import json

from audita.config import AuditaConfig
from audita.pipeline import process_transcript
from audita.schemas import CorrectionCandidate, CorrectionSet, parse_glossary_yaml, parse_transcript_json


class FakeLLMClient:
    def __init__(self, responses):
        self.responses = list(responses)
        self.calls = 0

    def create_corrections(self, messages, config):
        self.calls += 1
        return self.responses.pop(0)


def _config(tmp_path):
    return AuditaConfig(
        api_key="key",
        max_section_tokens=16000,
        confidence_threshold=0.8,
        max_retries=3,
        work_dir=tmp_path / "work",
    )


def _glossary():
    return parse_glossary_yaml(
        """
        glossary:
          - name: "Chauntea"
            category: deity
            summary: "Chauntea is a deity."
        """
    )


def _transcript():
    return parse_transcript_json(
        """
        [
          {"speaker": "Eric", "start": 0.0, "end": 1.0, "text": "I ask Chontia."}
        ]
        """
    )


def test_pipeline_processes_with_fake_llm_and_cleans_work_dir(tmp_path):
    correction = CorrectionCandidate(
        segment_id=0,
        original_text="I ask Chontia.",
        corrected_text="I ask Chauntea.",
        confidence=0.95,
    )
    fake_client = FakeLLMClient([CorrectionSet(corrections=[correction])])

    revised = process_transcript(
        _transcript(),
        _glossary(),
        _config(tmp_path),
        llm_client=fake_client,
    )

    assert revised[0].text == "I ask Chauntea."
    assert fake_client.calls == 1
    assert list((tmp_path / "work").iterdir()) == []


def test_pipeline_skips_bad_correction_and_preserves_diagnostics(tmp_path):
    correction = CorrectionCandidate(
        segment_id=0,
        original_text="Different text.",
        corrected_text="I ask Chauntea.",
        confidence=0.95,
    )
    fake_client = FakeLLMClient([CorrectionSet(corrections=[correction])])
    progress = []

    revised = process_transcript(
        _transcript(),
        _glossary(),
        _config(tmp_path),
        llm_client=fake_client,
        progress=progress.append,
    )

    assert revised[0].text == "I ask Chontia."
    assert any("Skipping correction for segment 0" in message for message in progress)
    preserved = list((tmp_path / "work").iterdir())
    assert len(preserved) == 1
    skipped_path = preserved[0] / "skipped-corrections.json"
    assert skipped_path.exists()
    diagnostics = json.loads(skipped_path.read_text(encoding="utf-8"))
    assert diagnostics["skipped_corrections"][0]["segment_id"] == 0
    assert "original_text" in diagnostics["skipped_corrections"][0]["reason"]