import contextlib
import functools
import inspect
import logging
import os
import sys
import textwrap
import threading
import traceback
import types
import warnings
from enum import Enum
from typing import Optional, Tuple, TYPE_CHECKING, Union
from unittest.mock import patch

import torch
import torch.utils._pytree as pytree
from torch.fx.experimental.proxy_tensor import make_fx
from torch.fx.graph import _PyTreeCodeGen, _PyTreeInfo
from torch.nn.parallel.distributed import DistributedDataParallel
from .backends.registry import CompilerFn, lookup_backend

from .hooks import Hooks

if TYPE_CHECKING:
    from torch._C._dynamo.eval_frame import (  # noqa: F401
        reset_code,
        set_eval_frame,
        set_guard_error_hook,
        set_guard_fail_hook,
        skip_code,
        unsupported,
    )
else:
    for name in dir(torch._C._dynamo.eval_frame):
        if name.startswith("__"):
            continue
        globals()[name] = getattr(torch._C._dynamo.eval_frame, name)

from . import config, convert_frame, skipfiles, utils
from .exc import ResetRequired
from .mutation_guard import install_generation_tagging_init
from .types import DynamoCallback
from .utils import compile_times

log = logging.getLogger(__name__)

from torch.fx.experimental import proxy_tensor

always_optimize_code_objects = utils.ExactWeakKeyDictionary()
null_context = contextlib.nullcontext

# See https://github.com/python/typing/pull/240
class Unset(Enum):
    token = 0


unset = Unset.token

compile_lock = threading.RLock()
most_recent_backend: Optional[CompilerFn] = None


class OptimizedModule(torch.nn.Module):
    """
    Wraps the original nn.Module object and later patches its
    forward method to optimized self.forward method.
    """

    def __init__(self, mod, dynamo_ctx):
        super().__init__()
        # Installs the params/buffer
        self._orig_mod = mod
        self.dynamo_ctx = dynamo_ctx

    def __getattr__(self, name):
        if name == "_orig_mod":
            return self._modules["_orig_mod"]
        return getattr(self._orig_mod, name)

    def forward(self, *args, **kwargs):
        return self.dynamo_ctx(self._orig_mod.forward)(*args, **kwargs)


def remove_from_cache(f):
    """
    Make sure f.__code__ is not cached to force a recompile
    """
    if isinstance(f, types.CodeType):
        reset_code(f)
    elif hasattr(f, "__code__"):
        reset_code(f.__code__)
    elif hasattr(getattr(f, "forward", None), "__code__"):
        reset_code(f.forward.__code__)
    else:
        from . import reset

        reset()
        log.warning("could not determine __code__ for %s", f)


def nothing():
    pass


def innermost_fn(fn):
    """
    In case of nesting of _TorchDynamoContext calls, find the innermost
    function. TorchDynamo caches on fn.__code__ object, so its necessary to find
    the innermost function to pass on the optimize, run, disable etc.
    """
    unaltered_fn = fn
    while hasattr(unaltered_fn, "_torchdynamo_orig_callable"):
        unaltered_fn = unaltered_fn._torchdynamo_orig_callable
        assert callable(unaltered_fn)
    return unaltered_fn


@contextlib.contextmanager
def enable_dynamic(enable: bool = True):
    if not enable:
        yield
        return
    with config.patch(dynamic_shapes=True, specialize_int_float=False):
        yield


class _TorchDynamoContext:
    def __init__(
        self,
        callback: DynamoCallback,
        on_enter=nothing,
        backend_ctx_ctor=null_context,
        patch_fn=nothing,
        first_ctx=False,
        *,
        dynamic=False,
    ):
        super().__init__()
        assert callable(callback) or callback is False or callback is None
        self.callback: DynamoCallback = callback
        self.prior: Union[Unset, DynamoCallback] = unset
        self.on_enter = on_enter
        self.extra_ctx_ctor = backend_ctx_ctor
        self.first_ctx = first_ctx
        self.dynamic = dynamic
        patch_fn()

    def __enter__(self):
        if config.raise_on_ctx_manager_usage:
            raise RuntimeError(
                "torch._dynamo.optimize(...) is used with a context manager. "
                "Please refer to https://github.com/pytorch/torchdynamo#usage-example "
                "to use torch._dynamo.optimize(...) as an annotation/decorator. "
            )
        self.on_enter()
        self.prior = set_eval_frame(self.callback)
        self.backend_ctx = self.extra_ctx_ctor()
        self.backend_ctx.__enter__()
        self.dynamic_ctx = enable_dynamic(self.dynamic)
        self.dynamic_ctx.__enter__()

    def __exit__(self, exc_type, exc_val, exc_tb):
        assert self.prior is not unset
        set_eval_frame(self.prior)
        self.prior = unset
        # TODO: This is totally not the right way to chain contexts manually
        self.dynamic_ctx.__exit__(exc_type, exc_val, exc_tb)
        self.backend_ctx.__exit__(exc_type, exc_val, exc_tb)

    def __call__(self, fn):
        fn = innermost_fn(fn)
        # Optimize the forward method of torch.nn.Module object
        if isinstance(fn, torch.nn.Module):
            mod = fn
            new_mod = OptimizedModule(mod, self)
            # Save the function pointer to find the original callable while nesting
            # of decorators.
            new_mod._torchdynamo_orig_callable = mod.forward
            return new_mod

        assert callable(fn)

        callback = self.callback
        on_enter = self.on_enter
        backend_ctx_ctor = self.extra_ctx_ctor

        @functools.wraps(fn)
        def _fn(*args, **kwargs):
            if (
                not isinstance(self, DisableContext)
                and torch.fx._symbolic_trace.is_fx_tracing()
            ):
                if config.error_on_nested_fx_trace:
                    raise RuntimeError(
                        "Detected that you are using FX to symbolically trace "
                        "a dynamo-optimized function. This is not supported at the moment."
                    )
                else:
                    return fn(*args, **kwargs)

            on_enter()
            prior = set_eval_frame(callback)
            backend_ctx = backend_ctx_ctor()
            backend_ctx.__enter__()
            dynamic_ctx = enable_dynamic(self.dynamic)
            dynamic_ctx.__enter__()
            try:
                return fn(*args, **kwargs)
            finally:
                set_eval_frame(prior)
                dynamic_ctx.__exit__(None, None, None)
                backend_ctx.__exit__(None, None, None)

        # hooks to properly handle inlining
        if isinstance(self, DisableContext):
            _fn._torchdynamo_disable = True  # type: ignore[attr-defined]
        else:
            _fn._torchdynamo_inline = fn  # type: ignore[attr-defined]

        # Save the function pointer to find the original callable while nesting
        # of decorators.
        _fn._torchdynamo_orig_callable = fn  # type: ignore[attr-defined]

        # If the function is called using torch._dynamo.optimize decorator, we
        # should prevent any type of skipping.
        if callback not in (None, False):
            if not hasattr(fn, "__code__"):
                raise RuntimeError(
                    textwrap.dedent(
                        """

                        torch._dynamo.optimize is called on a non function object.
                        If this is a callable class, please wrap the relevant code into a function and optimize the
                        wrapper function.

                        >> class CallableClass:
                        >>     def __init__(self):
                        >>         super().__init__()
                        >>         self.relu = torch.nn.ReLU()
                        >>
                        >>     def __call__(self, x):
                        >>         return self.relu(torch.sin(x))
                        >>
                        >>     def print_hello(self):
                        >>         print("Hello world")
                        >>
                        >> mod = CallableClass()

                        If you want to optimize the __call__ function and other code, wrap that up in a function

                        >> def wrapper_fn(x):
                        >>     y = mod(x)
                        >>     return y.sum()

                        and then optimize the wrapper_fn

                        >> opt_wrapper_fn = torch._dynamo.optimize(wrapper_fn)
                        """
                    )
                )
            always_optimize_code_objects[fn.__code__] = True

        return _fn


class OptimizeContext(_TorchDynamoContext):
    @staticmethod
    def _different_backend(old, new):
        return not (old == new or old is None)

    def __init__(self, callback, backend_ctx_ctor, first_ctx=False, *, dynamic=False):
        def on_enter():
            global most_recent_backend
            if OptimizeContext._different_backend(most_recent_backend, compiler_fn):
                if config.raise_on_backend_change:
                    raise ResetRequired()
                else:
                    warnings.warn(
                        "changing options to `torch.compile()` may require "
                        "calling `torch._dynamo.reset()` to take effect"
                    )
            most_recent_backend = compiler_fn
            install_generation_tagging_init()

        compiler_fn = innermost_fn(callback)
        super().__init__(
            callback=callback,
            on_enter=on_enter,
            backend_ctx_ctor=backend_ctx_ctor,
            patch_fn=TorchPatcher.patch,
            first_ctx=first_ctx,
            dynamic=dynamic,
        )


class RunOnlyContext(_TorchDynamoContext):
    def __init__(self):
        super().__init__(callback=False)


class DisableContext(_TorchDynamoContext):
    def __init__(self):
        super().__init__(callback=None)


def catch_errors_wrapper(callback, hooks: Hooks):
    @functools.wraps(callback)
    def catch_errors(frame, cache_size):
        if (
            frame.f_lasti >= 0
            or skipfiles.check(frame.f_code.co_filename)
            or config.disable
        ):
            log.debug(f"skipping {frame.f_code.co_name} {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.optimize_ddp:
            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,
                    )
                    hijacked_callback = convert_frame.convert_frame(
                        ddp_optimizer.compile_fn,
                        hooks=hooks,
                    )
                    return hijacked_callback(frame, cache_size, hooks)

        with compile_lock:
            return callback(frame, cache_size, hooks)

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


def _optimize_catch_errors(
    compile_fn, hooks: Hooks, backend_ctx_ctor=null_context, dynamic=False
):