#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

import pytest
from openai.types.audio import Transcription

from pipecat.frames.frames import (
    InputAudioRawFrame,
    MetricsFrame,
    TranscriptionFrame,
    VADUserStartedSpeakingFrame,
    VADUserStoppedSpeakingFrame,
)
from pipecat.metrics.metrics import STTUsageMetricsData
from pipecat.pipeline.worker import PipelineParams
from pipecat.services.openai.stt import OpenAIRealtimeSTTService, OpenAISTTService
from pipecat.tests.utils import run_test
from pipecat.turns.user_turn_strategies import ExternalUserTurnStrategies

SAMPLE_RATE = 16000


@pytest.mark.asyncio
async def test_segment_emits_usage_and_transcription(monkeypatch):
    service = OpenAISTTService(api_key="test-key")

    async def fake_transcribe(audio: bytes) -> Transcription:
        return Transcription(text="hello world")

    monkeypatch.setattr(service, "_transcribe", fake_transcribe)

    pcm = b"\x01\x02" * SAMPLE_RATE  # 1s of 16-bit mono audio
    received_down, _ = await run_test(
        service,
        frames_to_send=[
            VADUserStartedSpeakingFrame(),
            InputAudioRawFrame(audio=pcm, sample_rate=SAMPLE_RATE, num_channels=1),
            VADUserStoppedSpeakingFrame(),
        ],
        pipeline_params=PipelineParams(enable_usage_metrics=True),
    )

    transcripts = [f for f in received_down if isinstance(f, TranscriptionFrame)]
    assert len(transcripts) == 1
    assert transcripts[0].text == "hello world"
    assert transcripts[0].finalized is True

    usage_indexes = [
        i
        for i, f in enumerate(received_down)
        if isinstance(f, MetricsFrame) and any(isinstance(d, STTUsageMetricsData) for d in f.data)
    ]
    assert len(usage_indexes) == 1
    usage_frame = received_down[usage_indexes[0]]
    usage = next(d for d in usage_frame.data if isinstance(d, STTUsageMetricsData))
    assert usage.value.audio_seconds == pytest.approx(len(pcm) / (SAMPLE_RATE * 2))

    # Usage precedes the transcript so tracing attaches it to the span the
    # finalized TranscriptionFrame closes.
    assert usage_indexes[0] < received_down.index(transcripts[0])


def test_openai_realtime_should_interrupt_rides_on_recommended_strategies():
    # should_interrupt configures the strategies the service recommends via its
    # metadata frame; the service never broadcasts the interruption itself.
    for should_interrupt in (True, False):
        service = OpenAIRealtimeSTTService(
            api_key="test-key",
            turn_detection={"type": "server_vad"},
            should_interrupt=should_interrupt,
        )
        strategies = service.service_metadata_frame().user_turn_strategies
        assert isinstance(strategies, ExternalUserTurnStrategies)
        assert strategies.enable_interruptions is should_interrupt


def test_openai_realtime_server_defaults_recommend_strategies():
    """``turn_detection=None`` omits the field, so the session's own default stands.

    That default detects turns, so the recommendation applies just as it does
    for an explicit configuration.
    """
    service = OpenAIRealtimeSTTService(api_key="test-key", turn_detection=None)
    strategies = service.service_metadata_frame().user_turn_strategies
    assert isinstance(strategies, ExternalUserTurnStrategies)


def test_openai_realtime_local_vad_mode_recommends_no_strategies():
    """With turn detection off the server reports no boundaries to propose."""
    service = OpenAIRealtimeSTTService(api_key="test-key")
    assert service.service_metadata_frame().user_turn_strategies is None