from .quantize import *  # noqa: F403
from .observer import *  # noqa: F403
from .qconfig import *  # noqa: F403
from .fake_quantize import *  # noqa: F403
from .fuse_modules import fuse_modules
from .stubs import *  # noqa: F403
from .quant_type import *  # noqa: F403
from .quantize_jit import *  # noqa: F403

# from .quantize_fx import *
from .quantization_mappings import *  # noqa: F403
from .fuser_method_mappings import *  # noqa: F403


def default_eval_fn(model, calib_data):
    r"""
    Default evaluation function takes a torch.utils.data.Dataset or a list of
    input Tensors and run the model on the dataset
    """
    for data, target in calib_data:
        model(data)


__all__ = [
    "QuantWrapper",
    "QuantStub",
    "DeQuantStub",
    # Top level API for eager mode quantization
    "quantize",
    "quantize_dynamic",
    "quantize_qat",
    "prepare",
    "convert",
    "prepare_qat",
    # Top level API for graph mode quantization on TorchScript
    "quantize_jit",
    "quantize_dynamic_jit",
    "_prepare_ondevice_dynamic_jit",
    "_convert_ondevice_dynamic_jit",
    "_quantize_ondevice_dynamic_jit",
    # Top level API for graph mode quantization on GraphModule(torch.fx)
    # 'fuse_fx', 'quantize_fx',  # TODO: add quantize_dynamic_fx
    # 'prepare_fx', 'prepare_dynamic_fx', 'convert_fx',
    "QuantType",  # quantization type
    # custom module APIs
    "get_default_static_quant_module_mappings",
    "get_static_quant_module_class",
    "get_default_dynamic_quant_module_mappings",
    "get_default_qat_module_mappings",
    "get_default_qconfig_propagation_list",
    "get_default_compare_output_module_list",
    "get_quantized_operator",
    "get_fuser_method",
    # Sub functions for `prepare` and `swap_module`
    "propagate_qconfig_",
    "add_quant_dequant",
    "swap_module",
    "default_eval_fn",
    # Observers
    "ObserverBase",
    "WeightObserver",
    "HistogramObserver",
    "observer",
    "default_observer",
    "default_weight_observer",
    "default_placeholder_observer",
    "default_per_channel_weight_observer",
    # FakeQuantize (for qat)
    "default_fake_quant",
    "default_weight_fake_quant",
    "default_fixed_qparams_range_neg1to1_fake_quant",
    "default_fixed_qparams_range_0to1_fake_quant",
    "default_per_channel_weight_fake_quant",
    "default_histogram_fake_quant",
    # QConfig
    "QConfig",
    "default_qconfig",
    "default_dynamic_qconfig",
    "float16_dynamic_qconfig",
    "float_qparams_weight_only_qconfig",
    # QAT utilities
    "default_qat_qconfig",
    "prepare_qat",
    "quantize_qat",
    # module transformations
    "fuse_modules",
]
