import json

from audita.chunking import chunk_transcript
from audita.prompts import (
    build_glossary_correction_messages,
    build_grammar_correction_messages,
    build_grammar_spoken_form_validation_messages,
    build_grammar_validation_messages,
)
from audita.schemas import parse_glossary_yaml, parse_transcript_json


def test_prompt_requires_acoustically_plausible_transcription_errors():
    transcript = parse_transcript_json(
        """
        [
          {"id": 1, "speaker": "Eric", "start": 0.0, "end": 1.0, "text": "The gestures are nearby."}
        ]
        """
    )
    glossary = parse_glossary_yaml(
        """
        glossary:
          - name: "Jesters"
            category: faction
            summary: "The Jesters are a local faction."
          - name: "Lyra"
            category: npc
            summary: "Lyra is a hostile NPC."
        """
    )
    section = chunk_transcript(transcript, max_section_tokens=16000)[0]

    messages = build_glossary_correction_messages(section, glossary)
    prompt_text = "\n".join(message["content"] for message in messages)

    assert "acoustically plausible" in prompt_text
    assert "phonetically or acoustically similar" in prompt_text
    assert '"gestures" to "Jesters"' in prompt_text
    assert '"Lyra" to "Jesters"' in prompt_text
    assert "should be omitted" in prompt_text
    assert "glossary names and aliases already present in the transcript as protected spellings" in prompt_text
    assert "Do not replace, Anglicize, normalize, lowercase" in prompt_text
    assert "Preserve canonical glossary capitalization" in prompt_text
    assert "exact text span that needs replacement" in prompt_text
    assert "replacement text for that span" in prompt_text
    assert "Do not return corrections where original_text and corrected_text are identical" in prompt_text


def test_prompt_uses_simplified_segment_payload():
    transcript = parse_transcript_json(
        """
        [
          {"id": 1, "speaker": "Eric", "start": 0.0, "end": 1.0, "text": "The gestures are nearby."}
        ]
        """
    )
    glossary = parse_glossary_yaml(
        """
        glossary:
          - name: "Jesters"
            category: faction
            summary: "The Jesters are a local faction."
        """
    )
    section = chunk_transcript(transcript, max_section_tokens=16000)[0]

    messages = build_glossary_correction_messages(section, glossary)
    transcript_json = messages[1]["content"].split("Transcript section:\n", maxsplit=1)[1]
    prompt_segments = json.loads(transcript_json)

    assert prompt_segments == [{"id": 1, "original_text": "The gestures are nearby."}]
    assert "speaker" not in prompt_segments[0]
    assert "start" not in prompt_segments[0]
    assert "end" not in prompt_segments[0]


def test_prompts_do_not_include_inferred_plurals():
    transcript = parse_transcript_json(
        """
        [
          {"id": 1, "speaker": "Eric", "start": 0.0, "end": 1.0, "text": "The gestures are nearby."}
        ]
        """
    )
    glossary = parse_glossary_yaml(
        """
        glossary:
          - name: "Godfrey"
            aliases:
              - "Jester"
            category: npc
            summary: "Godfrey is an NPC."
        """
    )
    section = chunk_transcript(transcript, max_section_tokens=16000)[0]

    glossary_messages = build_glossary_correction_messages(section, glossary)
    glossary_json = glossary_messages[1]["content"].split("Glossary:\n", maxsplit=1)[1].split(
        "\n\nTranscript section:",
        maxsplit=1,
    )[0]
    grammar_messages = build_grammar_correction_messages(section, glossary)
    grammar_json = grammar_messages[1]["content"].split("Protected glossary/context:\n", maxsplit=1)[1].split(
        "\n\nTranscript section:",
        maxsplit=1,
    )[0]

    for prompt_glossary in (json.loads(glossary_json), json.loads(grammar_json)):
        entry = prompt_glossary["glossary"][0]
        assert "plural" not in entry
        assert "Godfreys" not in json.dumps(prompt_glossary)
        assert "Jesters" not in json.dumps(prompt_glossary)


def test_grammar_prompt_limits_readability_corrections_and_protects_glossary():
    transcript = parse_transcript_json(
        """
        [
          {"id": 1, "speaker": "Eric", "start": 0.0, "end": 1.0, "text": "then lyra went their"}
        ]
        """
    )
    glossary = parse_glossary_yaml(
        """
        glossary:
          - name: "Lyra"
            category: npc
            summary: "Lyra is a hostile NPC."
        """
    )
    section = chunk_transcript(transcript, max_section_tokens=16000)[0]

    messages = build_grammar_correction_messages(section, glossary)
    prompt_text = "\n".join(message["content"] for message in messages)

    assert "capitalization" in prompt_text
    assert "commas, periods, em dashes, and ellipses" in prompt_text
    assert "homophone fixes" in prompt_text
    assert "spelling fixes" in prompt_text
    assert "Do not paraphrase" in prompt_text
    assert "glossary names and aliases as protected spellings" in prompt_text
    assert "correct clear transcription or spelling errors toward glossary names or aliases" in prompt_text
    assert "Do not autocorrect, Anglicize, replace, normalize, lowercase" in prompt_text
    assert "that already appear correctly in the transcript" in prompt_text
    assert "Preserve canonical glossary capitalization" in prompt_text
    assert "appears exactly once" in prompt_text
    assert "Do not return speaker, start, or end fields" in prompt_text


def test_grammar_prompt_uses_simplified_segment_payload():
    transcript = parse_transcript_json(
        """
        [
          {"id": 1, "speaker": "Eric", "start": 0.0, "end": 1.0, "text": "then lyra went their"}
        ]
        """
    )
    glossary = parse_glossary_yaml(
        """
        glossary:
          - name: "Lyra"
            category: npc
            summary: "Lyra is a hostile NPC."
        """
    )
    section = chunk_transcript(transcript, max_section_tokens=16000)[0]

    messages = build_grammar_correction_messages(section, glossary)
    transcript_json = messages[1]["content"].split("Transcript section:\n", maxsplit=1)[1]
    prompt_segments = json.loads(transcript_json)

    assert prompt_segments == [{"id": 1, "original_text": "then lyra went their"}]
    assert "speaker" not in prompt_segments[0]
    assert "start" not in prompt_segments[0]
    assert "end" not in prompt_segments[0]


def test_grammar_validation_prompt_rejects_semantic_changes():
    messages = build_grammar_validation_messages(
        [
            {
                "correction_index": 0,
                "id": 1,
                "original_segment_text": "He became visible.",
                "corrected_segment_text": "He became invisible.",
                "original_text": "visible",
                "corrected_text": "invisible",
            }
        ]
    )
    prompt_text = "\n".join(message["content"] for message in messages)

    assert "preserves meaning" in prompt_text
    assert "became visible" in prompt_text
    assert "became invisible" in prompt_text
    assert "reverses the meaning" in prompt_text
    assert "do not try to rescue likely homophone or transcription fixes" in prompt_text
    assert "handled in a separate spoken-form validation step" in prompt_text
    assert "correction_index" in prompt_text
    assert "is_meaning_preserving" in prompt_text


def test_grammar_spoken_form_validation_prompt_allows_homophone_rescue():
    messages = build_grammar_spoken_form_validation_messages(
        [
            {
                "correction_index": 0,
                "id": 1,
                "original_segment_text": "ChatGPT still can't really do that with a dam.",
                "corrected_segment_text": "ChatGPT still can't really do that with a damn.",
                "original_text": "dam",
                "corrected_text": "damn",
            }
        ]
    )
    prompt_text = "\n".join(message["content"] for message in messages)

    assert "likely homophone, spoken-form, or transcription fix" in prompt_text
    assert '"dam" to "damn"' in prompt_text
    assert '"became visible" to "became invisible"' in prompt_text
    assert "is_likely_spoken_form_correction" in prompt_text