# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import asyncio
import time

from google.adk.agents import Agent
from google.adk.agents import LiveRequestQueue
from google.adk.agents.invocation_context import RealtimeCacheEntry
from google.adk.agents.run_config import RunConfig
from google.adk.events.event import Event
from google.adk.models import LlmResponse
from google.genai import types
import pytest

from .. import testing_utils


def test_audio_caching_direct():
  """Test audio caching logic directly without full live streaming."""
  # This test directly verifies that our audio caching logic works
  audio_data = b'\x00\xFF\x01\x02\x03\x04\x05\x06'
  audio_mime_type = 'audio/pcm'

  # Create mock responses for successful completion
  responses = [
      LlmResponse(
          content=types.Content(
              role='model',
              parts=[types.Part.from_text(text='Processing audio...')],
          ),
          turn_complete=False,
      ),
      LlmResponse(turn_complete=True),  # This should trigger flush
  ]

  mock_model = testing_utils.MockModel.create(responses)
  mock_model.model = 'gemini-2.0-flash-exp'  # For CFC support

  root_agent = Agent(
      name='test_agent',
      model=mock_model,
      tools=[],
  )

  # Test our implementation by directly calling it
  async def test_caching():
    # Create context similar to what would be created in real scenario
    invocation_context = await testing_utils.create_invocation_context(
        root_agent, run_config=RunConfig(support_cfc=True)
    )

    # Import our caching classes
    from google.adk.agents.invocation_context import RealtimeCacheEntry
    from google.adk.flows.llm_flows.base_llm_flow import BaseLlmFlow

    # Create a mock flow to test our methods
    flow = BaseLlmFlow()

    # Test adding audio to cache
    invocation_context.input_realtime_cache = []
    audio_entry = RealtimeCacheEntry(
        role='user',
        data=types.Blob(data=audio_data, mime_type=audio_mime_type),
        timestamp=1234567890.0,
    )
    invocation_context.input_realtime_cache.append(audio_entry)

    # Verify cache has data
    assert len(invocation_context.input_realtime_cache) == 1
    assert invocation_context.input_realtime_cache[0].data.data == audio_data

    # Test flushing cache
    await flow._handle_control_event_flush(invocation_context, responses[-1])

    # Verify cache was cleared
    assert len(invocation_context.input_realtime_cache) == 0

    # Check if artifacts were created
    artifact_keys = (
        await invocation_context.artifact_service.list_artifact_keys(
            app_name=invocation_context.app_name,
            user_id=invocation_context.user_id,
            session_id=invocation_context.session.id,
        )
    )

    # Should have at least one audio artifact
    audio_artifacts = [key for key in artifact_keys if 'audio' in key.lower()]
    assert (
        len(audio_artifacts) > 0
    ), f'Expected audio artifacts, found: {artifact_keys}'

    # Verify artifact content
    if audio_artifacts:
      artifact = await invocation_context.artifact_service.load_artifact(
          app_name=invocation_context.app_name,
          user_id=invocation_context.user_id,
          session_id=invocation_context.session.id,
          filename=audio_artifacts[0],
      )
      assert artifact.inline_data.data == audio_data

    return True

  # Run the async test
  result = asyncio.run(test_caching())
  assert result is True


def test_transcription_handling():
  """Test that transcriptions are properly handled and saved to session service."""

  # Create mock responses with transcriptions
  input_transcription = types.Transcription(
      text='Hello, this is transcribed input', finished=True
  )
  output_transcription = types.Transcription(
      text='This is transcribed output', finished=True
  )

  responses = [
      LlmResponse(
          content=types.Content(
              role='model', parts=[types.Part.from_text(text='Processing...')]
          ),
          turn_complete=False,
      ),
      LlmResponse(input_transcription=input_transcription, turn_complete=False),
      LlmResponse(
          output_transcription=output_transcription, turn_complete=False
      ),
      LlmResponse(turn_complete=True),
  ]

  mock_model = testing_utils.MockModel.create(responses)
  mock_model.model = 'gemini-2.0-flash-exp'

  root_agent = Agent(
      name='test_agent',
      model=mock_model,
      tools=[],
  )

  async def test_transcription():
    # Create context
    invocation_context = await testing_utils.create_invocation_context(
        root_agent, run_config=RunConfig(support_cfc=True)
    )

    from google.adk.events.event import Event
    from google.adk.flows.llm_flows.base_llm_flow import BaseLlmFlow

    flow = BaseLlmFlow()

    # Test processing transcription events
    session_events_before = len(invocation_context.session.events)

    # Simulate input transcription event
    input_event = Event(
        id=Event.new_id(),
        invocation_id=invocation_context.invocation_id,
        author='user',
        input_transcription=input_transcription,
    )

    # Simulate output transcription event
    output_event = Event(
        id=Event.new_id(),
        invocation_id=invocation_context.invocation_id,
        author=invocation_context.agent.name,
        output_transcription=output_transcription,
    )

    # Save transcription events to session
    await invocation_context.session_service.append_event(
        invocation_context.session, input_event
    )
    await invocation_context.session_service.append_event(
        invocation_context.session, output_event
    )

    # Verify transcriptions were saved to session
    session_events_after = len(invocation_context.session.events)
    assert session_events_after == session_events_before + 2

    # Check that transcription events were saved
    transcription_events = [
        event
        for event in invocation_context.session.events
        if hasattr(event, 'input_transcription')
        and event.input_transcription
        or hasattr(event, 'output_transcription')
        and event.output_transcription
    ]
    assert len(transcription_events) >= 2

    # Verify input transcription
    input_transcription_events = [
        event
        for event in invocation_context.session.events
        if hasattr(event, 'input_transcription') and event.input_transcription
    ]
    assert len(input_transcription_events) >= 1
    assert (
        input_transcription_events[0].input_transcription.text
        == 'Hello, this is transcribed input'
    )
    assert input_transcription_events[0].author == 'user'

    # Verify output transcription
    output_transcription_events = [
        event
        for event in invocation_context.session.events
        if hasattr(event, 'output_transcription') and event.output_transcription
    ]
    assert len(output_transcription_events) >= 1
    assert (
        output_transcription_events[0].output_transcription.text
        == 'This is transcribed output'
    )
    assert (
        output_transcription_events[0].author == invocation_context.agent.name
    )

    return True

  # Run the async test
  result = asyncio.run(test_transcription())
  assert result is True