import sys
import os

sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))

import infinicore
import torch
from framework import (
    BaseOperatorTest,
    TensorSpec,
    TestCase,
    GenericTestRunner,
    is_broadcast,
)

# Test cases format: (shape, size_or_scale_factor, align_corners_or_None, input_strides_or_None)
# infinicore.nn.functional.upsample_bilinear is deprecated in favor of interpolate(mode='bilinear')

_TEST_CASES_DATA = [
    ((1, 3, 16, 16), (32, 32), True, None),
    ((2, 3, 8, 8), (16, 16), None, (384, 128, 16, 1)),
    ((1, 1, 10, 10), (20, 20), False, None),
    ((2, 3, 6, 6), (12, 12), None, None),
    ((4, 3, 7, 7), 2.0, False, None),
    ((3, 3, 5, 5), (10, 10), True, None),
]

_TOLERANCE_MAP = {
    infinicore.float16: {"atol": 1e-2, "rtol": 1e-2},
    infinicore.float32: {"atol": 1e-5, "rtol": 1e-4},
    infinicore.bfloat16: {"atol": 1e-2, "rtol": 5e-2},
}

_TENSOR_DTYPES = [infinicore.float16, infinicore.bfloat16, infinicore.float32]


def parse_test_cases():
    test_cases = []
    for data in _TEST_CASES_DATA:
        shape, size_or_scale, align, in_strides = data

        for dtype in _TENSOR_DTYPES:
            tol = _TOLERANCE_MAP.get(dtype, {"atol": 1e-5, "rtol": 1e-4})
            in_spec = TensorSpec.from_tensor(shape, in_strides, dtype)

            kwargs = {"mode": "bilinear"}
            if isinstance(size_or_scale, tuple):
                kwargs["size"] = size_or_scale
            else:
                kwargs["scale_factor"] = size_or_scale
            if align is not None:
                kwargs["align_corners"] = align

            test_cases.append(
                TestCase(
                    inputs=[in_spec],
                    kwargs=kwargs,
                    output_spec=None,
                    comparison_target=None,
                    tolerance=tol,
                    description=f"upsample_bilinear - OUT_OF_PLACE",
                )
            )

    return test_cases


class OpTest(BaseOperatorTest):
    """UpsampleBilinear operator test with simplified implementation"""

    def __init__(self):
        super().__init__("UpsampleBilinear")

    def get_test_cases(self):
        return parse_test_cases()

    def torch_operator(self, *args, **kwargs):
        # Use interpolate with bilinear mode
        return torch.nn.functional.interpolate(*args, **kwargs)

    # def infinicore_operator(self, *args, **kwargs):
    #     """InfiniCore implementation (operator not yet available)."""
    #     return infinicore.nn.functional.interpolate(*args, **kwargs)


def main():
    """Main entry point"""
    runner = GenericTestRunner(OpTest)
    runner.run_and_exit()


if __name__ == "__main__":
    main()
