"""Testing utilities and infrastructure for Dynamo.

This module provides a comprehensive set of testing utilities including:
- Test result collection and validation
- Graph manipulation and comparison tools
- Test case management and execution helpers
- Specialized test decorators for different Python versions and features
- RNG state management
- Compilation counting and monitoring
- Debug utilities for bytecode transformation

The utilities in this module are used across Dynamo's test suite to ensure
consistent testing patterns and proper test isolation.
"""

import contextlib
import dis
import functools
import logging
import os.path
import random
import re
import sys
import types
import unittest
from collections.abc import Sequence
from typing import Any, Callable, Optional, overload, TypeVar, Union
from typing_extensions import ParamSpec
from unittest.mock import patch

import torch
from torch import fx
from torch._dynamo.backends.debugging import aot_eager
from torch._dynamo.output_graph import OutputGraph

from . import config, eval_frame, optimize_assert, reset
from .bytecode_transformation import (
    create_instruction,
    debug_checks,
    is_generator,
    transform_code_object,
)
from .guards import CheckFunctionManager, CompileId, GuardedCode
from .types import ConvertFrameReturn, DynamoFrameType, wrap_guarded_code
from .utils import same


np: Optional[types.ModuleType] = None
try:
    import numpy as np
except ModuleNotFoundError:
    np = None


unsupported = eval_frame.unsupported
three = 3

log = logging.getLogger(__name__)

_P = ParamSpec("_P")


def clone_me(x: Optional[torch.Tensor]) -> Optional[torch.Tensor]:
    if x is None:
        return None
    return x.detach().clone().requires_grad_(x.requires_grad)


def remove_optimized_module_prefix(name: str) -> str:
    return re.sub(r"^_orig_mod[.]", "", name)


def extract_graph_and_tracker(fn, *args, **kwargs):  # type: ignore[no-untyped-def]
    from torch._dynamo.symbolic_convert import InstructionTranslator

    gm = None
    region_tracker = None

    def extract_graph_backend(_gm, *args, **kwargs):  # type: ignore[no-untyped-def]
        nonlocal gm
        nonlocal region_tracker
        gm = _gm
        region_tracker = InstructionTranslator.current_tx().output.region_tracker
        return _gm

    torch.compile(backend=extract_graph_backend, fullgraph=True)(fn)(*args, **kwargs)
    return gm.graph, region_tracker  # type: ignore[union-attr]


def collect_results(
    model: torch.nn.Module, prediction: Any, loss: Any, example_inputs: Any
) -> list[Any]:
    results = []
    results.append(prediction)
    results.append(loss)
    # if isinstance(loss, torch.Tensor) and loss.item() > 1:
    #     log.warning(
    #         f"High loss value alert - {loss:.2f}. Can result in unstable gradients."
    #     )

    grads = {}
    params = {}
    for name, param in model.named_parameters():
        if isinstance(model, eval_frame.OptimizedModule):
            name = remove_optimized_module_prefix(name)
        param_copy = param
        grad = param.grad
        # Treat None and zero grad as same
        if param.grad is None:
            grad = torch.zeros_like(param)
        grads[name + ".grad"] = grad
        params[name] = param_copy
    results.append(grads)
    results.append(params)
    buffers = {}
    for name, buffer in model.named_buffers():
        if isinstance(model, eval_frame.OptimizedModule):
            name = remove_optimized_module_prefix(name)
        buffers[name] = buffer
    results.append(buffers)
    for example in example_inputs:
        if isinstance(example, (tuple, list)):
            results.extend(inp.grad for inp in example if isinstance(inp, torch.Tensor))
        else:
            if isinstance(example, torch.Tensor):
                results.append(example.grad)
    return results


def requires_bwd_pass(out: Any) -> bool:
    if isinstance(out, torch.Tensor):
        return out.requires_grad
    elif isinstance(out, (list, tuple)):
        return any(requires_bwd_pass(x) for x in out)
    elif out is None:
        return False
    elif isinstance(out, int):
        return False
    raise NotImplementedError("Don't know how to reduce", type(out))


@overload
def reduce_to_scalar_loss(out: torch.Tensor) -> torch.Tensor: ...


@overload
def reduce_to_scalar_loss(
    out: Union[list[Any], tuple[Any, ...], dict[Any, Any]],
) -> float: ...


def reduce_to_scalar_loss(out: Any) -> Union[torch.Tensor, float]:
    """Reduce the output of a model to get scalar loss"""
    if isinstance(out, torch.Tensor):
        # Mean does not work on integer tensors
        return out.sum() / out.numel()
    elif isinstance(out, (list, tuple)):
        return sum(reduce_to_scalar_loss(x) for x in out) / len(out)
    elif type(out).__name__ in (
        "MaskedLMOutput",
        "Seq2SeqLMOutput",
        "CausalLMOutputWithCrossAttentions",
    ):
        return reduce_to_scalar_loss(out.logits)
    elif type(out).__name__ == "SquashedNormal":
        return out.mean.sum()
    elif isinstance(out, dict):
        return sum(reduce_to_scalar_loss(value) for value in out.values()) / len(
            out.keys()
        )
    raise NotImplementedError("Don't know how to reduce", type(out))


def debug_dir() -> str:
    path = os.path.join(os.path.dirname(__file__), "../debug")
    if not os.path.exists(path):
        os.mkdir(path)
    return path


def debug_dump(name: str, code: types.CodeType, extra: str = "") -> None:
    with open(os.path.join(debug_dir(), name), "w") as fd:
        fd.write(
            f"{dis.Bytecode(code).info()}\n\n{dis.Bytecode(code).dis()}\n\n{extra}\n"
        )


def debug_insert_nops(
    frame: DynamoFrameType, cache_size: int, hooks: Any, _: Any, *, skip: int = 0
) -> ConvertFrameReturn:
    """used to debug jump updates"""

    def insert_nops(instructions: list[Any], code_options: Any) -> None:
        instructions.insert(0, create_instruction("NOP"))
        instructions.insert(0, create_instruction("NOP"))

    metrics_context = torch._dynamo.utils.get_metrics_context()
    with torch._dynamo.utils.dynamo_timed("debug_insert_nops"), metrics_context:
        if is_generator(frame.f_code):
            return ConvertFrameReturn()

        debug_checks(frame.f_code)
        code = transform_code_object(frame.f_code, insert_nops)
        graph = OutputGraph(
            code_options={},
            compiler_fn=None,
            root_tx=None,
            export=False,
            export_constraints=None,
            frame_state={"_id": 0},
            # TODO: shouldn't this be f_locals/f_globals from frame?
            local_scope=locals(),
            global_scope=globals(),
            f_code=frame.f_code,
            torch_function_mode_stack=[],
        )

        return wrap_guarded_code(
            GuardedCode(
                code,
                CheckFunctionManager(frame.f_code, graph).guard_manager,  # type: ignore[arg-type]
                CompileId(frame_id=0, frame_compile_id=0),
            )
        )


class CompileCounter:
    def __init__(self) -> None:
        self.frame_count = 0
        self.op_count = 0

    def __call__(
        self, gm: torch.fx.GraphModule, example_inputs: list[torch.Tensor]
    ) -> Callable[..., Any]:
        self.frame_count += 1
        for node in gm.graph.nodes:
            if "call" in node.op:
                self.op_count += 1
        return gm.forward

    def clear(self) -> None:
        self.frame_count = 0
        self.op_count = 0


class CompileCounterWithBackend:
    def __init__(self, backend: str) -> None:
        self.frame_count = 0
        self.op_count = 0
        self.backend = backend
        self.graphs: list[torch.fx.GraphModule] = []

    def __call__(
        self, gm: torch.fx.GraphModule, example_inputs: list[torch.Tensor]
    ) -> Callable[..., Any]:
        from .backends.registry import lookup_backend

        self.frame_count += 1
        for node in gm.graph.nodes:
            if "call" in node.op:
                self.op_count += 1
        self.graphs.append(gm)
        return lookup_backend(self.backend)(gm, example_inputs)

    def clear(self) -> None:
        self.frame_count = 0
        self.op_count = 0
        self.graphs = []


# Equivalent to backend="eager", but also records graphs that
# we can assert on
class EagerAndRecordGraphs:
    def __init__(self) -> None:
        self.graphs: list[torch.fx.GraphModule] = []

    def __call__(
        self, gm: torch.fx.GraphModule, example_inputs: list[torch.Tensor]
    ) -> Callable[..., Any]:
        self.graphs.append(gm)
        return gm.forward


class AotEagerAndRecordGraphs:
    def __init__(self) -> None:
        self.graphs: list[torch.fx.GraphModule] = []
        self.fw_graphs: list[torch.fx.GraphModule] = []
        self.bw_graphs: list[torch.fx.GraphModule] = []

    def __call__(
        self, gm: torch.fx.GraphModule, example_inputs: list[torch.Tensor]
    ) -> Callable[..., Any]:
        self.graphs.append(gm)

        def fw_compiler(
            gm: torch.fx.GraphModule, example_inputs: list[torch.Tensor]
        ) -> Callable[..., Any]:
            self.fw_graphs.append(gm)
            return gm.forward

        def bw_compiler(
            gm: torch.fx.GraphModule, example_inputs: list[torch.Tensor]
        ) -> Callable[..., Any]:
            self.bw_graphs.append(gm)
            return gm.forward

        return aot_eager(
            gm,
            example_inputs,
            fw_compiler=fw_compiler,
            bw_compiler=bw_compiler,
        )


def strip_comment(code: str) -> str:
    return re.sub(r"(?m)^ *#.*\n?", "", code)


def remove_trailing_space(code: str) -> str:
    return "\n".join([line.rstrip() for line in code.split("\n")])


def normalize_gm(gm_str: str) -> str:
    # strip comments as comments have path to files which may differ from
    # system to system.
    return remove_trailing_space(strip_comment(gm_str))


def empty_line_normalizer(code: str) -> str:
    """
    Normalize code: remove empty lines.
    """
    normal_code = re.sub(r"[\r\n]+", "\n", code)
    return normal_code


def standard_test(
    self: Any,
    fn: Callable[..., Any],
    nargs: int,
    expected_ops: Optional[int] = None,
    expected_ops_dynamic: Optional[int] = None,
    expected_frame_count: int = 1,
) -> None:
    if not config.assume_static_by_default and expected_ops_dynamic is not None:
        expected_ops = expected_ops_dynamic

    actual = CompileCounter()

    args1 = [torch.randn(10, 10) for _ in range(nargs)]
    args2 = [torch.randn(10, 10) for _ in range(nargs)]
    correct1 = fn(*args1)
    correct2 = fn(*args2)
    reset()
    opt_fn = optimize_assert(actual)(fn)
    val1a = opt_fn(*args1)
    val2a = opt_fn(*args2)
    val1b = opt_fn(*args1)
    val2b = opt_fn(*args2)
    reset()
    self.assertTrue(same(val1a, correct1))
    self.assertTrue(same(val1b, correct1))
    self.assertTrue(same(val2a, correct2))
    self.assertTrue(same(val2b, correct2))
    self.assertEqual(actual.frame_count, expected_frame_count)
    if expected_ops is not None:
        self.assertEqual(actual.op_count, expected_ops)


def dummy_fx_compile(
    gm: fx.GraphModule, example_inputs: list[torch.Tensor]
) -> Callable[..., Any]:
    return gm.forward


def format_speedup(
    speedup: float,
    pvalue: float,
    is_correct: bool = True,
    pvalue_threshold: float = 0.1,
) -> str:
    if not is_correct:
        return "ERROR"
    if pvalue > pvalue_threshold:
        return f"{speedup:.3f}x SAME"
    return f"{speedup:.3f}x p={pvalue:.2f}"


def rand_strided(
    size: Sequence[int],
    stride: Sequence[int],
    dtype: torch.dtype = torch.float32,
    device: Union[str, torch.device] = "cpu",
    extra_size: int = 0,
) -> torch.Tensor:
    needed_size = (
        sum((shape - 1) * stride for shape, stride in zip(size, stride))
        + 1
        + extra_size
    )
    if dtype.is_floating_point:
        if dtype.itemsize == 1:
            """
            normal distribution kernel is not implemented for fp8..
            Workaround that by creating a fp16 tensor and then cast.
            """
            buffer = torch.randn(needed_size, dtype=torch.float16, device=device).to(
                dtype=dtype
            )
        else:
            buffer = torch.randn(needed_size, dtype=dtype, device=device)
    else:
        buffer = torch.zeros(size=[needed_size], dtype=dtype, device=device)
    return torch.as_strided(buffer, size, stride)


_T = TypeVar("_T")


def check_dynamic_shape_capture() -> bool:
    # This also mirrors config from `test/dynamo/test_dynamic_shapes.py:make_dynamic_cls`
    return not config.assume_static_by_default


def _make_fn_with_patches(fn: Callable[_P, _T], *patches: Any) -> Callable[_P, _T]:
    @functools.wraps(fn)
    def _fn(*args: _P.args, **kwargs: _P.kwargs) -> _T:
        with contextlib.ExitStack() as stack:
            for module, attr, val in patches:
                stack.enter_context(patch.object(module, attr, val))

            return fn(*args, **kwargs)

    return _fn


def make_test_cls_with_patches(
    cls: type,
    cls_prefix: str,
    fn_suffix: str,
    *patches: Any,
    xfail_prop: Optional[str] = None,
    decorator: Callable[[Callable[..., Any]], Callable[..., Any]] = lambda x: x,
) -> type:
    DummyTestClass = type(f"{cls_prefix}{cls.__name__}", cls.__bases__, {})
    DummyTestClass.__qualname__ = DummyTestClass.__name__

    for name in dir(cls):
        if name.startswith("test_"):
            fn = getattr(cls, name)
            if not callable(fn):
                setattr(DummyTestClass, name, getattr(cls, name))
                continue
            new_name = f"{name}{fn_suffix}"
            new_fn = _make_fn_with_patches(fn, *patches)
            new_fn.__name__ = new_name
            if xfail_prop is not None and hasattr(fn, xfail_prop):
                new_fn = unittest.expectedFailure(new_fn)
            setattr(DummyTestClass, new_name, decorator(new_fn))
        # NB: Doesn't handle slots correctly, but whatever
        elif not hasattr(DummyTestClass, name):
            setattr(DummyTestClass, name, getattr(cls, name))

    return DummyTestClass


# test Python 3.11+ specific features
def skipIfNotPy311(fn: Callable[..., Any]) -> Callable[..., Any]:
    if sys.version_info >= (3, 11):
        return fn
    return unittest.skip(fn)


def skipIfNotPy312(fn: Callable[..., Any]) -> Callable[..., Any]:
    if sys.version_info >= (3, 12):
        return fn
    return unittest.skip("Requires Python 3.12+")(fn)


def xfailIfPy312(fn: Callable[..., Any]) -> Callable[..., Any]:
    if sys.version_info >= (3, 12):
        return unittest.expectedFailure(fn)
    return fn


def skipIfPy312(fn: Callable[..., Any]) -> Callable[..., Any]:
    if sys.version_info >= (3, 12):
        return unittest.skip("Not supported in Python 3.12+")(fn)
    return fn


def requiresPy310(fn: Callable[..., Any]) -> Callable[..., Any]:
    if sys.version_info >= (3, 10):
        return fn
    else:
        return unittest.skip("Requires Python 3.10+")(fn)


# Controls tests generated in test/inductor/test_torchinductor_dynamic_shapes.py
# and test/dynamo/test_dynamic_shapes.py
def expectedFailureDynamic(fn: Callable[..., Any]) -> Callable[..., Any]:
    fn._expected_failure_dynamic = True  # type: ignore[attr-defined]
    return fn


# Controls tests generated in test/inductor/test_torchinductor_codegen_dynamic_shapes.py
def expectedFailureCodegenDynamic(fn: Callable[..., Any]) -> Callable[..., Any]:
    fn._expected_failure_codegen_dynamic = True  # type: ignore[attr-defined]
    return fn


# Controls test generated in test/inductor/test_cpp_wrapper.py
def expectedFailureDynamicWrapper(fn: Callable[..., Any]) -> Callable[..., Any]:
    fn._expected_failure_dynamic_wrapper = True  # type: ignore[attr-defined]
    return fn


def reset_rng_state(use_xla: bool = False) -> None:
    torch.manual_seed(1337)
    random.seed(1337)
    if np:
        np.random.seed(1337)
    if use_xla:
        import torch_xla.core.xla_model as xm

        xm.set_rng_state(1337, str(xm.xla_device()))