt."])
    result = summarize_document("Text de test", client)
    assert result == {"summary": "Rezumat scurt.", "chunks": 1}
    assert len(client.chat_calls) == 1


def test_summarize_multi_chunk_reduces():
    long_text = "\n\n".join(
        f"Capitol {i} " + "continut lung " * 400 for i in range(4)
    )
    expected_chunks = len(_chunk_text(long_text))
    assert expected_chunks >= 2
    # One scripted output per partial chunk, then one final reduce output.
    outputs = [f"Partea {i}" for i in range(1, expected_chunks + 1)]
    outputs.append("REZUMAT FINAL")
    client = FakeClient(chat_outputs=outputs)
    result = summarize_document(long_text, client)
    assert result["chunks"] == expected_chunks
    assert len(client.chat_calls) == expected_chunks + 1
    assert result["summary"] == "REZUMAT FINAL"


def test_summarize_empty_raises():
    client = FakeClient()
    with pytest.raises(ValueError):
        summarize_document("   ", client)


def test_summarize_retries_on_empty_content():
    class EmptyFirstClient(FakeClient):
        def __init__(self):
            super().__init__()
            self.calls = 0

        def chat(self, messages, **kwargs):
            self.calls += 1
            return "" if self.calls == 1 else "Bun raspuns."

    client = EmptyFirstClient()
    result = summarize_document("Text scurt de test.", client)
    assert result["summary"] == "Bun raspuns."
    assert client.calls == 2

def test_summarize_system_prompt_suppresses_thinking():
    """Regression: 1zero leaks 'Thinking Process:' narration and can burn the
    whole token budget on reasoning, leaving sanitized content empty. The
    summarizer must instruct the model to answer directly without thinking."""
    client = FakeClient(chat_outputs=["Rezumat curat."])
    summarize_document("Text de test", client)
    system_msg = client.chat_calls[0][0]
    assert system_msg["role"] == "system"
    assert "nu afisa procesul de gandire" in system_msg["content"]


# ----------------------------------------------------------------------
# transcriber
# ----------------------------------------------------------------------
def _write_wav(path):
    # minimal fake RIFF header
    with open(path, "wb") as f:
        f.write(b"RIFF\x00\x00\x00\x00WAVE" + b"\x00" * 16)
    return path


def test_transcribe_meeting_single_file(tmp_path):
    wav = _write_wav(str(tmp_path / "meeting.wav"))
    client = FakeClient()
    result = transcribe_meeting([wav], client)
    assert result["transcript"] == "transcribed:meeting.wav"
    assert result["segments"] == [{"file": "meeting.wav", "text": "transcribed:meeting.wav"}]


def test_transcribe_meeting_directory_sorted(tmp_path):
    d = tmp_path / "segs"
    d.mkdir()
    _write_wav(str(d / "b.wav"))
    _write_wav(str(d / "a.wav"))
    client = FakeClient()
    result = transcribe_meeting([str(d)], client)
    assert result["segments"][0]["file"] == "a.wav"
    assert result["segments"][1]["file"] == "b.wav"


def test_transcribe_meeting_no_inputs_raises(tmp_path):
    client = FakeClient()
    with pytest.raises(ValueError):
        transcribe_meeting([str(tmp_path / "missing.wav")], client)
    with pytest.raises(ValueError):
        transcribe_meeting([str(tmp_path)], client)  # empty dir


def test_transcribe_meeting_unsupported_ext_raises(tmp_path):
    f = tmp_path / "notes.txt"
    f.write_text("hello")
    client = FakeClient()
    with pytest.raises(ValueError):
        transcribe_meeting([str(f)], client)


# ----------------------------------------------------------------------
# semantic search
# ----------------------------------------------------------------------
def test_cosine_identical_is_one():
    v = [1.0, 2.0, 3.0]
    assert math.isclose(_cosine(v, v), 1.0)


def test_cosine_orthogonal_is_zero():
    assert _cosine([1.0, 0.0], [0.0, 1.0]) == 0.0


def test_cosine_zero_vector_is_zero():
    assert _cosine([0.0, 0.0], [1.0, 1.0]) == 0.0


def test_semantic_search_ranks_relevant_first():
    client = FakeClient(embed_fn=fake_embed)
    idx = SemanticSearchIndex(client)
    idx.add_documents(["mar roshu", "banana galbena", "para verde"])
    results = idx.search("banana galbena", top_k=2)
    assert len(results) == 2
    assert results[0]["document"] == "banana galbena"
    assert results[0]["score"] == pytest.approx(1.0)
    assert results[0]["index"] == 1


def test_semantic_search_empty_index():
    client = FakeClient(embed_fn=fake_embed)
    idx = SemanticSearchIndex(client)
    assert idx.search("orice") == []


def test_semantic_search_min_score_filter():
    client = FakeClient(embed_fn=fake_embed)
    idx = SemanticSearchIndex(client)
    idx.add_documents(["unu", "doi"])
    results = idx.search("unu", top_k=5, min_score=0.99)
    assert len(results) == 1
    assert results[0]["document"] == "unu"


def test_semantic_index_dim_validation():
    client = FakeClient(embed_fn=lambda t: [0.0] * 10)  # wrong dim
    idx = SemanticSearchIndex(client)
    with pytest.raises(ValueError):
        idx.add_documents(["text"])


def test_semantic_index_save_load_roundtrip(tmp_path):
    client = FakeClient(embed_fn=fake_embed)
    idx = SemanticSearchIndex(client)
    idx.add_documents(["mar", "para", "pruna"])
    path = str(tmp_path / "index.json")
    idx.save(path)

    loaded = SemanticSearchIndex.load(path, client)
    assert loaded.documents == idx.documents
    assert loaded.vectors == idx.vectors
    assert loaded.search("para")[0]["document"] == "para"


def test_semantic_index_load_dim_mismatch(tmp_path):
    path = str(tmp_path / "bad.json")
    with open(path, "w") as f:
        json.dump({"model_dim": 768, "documents": [], "vectors": []}, f)
    with pytest.raises(ValueError):
        SemanticSearchIndex.load(path)


def test_semantic_index_load_len_mismatch(tmp_path):
    client = FakeClient(embed_fn=fake_embed)
    path = str(tmp_path / "bad2.json")
    with open(path, "w") as f:
        json.dump(
            {"model_dim": 1024, "documents": ["a"], "vectors": []}, f
        )
    with pytest.raises(ValueError):
        SemanticSearchIndex.load(path, client)


# ----------------------------------------------------------------------
# extractor
# ----------------------------------------------------------------------
def test_find_json_block_fenced():
    assert _find_json_block("text ```json\n{\"a\": 1}\n``` end") == '{"a": 1}'


def test_find_json_block_plain_braces():
    assert _find_json_block('Iată: {"a": 2} gata') == '{"a": 2}'


def test_find_json_block_none():
    assert _find_json_block("niciun json aici") is None


def test_extract_structured_data_success():
    client = FakeClient(chat_outputs=['{"nume": "Ion", "varsta": 40}'])
    schema = {"nume": "string", "varsta": "number"}
    result = extract_structured_data("Ion are 40 de ani.", schema, client)
    assert result == {"nume": "Ion", "varsta": 40}


def test_extract_structured_data_retry_on_garbage():
    client = FakeClient(
        chat_outputs=[
            "Nu pot raspunde",  # first attempt garbage
            '{"oras": "Chisinau"}',  # retry succeeds
        ]
    )
    result = extract_structured_data("Locuiesc in Chisinau.", {"oras": "string"}, client)
    assert result == {"oras": "Chisinau"}
    assert len(client.chat_calls) == 2  # original + retry


def test_extract_structured_data_fails_after_retry():
    client = FakeClient(chat_outputs=["garbage 1", "garbage 2"])
    with pytest.raises(OmniRouteError):
        extract_structured_data("text", {"a": "string"}, client)


def test_extract_structured_data_empty_text_raises():
    client = FakeClient()
    with pytest.raises(ValueError):
        extract_structured_data("", {"a": "string"}, client)


def test_extract_structured_data_with_fenced_json():
    client = FakeClient(chat_outputs=["```json\n{\"tara\": \"MD\"}\n```"])
    result = extract_structured_data("Republica Moldova", {"tara": "string"}, client)
    assert result == {"tara": "MD"}