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

# Test cases format: (input_shape, input_strides_or_None, boundaries_len)
_TEST_CASES_DATA = [
    ((5,), None, 3),
    ((4, 3), None, 4),
    ((2,), None, 5),
    ((6,), None, 2),
    ((3, 3), None, 6),
    ((1,), None, 1),
]

_TOLERANCE_MAP = {infinicore.int64: {"atol": 0, "rtol": 0}}
_TENSOR_DTYPES = [infinicore.float32]


def parse_test_cases():
    test_cases = []
    for shape, strides, b_len in _TEST_CASES_DATA:
        input_spec = TensorSpec.from_tensor(shape, strides, infinicore.float32)
        boundaries_spec = TensorSpec.from_tensor((b_len,), None, infinicore.float32)

        test_cases.append(
            TestCase(
                inputs=[input_spec, boundaries_spec],
                kwargs={},
                output_spec=None,
                comparison_target=None,
                tolerance=_TOLERANCE_MAP[infinicore.int64],
                description="bucketize - OUT_OF_PLACE",
            )
        )

    return test_cases


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

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

    def get_test_cases(self):
        return parse_test_cases()

    def torch_operator(self, *args, **kwargs):
        return torch.bucketize(*args, **kwargs)

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


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


if __name__ == "__main__":
    main()
