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import builtins
import math
import time
from typing import Dict

import triton


#  code based https://github.com/fpgaminer/GPTQ-triton
"""
Mostly the same as the autotuner in Triton, but with a few changes like using 40 runs instead of 100.
"""


class CustomizedTritonAutoTuner(triton.KernelInterface):
    def __init__(
        self,
        fn,
        arg_names,
        configs,
        key,
        reset_to_zero,
        prune_configs_by: Dict = None,
        nearest_power_of_two: bool = False,
    ):
        if not configs:
            self.configs = [triton.Config({}, num_warps=4, num_stages=2)]
        else:
            self.configs = configs
        self.key_idx = [arg_names.index(k) for k in key]
        self.nearest_power_of_two = nearest_power_of_two
        self.cache = {}
        # hook to reset all required tensor to zeros before relaunching a kernel
        self.hook = lambda args: 0
        if reset_to_zero is not None:
            self.reset_idx = [arg_names.index(k) for k in reset_to_zero]

            def _hook(args):
                for i in self.reset_idx:
                    args[i].zero_()

            self.hook = _hook
        self.arg_names = arg_names
        # prune configs
        if prune_configs_by:
            perf_model, top_k = (
                prune_configs_by["perf_model"],
                prune_configs_by["top_k"],
            )
            if "early_config_prune" in prune_configs_by:
                early_config_prune = prune_configs_by["early_config_prune"]
        else:
            perf_model, top_k, early_config_prune = None, None, None
        self.perf_model, self.configs_top_k = perf_model, top_k
        self.early_config_prune = early_config_prune
        self.fn = fn

    def _bench(self, *args, config, **meta):
        # check for conflicts, i.e. meta-parameters both provided
        # as kwargs and by the autotuner
        conflicts = meta.keys() & config.kwargs.keys()
        if conflicts:
            raise ValueError(
                f"Conflicting meta-parameters: {', '.join(conflicts)}."
                " Make sure that you don't re-define auto-tuned symbols."
            )
        # augment meta-parameters with tunable ones
        current = dict(meta, **config.kwargs)

        def kernel_call():
            if config.pre_hook:
                config.pre_hook(self.nargs)
            self.hook(args)
            self.fn.run(
                *args,
                num_warps=config.num_warps,
                num_stages=config.num_stages,
                **current,
            )

        try:
            # In testings using only 40 reps seems to be close enough and it appears to be what PyTorch uses
            # PyTorch also sets fast_flush to True, but I didn't see any speedup so I'll leave the default
            return triton.testing.do_bench(kernel_call, quantiles=(0.5, 0.2, 0.8), rep=40)
        except triton.OutOfResources:
            return (float("inf"), float("inf"), float("inf"))

    def run(self, *args, **kwargs):
        self.nargs = dict(zip(self.arg_names, args))
        if len(self.configs) > 1:
            key = tuple(args[i] for i in self.key_idx)

            # This reduces the amount of autotuning by rounding the keys to the nearest power of two
            # In my testing this gives decent results, and greatly reduces the amount of tuning required
            if self.nearest_power_of_two:
                key = tuple([2 ** int(math.log2(x) + 0.5) for x in key])

            if key not in self.cache:
                # prune configs
                pruned_configs = self.prune_configs(kwargs)
                bench_start = time.time()
                timings = {config: self._bench(*args, config=config, **kwargs) for config in pruned_configs}
                bench_end = time.time()
                self.bench_time = bench_end - bench_start
                self.cache[key] = builtins.min(timings, key=timings.get)
                self.hook(args)
                self.configs_timings = timings
            config = self.cache[key]
        else:
            config = self.configs[0]
        self.best_config = config
        if config.pre_hook is not None:
            config.pre_hook(self.nargs)
        return self.fn.run(
            *args,
            num_warps=config.num_warps,
            num_stages=config.num_stages,
            **kwargs,
            **config.kwargs,
        )

    def prune_configs(self, kwargs):
        pruned_configs = self.configs
        if self.early_config_prune:
            pruned_configs = self.early_config_prune(self.configs, self.nargs)
        if self.perf_model:
            top_k = self.configs_top_k
            if isinstance(top_k, float) and top_k <= 1.0:
                top_k = int(len(self.configs) * top_k)
            if len(pruned_configs) > top_k:
                est_timing = {
                    config: self.perf_model(
                        **self.nargs,
                        **kwargs,
                        **config.kwargs,
                        num_stages=config.num_stages,
                        num_warps=config.num_warps,
                    )
                    for config in pruned_configs
                }
                pruned_configs = sorted(est_timing.keys(), key=lambda x: est_timing[x])[:top_k]
        return pruned_configs

    def warmup(self, *args, **kwargs):
        self.nargs = dict(zip(self.arg_names, args))
        for config in self.prune_configs(kwargs):
            self.fn.warmup(
                *args,
                num_warps=config.num_warps,
                num_stages=config.num_stages,
                **kwargs,
                **config.kwargs,
            )
        self.nargs = None


def autotune(configs, key, prune_configs_by=None, reset_to_zero=None, nearest_power_of_two=False):
    def decorator(fn):
        return CustomizedTritonAutoTuner(
            fn,
            fn.arg_names,
            configs,
            key,
            reset_to_zero,
            prune_configs_by,
            nearest_power_of_two,
        )

    return decorator


def matmul248_kernel_config_pruner(configs, nargs):
    """
    The main purpose of this function is to shrink BLOCK_SIZE_* when the corresponding dimension is smaller.
    """
    m = max(2 ** int(math.ceil(math.log2(nargs["M"]))), 16)
    n = max(2 ** int(math.ceil(math.log2(nargs["N"]))), 16)
    k = max(2 ** int(math.ceil(math.log2(nargs["K"]))), 16)

    used = set()
    for config in configs:
        block_size_m = min(m, config.kwargs["BLOCK_SIZE_M"])
        block_size_n = min(n, config.kwargs["BLOCK_SIZE_N"])
        block_size_k = min(k, config.kwargs["BLOCK_SIZE_K"])
        group_size_m = config.kwargs["GROUP_SIZE_M"]

        if (
            block_size_m,
            block_size_n,
            block_size_k,
            group_size_m,
            config.num_stages,
            config.num_warps,
        ) in used:
            continue

        used.add(
            (
                block_size_m,
                block_size_n,
                block_size_k,
                group_size_m,
                config.num_stages,
                config.num_warps,
            )
        )
        yield triton.Config(
            {
                "BLOCK_SIZE_M": block_size_m,
                "BLOCK_SIZE_N": block_size_n,
                "BLOCK_SIZE_K": block_size_k,
                "GROUP_SIZE_M": group_size_m,
            },
            num_stages=config.num_stages,
            num_warps=config.num_warps,
        )


__all__ = ["autotune"]
