# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
# Also available under a BSD-style license. See LICENSE.

import argparse
import re
import sys

from torch_mlir_e2e_test.framework import run_tests
from torch_mlir_e2e_test.reporting import report_results
from torch_mlir_e2e_test.registry import GLOBAL_TEST_REGISTRY


# Available test configs.
from torch_mlir_e2e_test.configs import (
    LazyTensorCoreTestConfig,
    LinalgOnTensorsBackendTestConfig,
    StablehloBackendTestConfig,
    NativeTorchTestConfig,
    TorchScriptTestConfig,
    TosaBackendTestConfig,
    TorchDynamoTestConfig,
)

from torch_mlir_e2e_test.linalg_on_tensors_backends.refbackend import RefBackendLinalgOnTensorsBackend
from torch_mlir_e2e_test.stablehlo_backends.linalg_on_tensors import LinalgOnTensorsStablehloBackend
from torch_mlir_e2e_test.tosa_backends.linalg_on_tensors import LinalgOnTensorsTosaBackend

from .xfail_sets import (
    LINALG_XFAIL_SET,
    STABLEHLO_PASS_SET,
    TOSA_PASS_SET,
    LTC_XFAIL_SET,
    TORCHDYNAMO_XFAIL_SET,
    TORCHDYNAMO_CRASHING_SET
)

# Import tests to register them in the global registry.
from torch_mlir_e2e_test.test_suite import register_all_tests
register_all_tests()

def _get_argparse():
    config_choices = ["native_torch", "torchscript", "linalg", "stablehlo", "tosa", "lazy_tensor_core", "torchdynamo"]
    parser = argparse.ArgumentParser(description="Run torchscript e2e tests.")
    parser.add_argument("-c", "--config",
        choices=config_choices,
        default="linalg",
        help=f"""
Meaning of options:
"linalg": run through torch-mlir"s default Linalg-on-Tensors backend.
"stablehlo": run through torch-mlir"s default StableHLO backend.
"tosa": run through torch-mlir"s default TOSA backend.
"native_torch": run the torch.nn.Module as-is without compiling (useful for verifying model is deterministic; ALL tests should pass in this configuration).
"torchscript": compile the model to a torch.jit.ScriptModule, and then run that as-is (useful for verifying TorchScript is modeling the program correctly).
"lazy_tensor_core": run the model through the Lazy Tensor Core frontend and execute the traced graph.
"torchdynamo": run the model through the TorchDynamo frontend and execute the graph using Linalg-on-Tensors.
""")
    parser.add_argument("-f", "--filter", default=".*", help="""
Regular expression specifying which tests to include in this run.
""")
    parser.add_argument("-v", "--verbose",
                        default=False,
                        action="store_true",
                        help="report test results with additional detail")
    parser.add_argument("-s", "--sequential",
                        default=False,
                        action="store_true",
                        help="""Run tests sequentially rather than in parallel.
This can be useful for debugging, since it runs the tests in the same process,
which make it easier to attach a debugger or get a stack trace.""")
    parser.add_argument("--crashing_tests_to_not_attempt_to_run_and_a_bug_is_filed",
                        metavar="TEST", type=str, nargs="+",
                        help="A set of tests to not attempt to run, since they crash and cannot be XFAILed.")
    return parser

def main():
    args = _get_argparse().parse_args()

    all_test_unique_names = set(
        test.unique_name for test in GLOBAL_TEST_REGISTRY)

    # Find the selected config.
    if args.config == "linalg":
        config = LinalgOnTensorsBackendTestConfig(RefBackendLinalgOnTensorsBackend())
        xfail_set = LINALG_XFAIL_SET
        crashing_set = set()
    elif args.config == "tosa":
        config = TosaBackendTestConfig(LinalgOnTensorsTosaBackend())
        xfail_set = all_test_unique_names - TOSA_PASS_SET
        crashing_set = set()
    elif args.config == "stablehlo":
        config = StablehloBackendTestConfig(LinalgOnTensorsStablehloBackend())
        xfail_set = all_test_unique_names - STABLEHLO_PASS_SET
        crashing_set = set()
    elif args.config == "native_torch":
        config = NativeTorchTestConfig()
        xfail_set = set()
        crashing_set = set()
    elif args.config == "torchscript":
        config = TorchScriptTestConfig()
        xfail_set = set()
        crashing_set = set()
    elif args.config == "lazy_tensor_core":
        config = LazyTensorCoreTestConfig()
        xfail_set = LTC_XFAIL_SET
        crashing_set = set()
    elif args.config == "torchdynamo":
        config = TorchDynamoTestConfig(RefBackendLinalgOnTensorsBackend())
        xfail_set = TORCHDYNAMO_XFAIL_SET
        crashing_set = TORCHDYNAMO_CRASHING_SET

    do_not_attempt = set(args.crashing_tests_to_not_attempt_to_run_and_a_bug_is_filed or []).union(crashing_set)
    available_tests = [test for test in GLOBAL_TEST_REGISTRY if test.unique_name not in do_not_attempt]
    if args.crashing_tests_to_not_attempt_to_run_and_a_bug_is_filed is not None:
        for arg in args.crashing_tests_to_not_attempt_to_run_and_a_bug_is_filed:
            if arg not in all_test_unique_names:
                print(f"ERROR: --crashing_tests_to_not_attempt_to_run_and_a_bug_is_filed argument '{arg}' is not a valid test name")
                sys.exit(1)

    # Find the selected tests, and emit a diagnostic if none are found.
    tests = [
        test for test in available_tests
        if re.match(args.filter, test.unique_name)
    ]
    if len(tests) == 0:
        print(
            f"ERROR: the provided filter {args.filter!r} does not match any tests"
        )
        print("The available tests are:")
        for test in available_tests:
            print(test.unique_name)
        sys.exit(1)

    # Run the tests.
    results = run_tests(tests, config, args.sequential, args.verbose)

    # Report the test results.
    failed = report_results(results, xfail_set, args.verbose)
    sys.exit(1 if failed else 0)

def _suppress_warnings():
    import warnings
    # Ignore warning due to Python bug:
    # https://stackoverflow.com/questions/4964101/pep-3118-warning-when-using-ctypes-array-as-numpy-array
    warnings.filterwarnings("ignore",
                            message="A builtin ctypes object gave a PEP3118 format string that does not match its itemsize")

if __name__ == "__main__":
    _suppress_warnings()
    main()