import subprocess, time, re, hashlib, tempfile
from pathlib import Path
from typing import Optional, Tuple
import numpy as np
from pycuda.compiler import compile as cuda_compile # type: ignore
from tinygrad.helpers import DEBUG, getenv, colored, diskcache
from tinygrad.ops import Compiled
from tinygrad.runtime.lib import RawBufferCopyInOut, RawMallocBuffer, LRUAllocator
from tinygrad.codegen.kernel import LinearizerOptions
from tinygrad.renderer.cuda import CUDARenderer

def pretty_ptx(s):
  # all expressions match `<valid_before><expr><valid_after>` and replace it with `<valid_before>color(<expr>)<valid_after>`
  s = re.sub(r'([!@<\[\s,\+\-;\n])((?:[_%$][\w%\$_]+(?:\.[xyz])?\:?)|(?:buf\d+))([<>\]\s,\+\-;\n\)])', lambda m:m[1]+colored(m[2], "blue")+m[3], s, flags=re.M) # identifiers
  s = re.sub(r'(.)((?:b|s|u|f)(?:8|16|32|64)|pred)([\.\s])', lambda m:m[1]+colored(m[2], "green")+m[3], s, flags=re.M) # types
  s = re.sub(r'^(\s*)([\w]+)(.*?;$)', lambda m:m[1]+colored(m[2], "yellow")+m[3], s, flags=re.M) # instructions
  s = re.sub(r'([<>\[\]\s,\+\-;])((?:0[fF][0-9a-fA-F]{8})|(?:[0-9]+)|(?:0[xX][0-9a-fA-F]+))([<>\[\]\s,\+\-;])', lambda m:m[1]+colored(m[2], "yellow")+m[3], s, flags=re.M) # numbers
  s = re.sub(r'(\.)(param|reg|global)', lambda m:m[1]+colored(m[2], "magenta"), s, flags=re.M) # space
  s = re.sub(r'(\.)(version|target|address_size|visible|entry)', lambda m:m[1]+colored(m[2], "magenta"), s, flags=re.M) # derivatives
  return s
def arch(): return "sm_" + "".join([str(x) for x in pycuda.driver.Context.get_device().compute_capability()])

if getenv("CUDACPU", 0) == 1:
  import ctypes, ctypes.util
  lib = ctypes.CDLL(ctypes.util.find_library("gpuocelot"))
  lib.ptx_run.argtypes = [ctypes.c_char_p, ctypes.c_int, ctypes.POINTER(ctypes.c_void_p), ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int]
  class cuda:
    class module:
      def __init__(self, src): self.src = src
      def get_function(self, _): return self
      def __call__(self, *args, block, grid, shared): lib.ptx_run(self.src, len(args), (ctypes.c_void_p * len(args))(*[ctypes.cast(x, ctypes.c_void_p) for x in args]), *block, *grid, shared)
    module_from_buffer = lambda src: cuda.module(src) # pylint: disable=unnecessary-lambda # noqa: E731
    class Event:
      def __init__(self): pass
      def record(self): self.start = time.perf_counter()
      def time_till(self, other): return other.start - self.start
      def synchronize(self): pass
    class Context:
      synchronize = lambda:0 # noqa: E731
    CompileError = Exception
  class context:
    class device:
      compute_capability = lambda: (3,5) # pylint: disable=unnecessary-lambda # noqa: E731
    get_device = lambda: context.device # pylint: disable=unnecessary-lambda # noqa: E731
  import pycuda.driver # type: ignore
  pycuda.driver.Context = context
  RawCUDABuffer = RawMallocBuffer
else:
  import pycuda.autoprimaryctx # type: ignore # pylint: disable=unused-import # noqa: F401
  import pycuda.driver as cuda # type: ignore
  class CUDAAllocator(LRUAllocator):
    def _do_alloc(self, size, dtype, device, **kwargs): return cuda.mem_alloc(size * dtype.itemsize) # type: ignore
    def _cached_bufkey(self, size, dtype, device): return (device, size*dtype.itemsize) # Buffers of the same length could be reused, no matter what dtype.
  CUDAAlloc = CUDAAllocator(pycuda.driver.Context.get_device().total_memory())
  class RawCUDABuffer(RawBufferCopyInOut): # type: ignore
    def __init__(self, size, dtype): super().__init__(size, dtype, allocator=CUDAAlloc)
    def _copyin(self, x:np.ndarray, stream:Optional[cuda.Stream]=None): cuda.memcpy_htod_async(self._buf, x.ravel(), stream) # type: ignore
    def _copyout(self, x:np.ndarray): cuda.memcpy_dtoh(x, self._buf) # type: ignore

@diskcache
def compile_cuda(prg) -> bytes: return cuda_compile(prg, target="ptx", no_extern_c=True, options=['-Wno-deprecated-gpu-targets'])

class CUDAProgram:
  def __init__(self, name:str, _prg:bytes):
    prg = _prg.decode('utf-8')
    if DEBUG >= 5: print(pretty_ptx(prg))
    if DEBUG >= 6:
      try:
        fn = (Path(tempfile.gettempdir()) / f"tinycuda_{hashlib.md5(prg.encode('utf-8')).hexdigest()}").as_posix()
        with open(fn + ".ptx", "wb") as f: f.write(prg.encode('utf-8'))
        subprocess.run(["ptxas", f"-arch={arch()}", "-o", fn, fn+".ptx"], check=True)
        print(subprocess.check_output(['nvdisasm', fn]).decode('utf-8'))
      except Exception as e: print("failed to generate SASS", str(e))
    # TODO: name is wrong, so we get it from the ptx using hacks
    self.prg = cuda.module_from_buffer(prg.encode('utf-8')).get_function(prg.split(".visible .entry ")[1].split("(")[0])

  def __call__(self, *args, global_size:Tuple[int,int,int], local_size:Tuple[int,int,int], shared:int=0, wait=False):
    if wait:
      start, end = cuda.Event(), cuda.Event()
      start.record()
    self.prg(*[x._buf if isinstance(x, RawCUDABuffer) else np.int32(x) if (isinstance(x, int) and not getenv("CUDACPU")) else x for x in args], block=tuple(local_size), grid=tuple(global_size), shared=shared)
    if wait:
      end.record()
      end.synchronize()
      return start.time_till(end)*1e-3

if getenv("TRITON") == 1:
  from tinygrad.renderer.triton import uops_to_triton
  CUDABuffer = Compiled(RawCUDABuffer, LinearizerOptions(supports_float4=False, supports_float4_alu=False, global_max = [65535, 65535, 2147483647], local_max = [64, 1024, 1024], has_shared=False),
                        uops_to_triton, lambda x: x.encode('utf-8'), CUDAProgram, cuda.Context.synchronize)
else:
  CUDABuffer = Compiled(RawCUDABuffer, LinearizerOptions(supports_float4=False if getenv("PTX") else True, supports_float4_alu=False, global_max = [65535, 65535, 2147483647], local_max = [64, 1024, 1024]),
                        CUDARenderer, compile_cuda, CUDAProgram, cuda.Context.synchronize)