from __future__ import annotations

import itertools
from contextlib import contextmanager
from itertools import chain
from threading import local
from typing import Any, Callable, Union
from unittest.mock import patch

import sympy

from torch._inductor.utils import IndentedBuffer

from torch.fx.graph import inplace_methods, magic_methods

from .utils import reduction_num_outputs, sympy_str, sympy_symbol

threadlocal = local()


class Virtualized:
    """
    A global variable that redirects via thread local variable

    This allows us to swap in different op implementations in codegen.
    """

    def __init__(self, vname: str, default):
        self._key: str = f"__torchinductor_{vname}"
        self._default = default

    def _set_handler(self, value):
        prior = self._get_handler()
        setattr(threadlocal, self._key, value)

        @contextmanager
        def ctx():
            try:
                yield
            finally:
                self._set_handler(prior)

        return ctx()

    def _get_handler(self):
        try:
            return getattr(threadlocal, self._key)
        except AttributeError:
            return self._default()

    def __getattr__(self, name):
        return getattr(self._get_handler(), name)


class NullHandler:
    pass


def _arg_str(a) -> str:
    if isinstance(a, sympy.Expr):
        return sympy_str(a)
    return str(a)


class MockHandler:
    def __getattr__(self, name):
        if name == "name":
            return "MockHandler"

        def inner(*args, **kwargs):
            fargs = [_arg_str(a) for a in args]
            fargs.extend(f"{k}={v}" for k, v in kwargs.items())
            return f"ops.{name}({', '.join(fargs)})"

        return inner

    @staticmethod
    def masked(mask, body, other) -> str:
        return f"ops.masked({mask}, {body()}, {other})"

    @staticmethod
    def indirect_indexing(index_var, size, check=True) -> sympy.Symbol:
        return sympy_symbol(f"({str(index_var)})")

    @classmethod
    def _init_cls(cls):
        def make_handler(format_string):
            @staticmethod  # type: ignore[misc]
            def inner(*args):
                return format_string.format(*args)

            return inner

        for name, format_string in chain(
            magic_methods.items(), inplace_methods.items()
        ):
            setattr(cls, name, make_handler(format_string))


class KernelFormatterHandler:
    def __init__(self, parent_handler):
        self.parent_handler = parent_handler
        self.output = IndentedBuffer(1)
        self.var_counter = itertools.count()

    @staticmethod
    def ir_to_string(ir_fn, index, rindex=None) -> str:
        from .ir import FlexibleLayout

        args = [index, rindex] if rindex is not None else [index]
        names = ["index", "rindex"] if rindex is not None else ["index"]
        formatter = KernelFormatterHandler(MockHandler())

        with formatter.output.indent(-1):
            formatter.output.writeline(f"def inner_fn({', '.join(names)}):")
        for name, arg in zip(names, args):
            if arg:
                lhs = ", ".join(
                    [
                        str("_" if isinstance(v, (int, sympy.Integer)) else v)
                        for v in arg
                    ]
                )
                formatter.output.writeline(f"{lhs} = {name}")

        with V.set_ops_handler(formatter), patch.object(  # type: ignore[call-arg]
            FlexibleLayout, "allow_indexing", True
        ):
            result = ir_fn(*args)
            return formatter.getvalue(result)

    def __getattr__(self, name) -> Callable[..., str]:
        def inner(*args, **kwargs):
            line = getattr(self.parent_handler, name)(*args, **kwargs)
            if name == "indirect_indexing":
                return line
            # replace line with a new variable name
            varname = f"tmp{next(self.var_counter)}"
            self.output.writeline(f"{varname} = {line}")
            return varname

        return inner

    def reduction(
        self, dtype, src_dtype, reduction_type, value
    ) -> Union[tuple[str, ...], str]:
        line = self.parent_handler.reduction(dtype, src_dtype, reduction_type, value)
        num_values = reduction_num_outputs(reduction_type)
        varnames = [f"tmp{next(self.var_counter)}" for _ in range(num_values)]
        self.output.writeline(f"{','.join(varnames)} = {line}")
        return tuple(varnames) if num_values > 1 else varnames[0]

    def getvalue(self, result):
        self.output.writeline(f"return {result}")
        return self.output.getvalue()


class WrapperHandler:
    def __init__(self, inner):
        self._inner = inner

    def __getattr__(self, item):
        return getattr(self._inner, item)


MockHandler._init_cls()

_ops = Virtualized("ops", MockHandler)
_graph = Virtualized("graph", NullHandler)
_real_inputs = Virtualized("real_inputs", NullHandler)
_fake_mode = Virtualized("fake_mode", NullHandler)
_kernel = Virtualized("kernel", NullHandler)
_debug = Virtualized("debug", NullHandler)
_interpreter = Virtualized("interpreter", NullHandler)
_aot_compilation = Virtualized("aot_compilation", NullHandler)


class OpsValue:
    """The return type of most ops calls.

    This exists so we can overload magic methods, and write mathematical
    expressions much more fluently. So instead of

        ops.add(ops.mul(ops.mul(ops.sub(ops.mul(_Ap2, x), _Ap3), x), x), _1)

    we can write

        (_Ap2 * x - _Ap3) * x * x + _1

    """

    value: Any

    def __init__(self, value):
        self.value = value

    def __str__(self):
        return str(self.value)

    def __repr__(self):
        return f"OpsValue({self.value!r})"

    def __add__(self, other):
        return ops.add(self, other)

    def __mul__(self, other):
        return ops.mul(self, other)

    def __sub__(self, other):
        return ops.sub(self, other)

    def __neg__(self):
        return ops.neg(self)

    def __truediv__(self, other):
        return ops.truediv(self, other)

    def __floordiv__(self, other):
        return ops.floordiv(self, other)

    def __mod__(self, other):
        return ops.mod(self, other)

    def __pow__(self, other):
        return ops.pow(self, other)


class OpsWrapper:
    """This wraps any returned IR values into an `OpsValue` instance, so that we
    can overload the magic methods for writing mathematical expressions fluently.
    """

    def __getattr__(self, name):
        def inner(*args, **kwargs):
            new_args = [OpsWrapper._unwrap(a) for a in args]
            new_kwargs = {k: OpsWrapper._unwrap(v) for k, v in kwargs.items()}
            return OpsWrapper._wrap(getattr(_ops, name)(*new_args, **new_kwargs))

        return inner

    @staticmethod
    def _unwrap(x):
        if isinstance(x, (list, tuple)):
            return tuple(OpsWrapper._unwrap(v) for v in x)
        if isinstance(x, OpsValue):
            return x.value
        return x

    @staticmethod
    def _wrap(x):
        if isinstance(x, (list, tuple)):
            return tuple(OpsValue(v) for v in x)
        return OpsValue(x)

    @staticmethod
    def indirect_indexing(index, size, check=True):
        # Returns a sympy value, not IR value
        index = OpsWrapper._unwrap(index)
        return _ops.indirect_indexing(index, size, check)


ops = OpsWrapper()


class _V:
    MockHandler = MockHandler
    KernelFormatterHandler = KernelFormatterHandler
    WrapperHandler = WrapperHandler

    set_ops_handler: Callable[[Any], Any] = _ops._set_handler
    get_ops_handler: Callable[[], Any] = _ops._get_handler
    set_graph_handler: Callable[[Any], Any] = _graph._set_handler
    set_real_inputs: Callable[[Any], Any] = _real_inputs._set_handler
    get_real_inputs: Callable[[], Any] = _real_inputs._get_handler
    set_fake_mode: Callable[[Any], Any] = _fake_mode._set_handler
    get_fake_mode: Callable[[], Any] = _fake_mode._get_handler
    set_kernel_handler: Callable[[Any], Any] = _kernel._set_handler
    set_debug_handler: Callable[[Any], Any] = _debug._set_handler
    set_interpreter_handler: Callable[[Any], Any] = _interpreter._set_handler
    set_aot_compilation: Callable[[Any], Any] = _aot_compilation._set_handler
    get_aot_compilation: Callable[[], Any] = _aot_compilation._get_handler

    @property
    def ops(self) -> MockHandler:  # type: ignore[valid-type]
        """The operator handler specific to the current codegen task"""
        return _ops._get_handler()

    @property
    def graph(self):
        """The graph currently being generated"""
        return _graph._get_handler()

    @property
    def real_inputs(self):
        """non-fake example inputs"""
        return _real_inputs._get_handler()

    @property
    def fake_mode(self):
        """The graph currently being generated"""
        return _fake_mode._get_handler()

    @property
    def kernel(self):
        """The kernel currently being generated"""
        return _kernel._get_handler()

    @property
    def debug(self):
        return _debug._get_handler()

    @property
    def interpreter(self):
        return _interpreter._get_handler()

    @property
    def aot_compilation(self):
        return _aot_compilation._get_handler()


V = _V()