from typing import Dict, List, Final, Callable, DefaultDict
from collections import defaultdict
from tinygrad.ops import UnaryOps, BinaryOps, TernaryOps, Op
from tinygrad.helpers import DType, PtrDType, dtypes, ImageDType, DEBUG, getenv
from tinygrad.codegen.kernel import  UOp, Ops
from triton.compiler import compile as triton_compile
import linecache
import math
import re

triton_dtypes = {dtypes.double: "tl.float64", dtypes.float32: "tl.float32", dtypes.float16: "tl.float16", dtypes.bool: "tl.int1", dtypes.int8: "tl.int8", dtypes.uint8: "tl.uint8", dtypes.int32: "tl.int32", dtypes.int64: "tl.int64", dtypes.uint32: "tl.uint32", dtypes.uint64: "tl.uint64", dtypes.int16: "tl.int16", dtypes.uint16: "tl.uint16"}
signature_dtypes = {dtypes.double: "fp64",dtypes.float32: "fp32", dtypes.float16: "fp16", dtypes.bool: "i8", dtypes.int8: "i1", dtypes.uint8: "u8", dtypes.int32: "i32", dtypes.int64: "i64", dtypes.uint32: "u32", dtypes.uint64: "u64", dtypes.int16: "i16", dtypes.uint16: "u16"}

def next_power_of_2(x):
  return 1 << (x - 1).bit_length()

def render_valid(valid):
  return '(' * (len(valid) -1) + ') and '.join(valid) if len(valid) else 'True'

#NOTE Triton requires matching dimensions for load/store, disable this and see TestOps::test_output_padded_conv_transpose2d fail to compile
def fill_dims_for_idx(idx, dims):
  return "(" + idx + "+ (" + (f"0*({'+'.join(d for d in dims)})))") if len(dims) else idx

def get_max(var):
  if isinstance(var, int): return var
  return re.sub(r'\[(.*?)\]', '', str(var))[1:-1]

#NOTE can be removed after https://github.com/gpuocelot/gpuocelot/issues/8 gets resolved
def remove_single_scalar_curly_braces(ptx_code):
  return '\n'.join([re.sub(r'\{\s*(%\w+)\s*\}', r'\1', line) for line in ptx_code.split('\n')])

def render_const(args,dtype:DType):
  return (('-' if args<0 else '') + 'tl.where(1,float("inf"),0)') if math.isinf(args) else ('tl.where(1,float("nan"),0)' if math.isnan(args) else f"{int(args)}" if dtypes.is_int(dtype) else str(args))

def render_cast(x:str, dtype:DType, bitcast=False):
  return f"{x}.to({triton_dtypes[dtype]}, bitcast={bitcast})"

def define_scalar(local_size, dtype, args):
  if len(local_size) > 0: return f"tl.full(({','.join([str(next_power_of_2(x)) for x in local_size])},),{render_const(args,dtype)}, dtype={triton_dtypes[dtype]})"
  return render_const(args,dtype)

def uops_to_triton(function_name:str, uops:List[UOp]):
  local_size: List[int] = []
  depth = 1
  signatures, dims, bufs, kernel, valid = [], [], [], [], [] #type: ignore

  c: DefaultDict[str, int] = defaultdict(int)
  r: Dict[UOp, str] = {}
  def ssa(u, prefix="t"):
    nonlocal c, r
    c[prefix] += 1
    r[u]=f"{prefix}{c[prefix]-1}"
    return r[u]

  child_count: DefaultDict[UOp, int] = defaultdict(int)
  for ru in uops:
    for v in ru.vin:
      child_count[v] += 1

  def kk(s): kernel.append("  "*depth+s)
  code_for_op: Final[Dict[Op, Callable]] = {
    UnaryOps.EXP2: lambda x,dtype,: f"tl.math.exp2({x})",
    UnaryOps.LOG2: lambda x,dtype,: f"tl.math.log2({x})",
    UnaryOps.SIN: lambda x,dtype: f"tl.sin({x})",
    UnaryOps.SQRT: lambda x,dtype: f"tl.sqrt({x})",
    UnaryOps.NEG: lambda x,dtype: f"-{x}",
    BinaryOps.ADD: lambda x,y,dtype: f"({x}+{y})", BinaryOps.SUB: lambda x,y,: f"({x}-{y})",
    BinaryOps.MUL: lambda x,y,dtype: f"({x}*{y})", BinaryOps.DIV: lambda x,y,: f"({x}/{y})" if y != '0.0' else f"{x}*tl.where({x}==0.0, float('nan'), float('inf'))",
    BinaryOps.MAX: lambda x,y,dtype: f"tl.maximum({x},{y})",
    BinaryOps.CMPLT: lambda x,y,dtype: f"({x}<{y})",
    BinaryOps.MOD: lambda x,y,dtype: f"tl.abs({x})%tl.abs({y})*tl.where({x}<0,-1,1)",
    TernaryOps.MULACC: lambda x,y,z,dtype: f"(({x}*{y})+{z})",
    TernaryOps.WHERE: lambda x,y,z,dtype: f"tl.where({x},{y},{z})",
  }
  def int_div(x,y): return f"({x}//{y})" if y != '0' else f"{x}*tl.where({x}==0, float('nan'), float('inf'))"
  for u in uops:
    uop,dtype,vin,args = u.uop,u.dtype,u.vin,u.arg
    if uop == Ops.LOOP:
      kk(f"for {ssa(u, 'ridx')} in range({vin[0].arg}, {r[vin[1]]}):")
      depth += 1
    elif uop == Ops.END: depth -= 1
    elif uop == Ops.ALU:
      assert dtype is not None
      val = code_for_op[args](*[r[x] for x in vin])
      if child_count[u] <=1 or dtypes.is_int(dtype): r[u] = int_div(*[r[x] for x in vin]) if args == BinaryOps.DIV and dtypes.is_int(dtype) else val
      else: kk(f"{ssa(u, 'alu')} = ({val})")
    elif uop == Ops.LOAD:
      assert dtype is not None
      if len(vin) == 2: kk(f"{ssa(u, 'val')} = {render_cast(f'tl.load({r[vin[0]]} + { fill_dims_for_idx(r[vin[1]], dims)}, mask = {render_valid(valid)})', dtype)}")
      else: kk(f"{ssa(u, 'val')} = {render_cast(f'tl.where({r[vin[2]]}, tl.load({r[vin[0]]}+{fill_dims_for_idx(r[vin[1]],dims)} , mask={render_valid(valid+[r[vin[2]]])}), 0.0)', dtype)}")
    elif uop == Ops.DEFINE_ACC: kk(f"{ssa(u, 'acc')} = {define_scalar(local_size, dtype, args).replace('//', '/')}")
    elif uop == Ops.CONST: r[u] = define_scalar([], dtype, args)
    elif uop == Ops.ASSIGN:
      kk(f"{r[vin[0]]} = {r[vin[1]].replace('//', '/')}")
      r[u] = r[vin[0]]
    elif uop == Ops.STORE:
      assert not isinstance(dtype, ImageDType), "unimplemented: image store"
      kk(f"{'if '+r[vin[3]]+': ' if len(vin)>3 else ''}tl.store({r[vin[0]]} + {r[vin[1]]}, {r[vin[2]].replace('//', '/')}, mask = {render_valid(valid)}) ")
    elif uop == Ops.DEFINE_GLOBAL:
      bufs.append(args)
      signatures.append("*" if isinstance(dtype, PtrDType) else "" +  signature_dtypes[dtype])
      r[u] = args
    elif uop == Ops.SPECIAL:
      dims.append(args[1])
      valid.append(f"{args[1]}<{get_max(args[2])}")
      if args[1].startswith("g"): kk(f"{args[1]} = tl.program_id({args[0]}) # {args[2]}")
      elif args[1].startswith("l"):
        kk(f"{args[1]} = tl.arange({0}, {next_power_of_2(args[2])})")
        local_size.append(args[2])
      r[u] = args[1]
    elif uop == Ops.CAST and dtype is not None: r[u] = render_cast(r[vin[0]], dtype, isinstance(args, tuple) and args[1])
    else: raise NotImplementedError(f"unimplemented: {uop}")

  prg = f"import triton\nimport triton.language as tl\ntl.core.TRITON_MAX_TENSOR_NUMEL = float('inf')\n@triton.jit\ndef {function_name}("+','.join(bufs)+"):\n"
  for i, line in enumerate(list(filter(lambda line: "tl.arange" in line, kernel))): kernel[kernel.index(line)] +=  f"[{', '.join([':' if i == j else 'None' for j in range(len(local_size))])}]"
  prg += "\n".join(kernel)

  acc_local_size = 1
  for x in local_size: acc_local_size *= next_power_of_2(x)
  local_size = [acc_local_size] + [1] * (len(local_size) - 1)

  if DEBUG >= 4: print(prg)
  getlines = linecache.getlines
  linecache.getlines = lambda filename, module_globals=None: prg.splitlines(keepends=True) if "<triton>" == filename else getlines(filename, module_globals)
  exec(compile(prg, "<triton>", "exec"), globals()) # pylint: disable=W0122\
  compiled = triton_compile(globals()[function_name], signature=",".join(signatures), device_type="cuda", debug=False, cc=(35 if getenv("CUDACPU", 0) else None))
  prg = remove_single_scalar_curly_braces(compiled.asm["ptx"].split(".file")[0].split(".visible .func")[0])
  max_local_size =  [int(x) for x in prg.split(".maxntid ")[1].split("\n")[0].split(", ")]
  for i in range(len(local_size)): local_size[i] = min(local_size[i], max_local_size[i])

  return prg, {"shared":compiled.metadata["shared"], "local_size":local_size + [1]*(3-len(local_size))}