# mypy: allow-untyped-defs
"""
This file contains canonical definitions for our symbol naming conventions,
across torch.fx.experimental.symbolic_shapes and torch._inductor.  The
intention is:

1. To make it easily greppable where all the sites we use a prefix are
2. Make it possible to easily tell if we can introduce a new prefix without
   introducing a conflict

You can occasionally test if prefixes have been hardcoded by renaming prefixes
in this file and seeing what breaks.
"""

from enum import auto, Enum
from typing import Sequence, Union

import sympy


class SymT(Enum):
    SIZE = auto()
    FLOAT = auto()
    UNBACKED_INT = auto()
    UNBACKED_FLOAT = auto()
    # Inductor: The intermediates in inner_fn tmp0, one generated per ops call.
    # If one of these shows up in an indexing expression, that means an
    # indirect load is happening.
    TMP = auto()
    # Inductor: Placeholder variable that is later replaced with TMP
    INDIRECT = auto()
    # Inductor: Some size expressions are replaced with a precomputed size ps0
    # which is computed host side, and then directly reused in the kernel, so
    # we don't repeatedly recompute it on device.
    PRECOMPUTED_SIZE = auto()
    # Inductor: An indexing variable i0 in loops IR which ranges over non-reduced
    # dim in the loop
    INDEX = auto()
    # Inductor: A reduction indexing r0 variable in loops IR which ranges over
    # reduced dim in the loop
    RINDEX = auto()
    # Inductor: In templated kernels torch._inductor.kernel, we have a hook to
    # store the final output and append epilogue fusions.  To do this, we must
    # know what the indexes the outputs range over.  NB: These will also
    # advertise as INDEX, this is... probably OK?
    TEMPLATE_INDEX = auto()
    # Inductor: iteration domain for blockIdx.x/blockIdx.y
    XBLOCK = auto()
    YBLOCK = auto()
    # Inductor: this is used solely for dynamic_reshape_indexer
    VIEW = auto()
    # Alternate (non-modular) indexing used in halide kernels
    HALIDE = auto()


# Invariant: there must not be a prefix which is a prefix of another string,
# as this introduces ambiguity
prefix_str = {
    SymT.SIZE: "s",  # integer
    SymT.UNBACKED_INT: "u",  # integer
    # Prefix z here is chosen to avoid false aliasing in symbol_is_type test
    # DO NOT add a "z" type.  You also need to avoid conflicts on these
    # prefixes but this is somewhat easier to manage
    SymT.FLOAT: "zf",
    SymT.UNBACKED_FLOAT: "zuf",
    SymT.TMP: "tmp",
    SymT.PRECOMPUTED_SIZE: "ps",
    SymT.INDEX: "i",
    SymT.RINDEX: "r",
    SymT.TEMPLATE_INDEX: "idx",
    SymT.XBLOCK: "x",
    SymT.YBLOCK: "y",
    SymT.INDIRECT: "indirect",  # false aliasing?
    SymT.VIEW: "view",
    SymT.HALIDE: "h",
}


def make_symbol(prefix: SymT, idx: int, **kwargs) -> sympy.Symbol:
    # TODO: maybe put the assumptions here directly
    return sympy.Symbol(f"{prefix_str[prefix]}{idx}", **kwargs)


# This type is a little wider than it should be, because free_symbols says
# that it contains Basic, rather than Symbol
def symbol_is_type(sym: sympy.Basic, prefix: Union[SymT, Sequence[SymT]]) -> bool:
    assert isinstance(sym, sympy.Symbol)
    name_str = sym.name.lower()  # Match capitalized names like XBLOCK, RBLOCK
    if isinstance(prefix, SymT):
        return name_str.startswith(prefix_str[prefix])
    else:
        return name_str.startswith(tuple(prefix_str[p] for p in prefix))


def free_symbol_is_type(e: sympy.Expr, prefix: SymT) -> bool:
    return any(symbol_is_type(v, prefix) for v in e.free_symbols)