#!/usr/bin/env python3
# MIT License
#
# Copyright (c) 2023-2025 Advanced Micro Devices, Inc. All rights reserved.
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import sys
import pytest
import numpy as np
import pandas as pd


# =========================== Validating fields common for both host-trap and stochastic CSV output


def test_validate_pc_sampling_exec_mask_manipulation_csv(
    input_csv: pd.DataFrame, all_sampled: bool
):
    from rocprofiler_sdk.pc_sampling.exec_mask_manipulation.csv import (
        exec_mask_manipulation_validate_csv,
    )

    exec_mask_manipulation_validate_csv(input_csv, all_sampled=all_sampled)


# # ========================= Validating fields common for both host-trap and stochastic JSON output


def test_validate_pc_sampling_exec_mask_manipulation_json(
    input_json, input_csv: pd.DataFrame, all_sampled: bool
):
    data = input_json["rocprofiler-sdk-tool"]
    # The same amount of samples should be in both CSV and JSON files.
    assert len(input_csv) == len(data["buffer_records"]["pc_sample_stochastic"])
    # # validating JSON output
    from rocprofiler_sdk.pc_sampling.exec_mask_manipulation.json import (
        validate_json_exec_mask_manipulation,
    )

    validate_json_exec_mask_manipulation(
        data, pc_sampling_method="stochastic", all_sampled=all_sampled
    )


# ======================== Validating fields specific for stochastic sampling


def test_validate_pc_sampling_stochastic_specific_csv(input_csv: pd.DataFrame):
    from rocprofiler_sdk.pc_sampling.stochastic.csv.gfx9 import (
        validate_stochastic_samples_csv,
    )

    validate_stochastic_samples_csv(input_csv)


def test_validate_pc_sampling_stochastic_specific_json(input_json):
    from rocprofiler_sdk.pc_sampling.stochastic.json.gfx9 import (
        validate_stochastic_samples_json,
    )

    validate_stochastic_samples_json(input_json["rocprofiler-sdk-tool"])


if __name__ == "__main__":
    exit_code = pytest.main(["-x", __file__] + sys.argv[1:])
    sys.exit(exit_code)