import pytest
from unittest.mock import patch, MagicMock

# Import the module to be tested
import ai_client

@pytest.fixture(autouse=True)
def reset_ai_client_session():
    """Fixture to automatically reset the ai_client session before each test."""
    ai_client.reset_session()

def test_anthropic_history_bleed_calculation():
    """
    Tests that get_history_bleed_stats calculates the token usage
    percentage correctly for the Anthropic provider.
    """
    # 1. Set up the test environment
    ai_client.set_provider("anthropic", "claude-3-opus-20240229")

    # Define the mock return value for the token estimator
    mock_token_count = 150_000
    # The hardcoded limit in the module is 180_000
    expected_percentage = (mock_token_count / 180_000) * 100

    # 2. Mock the internal dependencies
    # We patch _estimate_prompt_tokens as it's the core of the calculation for anthropic
    with patch('ai_client._estimate_prompt_tokens', return_value=mock_token_count) as mock_estimator:
        
        # 3. Call the function under test (which doesn't exist yet)
        stats = ai_client.get_history_bleed_stats()

        # 4. Assert the results
        assert stats["provider"] == "anthropic"
        assert stats["limit"] == 180_000
        assert stats["current"] == mock_token_count
        assert stats["percentage"] == pytest.approx(expected_percentage)
        
        # Ensure the mock was called
        mock_estimator.assert_called_once()

def test_gemini_history_bleed_not_implemented():
    """
    Tests that get_history_bleed_stats returns a 'not implemented' state
    for Gemini, as its token calculation is different.
    """
    # 1. Set up the test environment
    ai_client.set_provider("gemini", "gemini-1.5-pro-latest")

    # 2. Call the function
    stats = ai_client.get_history_bleed_stats()

    # 3. Assert the 'not implemented' state
    assert stats["provider"] == "gemini"
    assert stats["limit"] == 900_000 # The constant _GEMINI_MAX_INPUT_TOKENS
    assert stats["current"] == 0
    assert stats["percentage"] == 0