import os

# issue#7378 may happen with custom opencv. It doesn't hurt to disable opencl
os.environ['OPENCV_OPENCL_RUNTIME'] = 'disabled'     # https://github.com/opencv/opencv/pull/10155
try:
    # issue#1924 may happen on old systems
    import cv2  # noqa
    # cv2.setNumThreads(0)
    if int(cv2.__version__.split('.')[0]) >= 3:
        cv2.ocl.setUseOpenCL(False)
    # check if cv is built with cuda or openmp
    info = cv2.getBuildInformation().split('\n')
    for line in info:
        splits = line.split()
        if not len(splits):
            continue
        answer = splits[-1].lower()
        if answer in ['yes', 'no']:
            if 'cuda' in line.lower() and answer == 'yes':
                # issue#1197
                print("OpenCV is built with CUDA support. "
                      "This may cause slow initialization or sometimes segfault with TensorFlow.")
        if answer == 'openmp':
            print("OpenCV is built with OpenMP support. This usually results in poor performance. For details, see "
                  "https://github.com/tensorpack/benchmarks/blob/master/ImageNet/benchmark-opencv-resize.py")
except (ImportError, TypeError):
    pass

os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'  # issue#9339
os.environ['TF_AUTOTUNE_THRESHOLD'] = '2'   # use more warm-up

# Since 1.3, this is not needed
os.environ['TF_AVGPOOL_USE_CUDNN'] = '1'   # issue#8566

# TF1.5 features
os.environ['TF_SYNC_ON_FINISH'] = '0'   # will become default
os.environ['TF_GPU_THREAD_MODE'] = 'gpu_private'
os.environ['TF_GPU_THREAD_COUNT'] = '2'

# Available in TF1.6+ & cudnn7. Haven't seen different performance on R50.
# NOTE we disable it because:
# this mode may use scaled atomic integer reduction that may cause a numerical
# overflow for certain input data range.
os.environ['TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT'] = '0'

# Available since 1.12. issue#15874
# But they're sometimes buggy. We leave this decision to users.
# os.environ['TF_ENABLE_WHILE_V2'] = '1'
# os.environ['TF_ENABLE_COND_V2'] = '1'

try:
    import tensorflow as tf  # noqa
    _version = tf.__version__.split('.')
    assert (int(_version[0]), int(_version[1])) >= (1, 3), "TF>=1.3 is required!"
    _HAS_TF = True
except ImportError:
    print("Failed to import tensorflow.")
    _HAS_TF = False
else:
    # Install stacktrace handler
    try:
        from tensorflow.python.framework import test_util
        test_util.InstallStackTraceHandler()
    except Exception:
        pass

    # silence the massive deprecation warnings in TF 1.13+
    if (int(_version[0]), int(_version[1])) >= (1, 13):
        try:
            from tensorflow.python.util.deprecation import silence
        except Exception:
            pass
        else:
            silence().__enter__()
        try:
            from tensorflow.python.util import deprecation_wrapper
            deprecation_wrapper._PER_MODULE_WARNING_LIMIT = 0
        except Exception:
            pass

    # Monkey-patch tf.test.is_gpu_available to avoid side effects:
    # https://github.com/tensorflow/tensorflow/issues/26460
    try:
        list_dev = tf.config.experimental.list_physical_devices
    except AttributeError:
        pass
    else:
        old_is_gpu_available = tf.test.is_gpu_available

        def is_gpu_available(*args, **kwargs):
            if len(args) == 0 and len(kwargs) == 0:
                return len(list_dev('GPU')) > 0
            return old_is_gpu_available(*args, **kwargs)

        tf.test.is_gpu_available = is_gpu_available


# These lines will be programatically read/write by setup.py
# Don't touch them.
__version__ = '0.11'
__git_version__ = "v0.0.0-175-g59255cc-dirty"