from __future__ import annotations
from typing import List, Tuple, cast, Optional, Any, Dict
import functools
from tinygrad.shape.shapetracker import ShapeTracker, View
from tinygrad.dtype import dtypes, PtrDType, ImageDType, DType
from tinygrad.ops import BufferOps, LazyOp, TernaryOps, ReduceOps, UnaryOps, MetaOps, KernelInfo
from tinygrad.codegen.uops import UOp, UOps
from tinygrad.renderer import Renderer
from tinygrad.helpers import getenv, prod

# TODO: this needs to be replaced, there shouldn't be variables in the shapetracker, only ints and UOps
from tinygrad.shape.symbolic import Variable, NumNode, SumNode, MulNode, DivNode, ModNode, LtNode, AndNode
def variable_to_uop(x, ctx=None) -> UOp: return UOp.const(dtypes.bigint, x) if isinstance(x, int) else x.render(render_ops, ctx)
render_ops: Any = { NumNode: lambda self, ops, ctx: UOp.const(dtypes.bigint, self.b),
                    MulNode: lambda self, ops, ctx: self.a.render(ops, ctx)*variable_to_uop(self.b, ctx),
                    DivNode: lambda self, ops, ctx: self.a.render(ops, ctx)//variable_to_uop(self.b, ctx),
                    ModNode: lambda self, ops, ctx: self.a.render(ops, ctx)%variable_to_uop(self.b, ctx),
                    LtNode: lambda self, ops, ctx: self.a.render(ops, ctx).lt(variable_to_uop(self.b, ctx)),
  Variable: lambda self,ops,ctx: ctx[self] if ctx is not None and self in ctx else UOp(UOps.DEFINE_VAR, dtypes.int32, (), self),
  SumNode: lambda self,ops,ctx: functools.reduce(lambda a,b: a+b.render(ops, ctx), self.nodes[1:], self.nodes[0].render(ops,ctx)),
  AndNode: lambda self,ops,ctx: functools.reduce(lambda a,b: a*b.render(ops, ctx), self.nodes[1:], self.nodes[0].render(ops,ctx)) }

if getenv("UOP_IS_SYMBOLIC"):
  # TODO: change this once UOps is ready to replace symbolic
  def _uop_view(view:View, idxs:List[UOp], vexpr:UOp) -> Tuple[UOp, UOp]:
    # TODO: dtypes.realint
    iexpr = variable_to_uop(view.offset)
    for idx,sh,st,m in zip(idxs, view.shape, view.strides, view.mask if view.mask is not None else [None]*len(view.shape)):
      if sh != 1 and st != 0: iexpr = iexpr + idx*variable_to_uop(st)
      if m is not None:
        if m[0] != 0: vexpr = vexpr * idx.ge(variable_to_uop(m[0]))
        if m[1] != sh: vexpr = vexpr * idx.lt(variable_to_uop(m[1]))
    return iexpr, vexpr

  def st_to_uops(st:ShapeTracker, idxs:List[UOp]) -> Tuple[UOp, UOp]:
    idx, valid = _uop_view(st.views[-1], idxs, UOp.const(dtypes.bool, True))
    for view in reversed(st.views[0:-1]):
      view = view.minify()
      acc, idxs = 1, []
      for _d in reversed(view.shape):
        d = variable_to_uop(_d)
        idxs.append((idx//acc)%d)
        acc *= d
      idx, valid = _uop_view(view, idxs[::-1], valid)
    return idx, valid
else:
  def st_to_uops(st:ShapeTracker, idxs:List[UOp]) -> Tuple[UOp, UOp]:
    fake_idxs = [Variable(f"__idx{i}", 0, s-1) for i,s in enumerate(st.shape)]
    idx, valid = st.expr_idxs(fake_idxs)
    ctx = dict(zip(fake_idxs, idxs))
    uidx, uvalid = idx.render(render_ops, ctx), valid.render(render_ops, ctx)
    if uvalid.op is UOps.CONST: uvalid = UOp.const(dtypes.bool, uvalid.arg)
    assert uvalid.dtype == dtypes.bool
    return uidx, uvalid

def get_grouped_dims(prefix, dims, max_sizes:Optional[Tuple[int, ...]]) -> List[UOp]:
  # TODO: this should be per dim max
  maxdim = len(max_sizes) if max_sizes is not None else 0
  local_idxs = [UOp(UOps.SPECIAL, dtypes.bigint, (),
    (i, f"{prefix}{i}", s)) for i,s in enumerate((prod(dims[:-(maxdim-1)]),) + dims[-(maxdim-1):] if len(dims) > maxdim else dims)]
  if maxdim != 0 and len(dims) > maxdim:
    dd = local_idxs[0]
    nli = []
    for s in dims[:-(maxdim-1)]:
      nli.append(dd % s)
      dd //= s
    local_idxs = nli + local_idxs[-(maxdim-1):]
  return local_idxs

class IndependentLowerer:
  def lower(self, ast:LazyOp, opts:Renderer) -> UOp:
    self.output_count = len(ast.src)

    ki = ast.arg if isinstance(ast.arg, KernelInfo) else KernelInfo()
    # NOTE: assumes the shape is <global dims> <local dims> <group_for_reduces> <reduces> <upcasts/unrolls>
    full_shape = ast.full_shape
    first_upcasted = len(full_shape)-ki.upcasted
    # if there's no reduce, this is first_upcasted
    first_reduce = [x!=y for x,y in zip(ast.src[0].arg.st.shape[:first_upcasted]+(0,), full_shape[:first_upcasted]+(1,))].index(True)
    local_loads = [x for x in ast.lazyops if x.op is BufferOps.LOAD and x.arg.idx == -1]
    # NOTE: this is taking the first one...there may be subtlelies here with multireduces
    group_for_reduces = sum([x!=y for x,y in zip(
      local_loads[0].arg.st.shape[first_reduce:first_upcasted], ast.src[0].arg.st.shape[first_reduce:first_upcasted])]) if local_loads else 0
    global_dims = first_reduce-ki.local_dims

    if opts.has_local:
      # define indexes for GPU-like execution
      self.idxs = get_grouped_dims("gidx", full_shape[:global_dims], opts.global_max) + \
                  get_grouped_dims("lidx", full_shape[global_dims:first_reduce+group_for_reduces], opts.local_max)
    else:
      # all loops are RANGES
      self.idxs = [UOp(UOps.RANGE, dtypes.bigint, (UOp.const(dtypes.bigint, 0), variable_to_uop(g)), (i, False))
                   for i,g in enumerate(full_shape[:first_reduce])]

    # reduce loops
    self.idxs += [UOp(UOps.RANGE, dtypes.bigint, (UOp.const(dtypes.bigint, 0), variable_to_uop(g)), (i, True))
      for i,g in enumerate(full_shape[first_reduce+group_for_reduces:first_upcasted], start=first_reduce+group_for_reduces)]

    # upcast loops
    for i,g in enumerate(full_shape[first_upcasted:], start=first_upcasted):
      assert isinstance(g, int), "needs to be int to upcast/unroll"
      self.idxs.append(UOp(UOps.EXPAND, dtypes.bigint, tuple(UOp.const(dtypes.bigint, j) for j in range(0, g)), ((i,g),)))

    # late indexes (group for reduce)
    self.ridxs = self.idxs[:]
    for a in range(first_reduce, first_reduce+group_for_reduces):
      self.ridxs[a] = UOp(UOps.RANGE, dtypes.bigint, (UOp.const(dtypes.bigint, 0), variable_to_uop(full_shape[a])), (1000+a, True))

    self.uop_cache: Dict[LazyOp, UOp] = {}
    return self.to_uop(ast)

  def to_uop(self, x:LazyOp) -> UOp:
    if uop:=self.uop_cache.get(x, None): return uop
    ret = self._to_uop(x)
    self.uop_cache[x] = ret
    return ret

  def _to_uop(self, x:LazyOp) -> UOp:
    if x.op in BufferOps:
      idx, valid = st_to_uops(x.arg.st, self.ridxs if x.op is BufferOps.LOAD and x.arg.idx == -1 else self.idxs)
      # TODO: check has_valid in UPat, not here
      has_valid = valid.op is not UOps.CONST or valid.arg is not True
      if x.op is BufferOps.CONST:
        dtype = x.arg.dtype.base if isinstance(x.arg.dtype, ImageDType) else x.arg.dtype
        return UOp.alu(TernaryOps.WHERE, valid, UOp.const(dtype, x.arg.val), UOp.const(dtype, 0))
      if x.arg.idx == -1:
        buf = UOp(UOps.DEFINE_LOCAL, PtrDType(x.arg.dtype.base if isinstance(x.arg.dtype, ImageDType) else x.arg.dtype), (), ("temp", x.arg.st.size))
      else:
        buf = UOp(UOps.DEFINE_GLOBAL, x.arg.dtype if isinstance(x.arg.dtype, ImageDType) else PtrDType(x.arg.dtype), (),
                  (x.arg.idx, x.arg.idx < self.output_count))
      if x.op is BufferOps.LOAD:
        barrier = (UOp(UOps.BARRIER, None, (self.to_uop(x.src[0]),)),) if len(x.src) else ()
        return UOp(UOps.LOAD, x.arg.dtype.scalar(), (buf, idx) + ((valid, UOp.const(x.arg.dtype.scalar(), 0)) if has_valid else ()) + barrier)
      # NOTE: only store the local reduceop in the first thread
      if x.arg.idx != -1:
        has_valid = True
        for oidx, ridx in zip(self.idxs, self.ridxs):
          if oidx != ridx: valid = valid * oidx.eq(0)
      return UOp(UOps.STORE, None, (buf, idx, self.to_uop(x.src[0])) + ((valid,) if has_valid else ()))

    in_uops = tuple(self.to_uop(y) for y in x.src)
    if x.op is MetaOps.KERNEL: return UOp(UOps.SINK, src=in_uops)
    if x.op is UnaryOps.CAST: return UOp(UOps.CAST, x.arg.scalar(), in_uops)
    if x.op is UnaryOps.BITCAST: return UOp(UOps.BITCAST, x.arg.scalar(), in_uops)
    if x.op in ReduceOps:
      dtype = x.dtype.base if isinstance(x.dtype, ImageDType) else x.dtype
      if x.op is ReduceOps.WMMA:
        wmma_sz, upcast_axis = x.arg[4], x.arg[6]
        ret = UOp(UOps.WMMA, dtype=dtype.vec(wmma_sz[2]), src=(
          UOp(UOps.CONTRACT, dtype=cast(DType, in_uops[0].dtype).vec(wmma_sz[0]), src=(in_uops[0],), arg=(upcast_axis[0],)),
          UOp(UOps.CONTRACT, dtype=cast(DType, in_uops[1].dtype).vec(wmma_sz[1]), src=(in_uops[1],), arg=(upcast_axis[1],)),
          UOp.const(dtype.vec(wmma_sz[2]), 0.0)), arg=x.arg)
        return UOp(UOps.EXPAND, dtype, tuple(UOp(UOps.GEP, dtype, (ret,), i) for i in range(wmma_sz[2])), arg=((upcast_axis[2], wmma_sz[2]),))
      # NOTE: always using ridxs is fine here
      return UOp(UOps.REDUCE, dtype, (in_uops[0],) + tuple(self.ridxs[i] for i in x.arg), x.op)
    return UOp.alu(x.op, *in_uops)

def lazyop_to_uop(ast:LazyOp, opts:Renderer) -> UOp: return IndependentLowerer().lower(ast, opts)