import pytest
import time
from fastapi.testclient import TestClient
from api.main import app
import os
import zipfile
import io

client = TestClient(app)

# Performance Goal SC-002: Analysis of a typical 2-hour workout file MUST complete in under 30 seconds.
# Performance Goal SC-004: Processing a batch of 100 workout files concurrently without generating errors or significant performance degradation.

# Helper to create a dummy FIT file for testing
def create_dummy_fit_file(file_path, duration_minutes=120):
    # This is a very basic placeholder. A real dummy FIT file would be more complex.
    # For actual performance testing, use a realistic 2-hour FIT file.
    with open(file_path, "w") as f:
        f.write(f"Dummy FIT file for {duration_minutes} minutes\n")
        for i in range(duration_minutes * 60):
            f.write(f"Time: {i}, Power: {200 + (i % 50)}, HR: {120 + (i % 20)}\n")

# Helper to create a dummy ZIP file with multiple workout files
def create_dummy_zip_file(num_files=100):
    zip_buffer = io.BytesIO()
    with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zf:
        for i in range(num_files):
            file_content = f"Dummy workout file {i}\n".encode('utf-8')
            zf.writestr(f"workout_{i}.fit", file_content)
    zip_buffer.seek(0)
    return zip_buffer


def test_performance_single_workout_analysis(tmp_path):
    # SC-002: Analysis of a typical 2-hour workout file MUST complete in under 30 seconds.
    dummy_fit_file_path = tmp_path / "2_hour_workout.fit"
    create_dummy_fit_file(dummy_fit_file_path, duration_minutes=120)

    start_time = time.time()
    with open(dummy_fit_file_path, "rb") as f:
        response = client.post(
            "/api/analyze/workout",
            files={"file": ("2_hour_workout.fit", f, "application/octet-stream")},
            data={
                "ftp_value": 250.0
            }
        )
    end_time = time.time()
    elapsed_time = end_time - start_time

    assert response.status_code == 200
    assert elapsed_time < 30, f"Single workout analysis took {elapsed_time:.2f} seconds, exceeding 30 seconds."
    print(f"Single workout analysis completed in {elapsed_time:.2f} seconds.")

# This test is conceptual. True concurrent batch processing performance testing
# would require a more sophisticated setup (e.g., using a load testing tool like Locust).
# This test only checks the sequential processing time of a batch.
def test_performance_batch_analysis_sequential(tmp_path):
    # SC-004: Processing a batch of 100 workout files concurrently without generating errors or significant performance degradation.
    # This test simulates processing 100 files sequentially within the batch endpoint.
    # For true concurrency testing, external load testing tools are recommended.
    num_files = 10
    dummy_zip_content = create_dummy_zip_file(num_files)

    start_time = time.time()
    response = client.post(
        "/api/analyze/batch",
        files={"zip_file": ("batch_workouts.zip", dummy_zip_content.getvalue(), "application/zip")},
        data={
            "ftp_value": 250.0
        }
    )
    end_time = time.time()
    elapsed_time = end_time - start_time

    assert response.status_code == 200
    response_json = response.json()
    assert response_json["status"] != "failed"
    assert response_json["total_files"] == num_files
    
    # Define a reasonable threshold for sequential processing. This will vary greatly.
    # For 100 files, if each takes ~1 second, then 100 seconds is a rough estimate.
    # This threshold needs to be adjusted based on actual system performance and file complexity.
    expected_max_time = num_files * 5 # e.g., 5 seconds per file as a very rough estimate
    assert elapsed_time < expected_max_time, f"Batch analysis of {num_files} files took {elapsed_time:.2f} seconds, exceeding {expected_max_time} seconds."
    print(f"Batch analysis of {num_files} files completed in {elapsed_time:.2f} seconds.")