import collections
import dis
import functools
import itertools
import logging
import os
import random
import sys
import threading
import time
import traceback
import types
import typing
import weakref
from typing import Any, Callable, Dict, List, Optional, Set

from torch.fx._lazy_graph_module import (  # type: ignore[attr-defined]
    _use_lazy_graph_module,
)

try:
    import numpy as np
except ModuleNotFoundError:
    np = None  # type: ignore[assignment]

import torch
import torch._logging
from torch._guards import compile_context, CompileContext, CompileId, tracing
from torch._logging import structured
from torch._utils_internal import signpost_event
from torch.fx.experimental.symbolic_shapes import (
    ConstraintViolationError,
    GuardOnDataDependentSymNode,
)
from torch.fx.graph_module import _forward_from_src as original_forward_from_src
from torch.nn.parallel.distributed import DistributedDataParallel
from torch.utils._python_dispatch import _disable_current_modes
from torch.utils._traceback import format_traceback_short

from . import config, exc, trace_rules
from .backends.registry import CompilerFn
from .bytecode_analysis import remove_dead_code, remove_pointless_jumps
from .bytecode_transformation import (
    check_inst_exn_tab_entries_valid,
    Instruction,
    is_generator,
    propagate_inst_exn_table_entries,
    transform_code_object,
)
from .cache_size import (
    CacheSizeRelevantForFrame,
    compute_cache_size,
    exceeds_cache_size_limit,
    is_recompilation,
)
from .eval_frame import always_optimize_code_objects, skip_code, TorchPatcher
from .exc import (
    augment_exc_message,
    BackendCompilerFailed,
    format_error_msg,
    InternalTorchDynamoError,
    TorchRuntimeError,
    UncapturedHigherOrderOpError,
    unimplemented,
    Unsupported,
)
from .guards import (
    CheckFunctionManager,
    get_and_maybe_log_recompilation_reason,
    GuardedCode,
)
from .hooks import Hooks
from .output_graph import OutputGraph
from .replay_record import ExecutionRecord
from .symbolic_convert import InstructionTranslator, SpeculationLog
from .trace_rules import is_numpy
from .types import BytecodeHook
from .utils import (
    CleanupManager,
    CompilationMetrics,
    counters,
    dynamo_timed,
    format_bytecode,
    frame_phase_timing,
    gen_record_file_name,
    increment_frame,
    is_namedtuple,
    istype,
    LazyString,
    maybe_cprofile,
    orig_code_map,
    record_compilation_metrics,
    reset_graph_break_dup_checker,
    setup_compile_debug,
    troubleshooting_url,
    write_record_to_file,
)

log = logging.getLogger(__name__)
bytecode_log = torch._logging.getArtifactLogger(__name__, "bytecode")
GlobalStateGuard = torch._C._dynamo.guards.GlobalStateGuard

compile_lock = threading.RLock()


class Tracker:
    def __init__(self):
        self.seen = []
        self.seen_ids = set()

    def add(self, strong_obj):
        idx = id(strong_obj)
        if idx not in self.seen_ids:
            obj = weakref.ref(strong_obj, lambda _: self.seen_ids.remove(idx))
            self.seen.append(obj)
            self.seen_ids.add(idx)

    def __contains__(self, item):
        return id(item) in self.seen_ids

    def clear(self):
        self.seen.clear()
        self.seen_ids.clear()


input_codes = Tracker()
output_codes = Tracker()

initial_global_state: Optional[GlobalStateGuard] = None


@functools.wraps(original_forward_from_src)
def fx_forward_from_src_skip_result(*args, **kwargs):
    # we monkey patch FX to prevent infinite loop of trying to convert
    # our generated code
    result: types.FunctionType = original_forward_from_src(*args, **kwargs)
    skip_code(result.__code__)
    return result


def preserve_global_state(fn):
    """
    Context manager to:
        1) Save/restore torch.is_grad_enabled() state
        2) Save/restore python random state
        3) Save/restore torch random state
        4) Monkey patch torch.fx.graph_module._forward_from_src
    """

    @functools.wraps(fn)
    def _fn(*args, **kwargs):
        guards = GlobalStateGuard()
        prior_grad_mode = torch.is_grad_enabled()
        prior_inference_mode = torch.is_inference_mode_enabled()
        prior_deterministic = torch.are_deterministic_algorithms_enabled()
        prior_warn_only = torch.is_deterministic_algorithms_warn_only_enabled()
        py_rng_state = random.getstate()
        torch_rng_state = torch.random.get_rng_state()
        if torch.cuda.is_available():
            cuda_rng_state = torch.cuda.get_rng_state()
        prior_fwd_from_src = torch.fx.graph_module._forward_from_src
        torch.fx.graph_module._forward_from_src = fx_forward_from_src_skip_result
        cleanup = setup_compile_debug()
        try:
            return fn(*args, **kwargs)
        finally:
            cleanup.close()
            torch._C._set_grad_enabled(prior_grad_mode)
            torch.torch.autograd.grad_mode._enter_inference_mode(prior_inference_mode)
            torch.use_deterministic_algorithms(
                prior_deterministic, warn_only=prior_warn_only
            )
            random.setstate(py_rng_state)
            torch.random.set_rng_state(torch_rng_state)
            if torch.cuda.is_available():
                torch.cuda.set_rng_state(cuda_rng_state)  # type: ignore[possibly-undefined]
            torch.fx.graph_module._forward_from_src = prior_fwd_from_src
            assert (
                guards.check()
            ), "Global state changed while dynamo tracing, please report a bug"

    _fn._torchdynamo_orig_callable = fn  # type: ignore[attr-defined]
    return _fn


@TorchPatcher.suppress_torch_distributed_warnings
def has_tensor_in_frame(frame):
    """Check if the frame has torch.* related bits"""
    # Check if the function was decorated using torch._dynamo.optimize
    if frame.f_code in always_optimize_code_objects:
        return True

    # Check if there is global import of torch.*
    for co_name in frame.f_code.co_names:
        if co_name in frame.f_globals:
            obj = frame.f_globals[co_name]
            if isinstance(obj, types.ModuleType) and (
                obj.__name__.startswith("torch.") or obj is torch
            ):
                return True
            # ... or a global import of numpy.*
            if np and config.trace_numpy and (obj is np or is_numpy(obj)):
                return True

    seen_ids: Dict[int, bool] = dict()

    def has_tensor(obj):
        """Recursively check if the obj has a tensor"""
        obj_id = id(obj)
        if obj_id in seen_ids:
            return seen_ids[obj_id]
        seen_ids[obj_id] = False

        if isinstance(obj, (torch.Tensor, torch.nn.Module)) or (
            istype(obj, type) and issubclass(obj, torch.nn.Module)
        ):
            seen_ids[obj_id] = True
            return seen_ids[obj_id]
        elif (
            config.trace_numpy
            and np
            and (istype(obj, np.ndarray) or isinstance(obj, np.generic))
        ):
            seen_ids[obj_id] = True
            return seen_ids[obj_id]
        elif istype(obj, (list, tuple)):
            seen_ids[obj_id] = any(has_tensor(v) for v in obj)
            return seen_ids[obj_id]
        elif istype(obj, dict):
            # Some packages like pytest can be updated during runtime. So, make a
            # copy of values to avoid issues like "RuntimeError: dictionary
            # changed size during iteration"
            values = list(obj.values())
            seen_ids[obj_id] = any(has_tensor(v) for v in values)
            return seen_ids[obj_id]
        elif istype(obj, (str, int, float, type(None), bool)):
            seen_ids[obj_id] = False
            return seen_ids[obj_id]
        elif is_namedtuple(obj) and hasattr(obj, "_fields"):
            seen_ids[obj_id] = any(has_tensor(getattr(obj, v)) for v in obj._fields)
            return seen_ids[obj_id]
        else:
            # if config.debug:
            #     print(
            #         f"Assuming that object of type {type(obj)} does not have a tensor"
            #     )
            return False

    # Check if the passed arguments are of type Tensor
    for value in frame.f_locals.values():
        if has_tensor(value):
            return True

    log.debug(
        "skipping because no torch.* %s \
            %s %s",
        frame.f_code.co_name,
        frame.f_code.co_filename,
        frame.f_code.co_firstlineno,
    )

    return False


def exception_handler(e, code, frame=None, export=False):
    record_filename = None
    if hasattr(e, "exec_record"):
        record_filename = gen_record_file_name(e, code)
        write_record_to_file(record_filename, e.exec_record)
        e.record_filename = record_filename

    augment_exc_message(e, export=export)


FRAME_COUNTER = 0
FRAME_COMPILE_COUNTER: typing.Counter[int] = collections.Counter()


def convert_frame_assert(
    compiler_fn: CompilerFn,
    one_graph: bool = True,
    export: bool = False,
    export_constraints=None,
):
    """Fully convert a frame into an FX graph"""
    reset_graph_break_dup_checker()

    def _convert_frame_assert(
        frame: types.FrameType, cache_entry, hooks: Hooks, frame_state, *, skip: int = 0
    ):
        increment_frame()

        code = frame.f_code

        cache_size = compute_cache_size(frame, cache_entry)
        recompile_reasons = None
        if is_recompilation(cache_size):
            recompile_reasons = get_and_maybe_log_recompilation_reason(
                cache_entry, frame
            )

        input_codes.add(code)
        if code in output_codes:
            return None
        if (
            os.environ.get("TORCHDYNAMO_DEBUG_FUNCTION")
            and os.environ.get("TORCHDYNAMO_DEBUG_FUNCTION") != code.co_name
        ):
            return None
        if code.co_name == "<genexpr>" and code.co_filename.endswith(
            (
                "transformers/file_utils.py",
                "transformers/utils/generic.py",
                "diffusers/utils/outputs.py",
            )
        ):
            # not needed, but cleans up torchbench error stats
            return None
        if code.co_name == "__setattr__":
            # setattr could be tricky to handle generally,
            # but also not likely useful to compile- skip the whole frame
            return None
        if code.co_name == "__init__" and code.co_filename.startswith(
            os.path.dirname(torch.optim.__file__)
        ):
            # optimizer support is still incomplete see
            # test_state_dict in test/dynamo/test_optimizers.py
            return None

        # Check if the frame is generated by an exec builtin call
        # TODO - Running exec generated frame seems propagates f_globals to the
        # next frames.
        if code.co_name == "<module>" and code.co_filename == "<string>":
            return None

        if (
            code.co_name == "<lambda>"
            and code.co_filename == "<string>"
            and not bool(frame.f_builtins)
        ):
            # namedtuple subclass constructor. Empty builtins cause issue with
            # len keyword in LIST_LEN guard.
            return None

        if is_generator(code):
            unimplemented("generator")
        exceeded, limit_type = exceeds_cache_size_limit(cache_size)
        if exceeded:

            def format_func_info(code):
                return f"'{code.co_name}' ({code.co_filename}:{code.co_firstlineno})"

            def format_guard_failures():
                assert recompile_reasons, "TODO(whc) any other recompile reasons?"
                return recompile_reasons[-1]

            log.warning(
                "torch._dynamo hit config.%s (%s)\n"
                "   function: %s\n"
                "   last reason: %s\n"
                'To log all recompilation reasons, use TORCH_LOGS="recompiles".\n'
                "To diagnose recompilation issues, see %s.",
                limit_type,
                getattr(config, limit_type),
                format_func_info(code),
                format_guard_failures(),
                troubleshooting_url,
            )
            unimplemented(f"{limit_type} reached")

        if not has_tensor_in_frame(frame):
            return None

        global initial_global_state
        initial_global_state = GlobalStateGuard()

        global FRAME_COUNTER
        if "_id" not in frame_state:
            frame_state["_id"] = FRAME_COUNTER
            FRAME_COUNTER += 1
        frame_id = frame_state["_id"]

        frame_compile_id = FRAME_COMPILE_COUNTER[frame_id]
        FRAME_COMPILE_COUNTER[frame_id] += 1

        compile_id = CompileId(frame_id, frame_compile_id)

        signpost_event(
            "dynamo",
            "_convert_frame_assert._compile",
            {
                "co_name": code.co_name,
                "co_filename": code.co_filename,
                "co_firstlineno": code.co_firstlineno,
                "cache_size": cache_size.num_cache_entries_with_same_id_matched_objs,
                "accumulated_cache_size": cache_size.num_cache_entries,
            },
        )

        return _compile(
            frame.f_code,
            frame.f_globals,
            frame.f_locals,
            frame.f_builtins,
            compiler_fn,
            one_graph,
            export,
            export_constraints,
            hooks,
            cache_size,
            frame,
            frame_state=frame_state,
            compile_id=compile_id,
            skip=skip + 1,
        )

    _convert_frame_assert._torchdynamo_orig_callable = compiler_fn  # type: ignore[attr-defined]

    def _clone_with_backend(backend):
        return convert_frame_assert(backend, one_graph, export, export_constraints)

    _convert_frame_assert._clone_with_backend = _clone_with_backend  # type: ignore[attr-defined]
    return _convert_frame_assert


from collections import OrderedDict

from torch.utils.hooks import RemovableHandle

# we have to use `OrderedDict` to make `RemovableHandle` work.
_bytecode_hooks: Dict[int, BytecodeHook] = OrderedDict()


def register_bytecode_hook(hook: BytecodeHook) -> RemovableHandle:
    """Register hooks for bytecode generated by Dynamo. The hook can do some
    logging, as well as return a new code object to be used. Please refer
    to `BytecodeHook` for the hook signature.
    """
    handle = RemovableHandle(_bytecode_hooks)
    _bytecode_hooks[handle.id] = hook
    return handle


@_use_lazy_graph_module(config.use_lazy_graph_module)
@maybe_cprofile
def _compile(
    code: types.CodeType,
    globals: Dict[str, object],
    locals: Dict[str, object],
    builtins: Dict[str, object],
    compiler_fn: CompilerFn,
    one_graph: bool,
    export: bool,
    export_constraints,
    hooks: Hooks,
    cache_size: CacheSizeRelevantForFrame,
    frame: Optional[types.FrameType] = None,
    frame_state=None,
    compile_id=None,
    *,
    skip: int = 0,
) -> Optional[GuardedCode]:
    from torch.fx.experimental.validator import (
        bisect,
        BisectValidationException,
        translation_validation_enabled,
        ValidationException,
    )

    output: Optional[OutputGraph] = None
    tracer: Optional[InstructionTranslator] = None
    # This is shared across restarts
    mutated_closure_cell_contents: Set[str] = set()
    speculation_log = SpeculationLog()
    torch._dynamo.callback_handler.run_start_callbacks()

    @preserve_global_state
    def transform(instructions, code_options):
        nonlocal output
        nonlocal tracer
        speculation_log.restart()
        tracer = InstructionTranslator(
            instructions,
            code,
            locals,
            globals,
            builtins,
            code_options,
            compiler_fn,
            one_graph,
            export,
            export_constraints,
            mutated_closure_cell_contents,
            frame_state=frame_state,
            speculation_log=speculation_log,
        )

        try:
            with tracing(tracer.output.tracing_context), tracer.set_current_tx():
                tracer.run()
        except exc.UnspecializeRestartAnalysis:
            speculation_log.clear()
            raise
        except (exc.SpeculationRestartAnalysis, exc.SkipFrame):
            raise
        except Exception:
            if translation_validation_enabled():
                bisect(tracer.output.shape_env)
            raise
        finally:
            tracer.output.call_cleanup_hooks()

        output = tracer.output
        assert output is not None
        assert output.output_instructions
        instructions[:] = output.output_instructions
        code_options.update(output.code_options)

        if config.dead_code_elimination:
            propagate_inst_exn_table_entries(instructions)
            check_inst_exn_tab_entries_valid(instructions)
            instructions[:] = remove_pointless_jumps(remove_dead_code(instructions))

    @dynamo_timed(phase_name="entire_frame_compile")
    def compile_inner(
        code: types.CodeType,
        one_graph: bool,
        hooks: Hooks,
        transform: Callable[[List[Instruction], Dict[str, Any]], Any],
    ) -> Optional[GuardedCode]:
        nonlocal output
        for attempt in itertools.count():
            CompileContext.get().attempt = attempt
            try:
                out_code = transform_code_object(code, transform)
                break
            except exc.RestartAnalysis as e:
                log.info(
                    "Restarting analysis due to %s",
                    LazyString(format_traceback_short, e.__traceback__),
                )
                if attempt > 100:
                    unimplemented("100+ RestartAnalysis() calls")
            except exc.SkipFrame as e:
                log.debug(
                    "Skipping frame %s %s \
                    %s %s",
                    e,
                    code.co_name,
                    code.co_filename,
                    code.co_firstlineno,
                )
                if one_graph:
                    log.debug("No graph captured with one_graph=True")
                return None

        def log_bytecode(prefix, name, filename, line_no, code):
            if bytecode_log.isEnabledFor(logging.DEBUG):
                bytecode_log.debug(
                    format_bytecode(prefix, name, filename, line_no, code)
                )

        log_bytecode(
            "ORIGINAL BYTECODE",
            code.co_name,
            code.co_filename,
            code.co_firstlineno,
            code,
        )
        log_bytecode(
            "MODIFIED BYTECODE",
            code.co_name,
            code.co_filename,
            code.co_firstlineno,
            out_code,  # type: ignore[possibly-undefined]
        )

        for hook in _bytecode_hooks.values():
            hook_output = hook(code, out_code)
            if hook_output is not None:
                out_code = hook_output

        orig_code_map[out_code] = code
        output_codes.add(out_code)

        assert output is not None

        # Tests for new code objects.
        # The rationale for these tests can be found in torch/csrc/dynamo/eval_frame.c
        # Only test once the code object is created.
        # They are not tested during runtime.

        def count_args(code):
            import inspect

            return (
                code.co_argcount
                + code.co_kwonlyargcount
                + bool(code.co_flags & inspect.CO_VARARGS)
                + bool(code.co_flags & inspect.CO_VARKEYWORDS)
            )

        total_argcount_old = count_args(code)
        total_argcount_new = count_args(out_code)
        msg = "arg mismatch: "
        msg += f"old code object has args {code.co_varnames[:total_argcount_old]}, "
        msg += f"new code object has args {out_code.co_varnames[:total_argcount_new]}"
        assert (
            code.co_varnames[:total_argcount_old]
            == out_code.co_varnames[:total_argcount_new]
        ), msg

        msg = "free var mismatch: "
        msg += f"old code object has free var {code.co_freevars}, "
        msg += f"new code object has free var {out_code.co_freevars}"
        assert code.co_freevars == out_code.co_freevars, msg

        msg = "cell var mismatch: "
        msg += f"old code object has cell var {code.co_cellvars}, "
        msg += f"new code object has cell var {out_code.co_cellvars}"
        assert code.co_cellvars == out_code.co_cellvars, msg

        # Skipping Dynamo on a frame without any extracted graph.
        # This does not affect eager functionality. But this is necessary
        # for export for cases where Dynamo-reconstructed bytecode can create
        # new function frames, confusing export in thinking that there
        # are extra graphs now.

        if output.export and output.is_empty_graph():
            return None

        assert output.guards is not None
        CleanupManager.instance[out_code] = output.cleanups
        check_fn = CheckFunctionManager(
            output,
            hooks.guard_fail_fn if hooks else None,
        )

        guarded_code = GuardedCode(out_code, check_fn.check_fn)

        if not output.is_empty_graph() and hooks.guard_export_fn is not None:
            # We should not run the guard_export_fn when Dynamo does not
            # generate any graph. This can happen in export when TorchDynamo
            # generated bytecode has some reconstruction logic for mutated
            # variables which can trigger TorchDynamo on the children frames but
            # they are benign and do not generate any new graphs.
            hooks.guard_export_fn(output.guards)

        return guarded_code

    with compile_context(CompileContext(compile_id)):
        log.debug(
            "torchdynamo start compiling %s %s:%s, stack (elided %s frames):\n%s",
            code.co_name,
            code.co_filename,
            code.co_firstlineno,
            skip + 2,
            # -2: omit current frame, omit contextlib decorator
            "".join(traceback.format_list(traceback.extract_stack()[: -2 - skip])),
        )
        # -4: -2 as above, plus trace_structured frames
        torch._logging.trace_structured(
            "dynamo_start",
            lambda: {
                "stack": structured.from_traceback(
                    traceback.extract_stack()[: -4 - skip]
                )
            },
        )
        start_time = time.time()
        fail_type: Optional[str] = None
        fail_reason: Optional[str] = None
        fail_user_frame_filename: Optional[str] = None
        fail_user_frame_lineno: Optional[int] = None
        try:
            guarded_code = compile_inner(code, one_graph, hooks, transform)
            return guarded_code
        except (
            Unsupported,
            TorchRuntimeError,
            BackendCompilerFailed,
            AssertionError,
            ConstraintViolationError,
            GuardOnDataDependentSymNode,
            ValidationException,
            UncapturedHigherOrderOpError,
            BisectValidationException,
        ) as e:
            fail_type = str(type(e))
            fail_reason = str(e)
            exception_handler(e, code, frame, export=export)
            if e.innermost_user_frame_summary is not None:  # type: ignore[union-attr]
                fail_user_frame_filename = e.innermost_user_frame_summary.filename  # type: ignore[union-attr]
                fail_user_frame_lineno = e.innermost_user_frame_summary.lineno  # type: ignore[union-attr]
            raise
        except Exception as e:
            fail_type = str(type(e))
            fail_reason = str(e)
            exception_handler(e, code, frame, export=export)
            if e.innermost_user_frame_summary is not None:  # type: ignore[attr-defined]
                fail_user_frame_filename = e.innermost_user_frame_summary.filename  # type: ignore[attr-defined]
                fail_user_frame_lineno = e.innermost_user_frame_summary.lineno  # type: ignore[attr-defined]
            raise InternalTorchDynamoError(str(e)).with_traceback(
                e.__traceback__
            ) from None
        finally:
            if tracer:
                tracer.output.local_scope = {}

            from .utils import curr_frame

            frame_key = str(curr_frame)
            if (
                fail_reason is None
                and output is not None
                and frame_key in frame_phase_timing
            ):
                guard_count = len(output.guards)
                shape_env_guard_count = len(output.shape_env.guards)
                graph_op_count = output.count_calls()
                graph_node_count = len(output.graph.nodes)
                graph_input_count = len(output.placeholders)
                entire_frame_compile_time = frame_phase_timing[frame_key].get(
                    "entire_frame_compile", None
                )
                backend_compile_time = frame_phase_timing[frame_key].get(
                    "backend_compile", None
                )
                inductor_compile_time = frame_phase_timing[frame_key].get(
                    "inductor_compile", None
                )
                code_gen_time = frame_phase_timing[frame_key].get("code_gen", None)
                non_compliant_ops = {op.__qualname__ for op in output.non_compliant_ops}
                compliant_custom_ops = {
                    op.__qualname__ for op in output.compliant_custom_ops
                }
            else:
                guard_count = None
                shape_env_guard_count = None
                graph_op_count = None
                graph_node_count = None
                graph_input_count = None
                entire_frame_compile_time = None
                backend_compile_time = None
                inductor_compile_time = None
                code_gen_time = None
                non_compliant_ops = set({})
                compliant_custom_ops = set({})
            metrics = CompilationMetrics(
                frame_key,
                code.co_name,
                code.co_filename,
                code.co_firstlineno,
                cache_size.num_cache_entries_with_same_id_matched_objs,
                cache_size.num_cache_entries,
                guard_count,
                shape_env_guard_count,
                graph_op_count,
                graph_node_count,
                graph_input_count,
                start_time,
                entire_frame_compile_time,
                backend_compile_time,
                inductor_compile_time,
                code_gen_time,
                fail_type,
                fail_reason,
                fail_user_frame_filename,
                fail_user_frame_lineno,
                non_compliant_ops,
                compliant_custom_ops,
            )
            record_compilation_metrics(metrics)
            torch._dynamo.callback_handler.run_end_callbacks()


def convert_frame(compiler_fn: CompilerFn, hooks: Hooks):
    """Try to convert a frame into an FX graph, if error leave frame unmodified"""
    inner_convert = convert_frame_assert(compiler_fn, one_graph=False)

    def _convert_frame(
        frame: types.FrameType, cache_entry, hooks: Hooks, frame_state, skip: int = 0
    ):
        counters["frames"]["total"] += 1
        try:
            result = inner_convert(
                frame, cache_entry, hooks, frame_state, skip=skip + 1
            )
            counters["frames"]["ok"] += 1
            return result
        except Exception as e:
            # These two exception types are "soft" failure, in the sense that
            # we know this is due to something we didn't implement all the
            # way, scare the user less about it.  That being said, if you
            # are trying to understand why a graph break happened, it's still
            # important to have this information, so offer it.
            #
            # NB: NotImplementedError used to be on this list, but actually
            # it is impossible for it to reach here, as it is converted into
            # InternalTorchDynamoError.  This behavior seemed reasonable
            # to me (ezyang, Aug 2023) so I kept it, but maybe at some point
            # someone wanted these to also get suppressed.  If so, you'll
            # need to make these exceptions not get wrapped

            # We intentionally don't want to suppress error here.
            if isinstance(e, UncapturedHigherOrderOpError):
                raise

            soft_fail = isinstance(e, Unsupported)
            if not config.suppress_errors and not soft_fail:
                raise

            # Suppress the error.  NB: It's very important to do the
            # suppression logging HERE, where the actual suppression
            # happens. Previously it was somewhere else and so it was
            # possible to accidentally not log at all.
            record_filename = getattr(e, "record_filename", None)
            code = frame.f_code
            error_msg = format_error_msg(e, code, record_filename, frame)

            if soft_fail:
                log.info(error_msg, exc_info=True)
            else:
                log.warning(error_msg, exc_info=True)
        return None

    _convert_frame._torchdynamo_orig_callable = compiler_fn  # type: ignore[attr-defined]
    _convert_frame._clone_with_backend = lambda backend: convert_frame(backend, hooks)  # type: ignore[attr-defined]
    return _convert_frame


# TODO mlazos: add support for same args, or record them
def replay(filename):
    from .backends.debugging import eager

    original_replay_val = config.replay_record_enabled
    config.replay_record_enabled = False
    with open(filename, "rb") as in_file:
        record = ExecutionRecord.load(in_file)
    record.globals = dict(itertools.chain(record.globals.items(), globals().items()))

    try:
        _compile(
            record.code,
            record.globals,
            record.locals,
            record.builtins,
            compiler_fn=eager,
            one_graph=False,
            export=False,
            export_constraints=None,
            hooks=Hooks(),
            cache_size=CacheSizeRelevantForFrame(0, 0),
            frame=None,
            frame_state={},
        )
    finally:
        config.replay_record_enabled = original_replay_val


def first_real_inst_idx(code):
    if sys.version_info < (3, 11):
        return 0
    for inst in dis.get_instructions(code):
        if inst.opname == "RESUME":
            return inst.offset // 2
    raise RuntimeError("RESUME instruction not found in code")


def catch_errors_wrapper(callback, hooks: Hooks):
    @functools.wraps(callback)
    def catch_errors(frame, cache_entry, frame_state):
        assert frame_state is not None

        is_skipfile = trace_rules.check(frame.f_code)
        if (
            # TODO: the first condition is not covered by any test
            frame.f_lasti >= first_real_inst_idx(frame.f_code)
            or is_skipfile
            or config.disable
        ):
            if log.isEnabledFor(logging.DEBUG):
                skip_reason = (
                    "traced frame already"
                    if frame.f_lasti >= first_real_inst_idx(frame.f_code)
                    else "in skipfiles"
                    if trace_rules.check(frame.f_code)
                    else "dynamo tracing is disabled"
                )
                if not is_skipfile or config.verbose:
                    log.debug(
                        "skipping: %s (reason: %s, file: %s)",
                        frame.f_code.co_name,
                        skip_reason,
                        frame.f_code.co_filename,
                    )
            return None
        if frame.f_code.co_filename == "<string>" and frame.f_code.co_name == "__new__":
            # nametuple constructor
            return None
        if config._get_optimize_ddp_mode() == "ddp_optimizer":
            ddp_module = DistributedDataParallel._get_active_ddp_module()
            if ddp_module:
                with compile_lock:
                    from torch._dynamo.backends.distributed import DDPOptimizer

                    ddp_optimizer = DDPOptimizer(
                        bucket_bytes_cap=ddp_module.bucket_bytes_cap,
                        backend_compile_fn=callback._torchdynamo_orig_callable,
                    )
                    assert hasattr(
                        callback, "_clone_with_backend"
                    ), "DDPOptimizer only supports callback fns that know how to clone themselves."
                    hijacked_callback = callback._clone_with_backend(
                        ddp_optimizer.compile_fn,
                    )
                    return hijacked_callback(frame, cache_entry, hooks, frame_state)

        with compile_lock, _disable_current_modes():
            # skip=1: skip this frame
            return callback(frame, cache_entry, hooks, frame_state, skip=1)

    catch_errors._torchdynamo_orig_callable = callback  # type: ignore[attr-defined]
    return catch_errors