from __future__ import annotations

import builtins
import time
import inspect
import hashlib
import json
from functools import cached_property
from typing import Dict, Tuple, List, Optional

from .. import knobs
from .jit import KernelInterface, JITFunction
from .errors import OutOfResources, PTXASError, AutotunerError
from .driver import driver
from .cache import get_cache_manager, triton_key
from triton._C.libtriton import get_cache_invalidating_env_vars


class Autotuner(KernelInterface):

    def __init__(self, fn, arg_names, configs, key, reset_to_zero, restore_value, pre_hook=None, post_hook=None,
                 prune_configs_by: Optional[Dict] = None, warmup=None, rep=None, use_cuda_graph=False, do_bench=None,
                 cache_results=False):
        """
        :param prune_configs_by: a dict of functions that are used to prune configs, fields:
            'perf_model': performance model used to predicate running time with different configs, returns running time
            'top_k': number of configs to bench
            'early_config_prune': a function used to prune configs. It should have the signature
                `prune_configs_by( configs: List[triton.Config], named_args: Dict[str, Any], **kwargs: Dict[str, Any]) -> List[triton.Config]:`
                and return pruned configs. It should return at least one config.
        """
        if not configs:
            self.configs = [Config({}, num_warps=4, num_stages=3, num_ctas=1)]
        else:
            self.configs = configs
        self.keys = key
        self.cache: Dict[Tuple, Config] = {}
        self.arg_names = arg_names
        self.cache_results = (cache_results or knobs.autotuning.cache) and not knobs.runtime.interpret

        # Reset to zero or restore values
        self.reset_to_zero = []
        if reset_to_zero is not None:
            self.reset_to_zero = list(reset_to_zero)
        self.restore_value = []
        if restore_value is not None:
            self.restore_value = list(restore_value)

        # Hook to reset or restore for required tensors
        self.pre_hook = lambda kwargs, reset_only=False: 0
        self.post_hook = lambda kwargs, exception: 0
        self.user_defined_pre_hook = False
        self.user_defined_post_hook = False
        if pre_hook:
            self.pre_hook = pre_hook
            self.user_defined_pre_hook = True
        elif (len(self.reset_to_zero) > 0 or len(self.restore_value) > 0):

            def _pre_hook(kwargs, reset_only=False):
                for name in self.reset_to_zero:
                    if kwargs[name] is not None:
                        kwargs[name].zero_()
                if not reset_only:
                    self.restore_copies = {
                        name: kwargs[name].clone()
                        for name in self.restore_value
                        if kwargs[name] is not None
                    }

            self.pre_hook = _pre_hook

        if post_hook:
            self.post_hook = post_hook
            self.user_defined_post_hook = True
        elif len(self.restore_value) > 0:

            def _post_hook(kwargs, exception):
                for name, value in self.restore_copies.items():
                    kwargs[name].copy_(value)
                self.restore_copies = {}

            self.post_hook = _post_hook

        self.perf_model = None
        self.configs_top_k = 1.0
        self.early_config_prune = None
        if prune_configs_by:
            self.perf_model = prune_configs_by.get("perf_model", self.perf_model)
            self.configs_top_k = prune_configs_by.get("top_k", self.configs_top_k)
            self.early_config_prune = prune_configs_by.get("early_config_prune", self.early_config_prune)

        self.fn = fn
        self.base_fn = fn
        while not inspect.isfunction(self.base_fn):
            self.base_fn = self.base_fn.fn

        self._do_bench = do_bench
        self.num_warmups = warmup
        self.num_reps = rep
        self.use_cuda_graph = use_cuda_graph

        # If we got explicitly called via the old interface, raise a warning
        # and proceed with the old behavior.
        if warmup is not None or rep is not None or use_cuda_graph:
            import warnings
            warnings.warn(("warmup, rep, and use_cuda_graph parameters are deprecated. See "
                           "https://github.com/triton-lang/triton/pull/4496 for details."), DeprecationWarning,
                          stacklevel=1)
            if use_cuda_graph:
                from ..testing import do_bench_cudagraph
                self._do_bench = lambda kernel_call, quantiles: do_bench_cudagraph(
                    kernel_call,
                    rep=rep if rep is not None else 100,
                    quantiles=quantiles,
                )
                return

            import triton.testing
            self._do_bench = lambda kernel_call, quantiles: triton.testing.do_bench(
                kernel_call,
                warmup=warmup if warmup is not None else 25,
                rep=rep if rep is not None else 100,
                quantiles=quantiles,
            )
            return

    @cached_property
    def do_bench(self):
        if self._do_bench is None:
            return driver.active.get_benchmarker()
        return self._do_bench

    def _bench(self, *args, config, **meta):
        from ..compiler.errors import CompileTimeAssertionFailure

        verbose = knobs.autotuning.print
        if verbose:
            print(f"Autotuning kernel {self.base_fn.__name__} with config {config}")

        # 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.all_kwargs())
        full_nargs = {**self.nargs, **current}

        def kernel_call():
            if config.pre_hook:
                config.pre_hook(full_nargs)
            self.pre_hook(full_nargs)
            try:
                self.fn.run(
                    *args,
                    **current,
                )
            except Exception as e:
                try:
                    self.post_hook(full_nargs, exception=e)
                finally:
                    # Throw exception raised by `self.fn.run`
                    raise

            self.post_hook(full_nargs, exception=None)

        try:
            return self.do_bench(kernel_call, quantiles=(0.5, 0.2, 0.8))
        except (OutOfResources, CompileTimeAssertionFailure, PTXASError) as e:
            if verbose:
                print(f"Autotuning failed with {e}")
            return [float("inf"), float("inf"), float("inf")]

    def check_disk_cache(self, tuning_key, configs, bench_fn):
        # We can't serialize prehooks, so just give up and run the benchmarks.
        if not tuning_key or any(cfg.pre_hook for cfg in configs):
            bench_fn()
            return False

        from triton.compiler.compiler import make_backend

        fn = self.fn
        while not isinstance(fn, JITFunction):
            fn = fn.fn

        env_vars = get_cache_invalidating_env_vars()
        cache_key = [
            triton_key(),
            make_backend(driver.active.get_current_target()).hash(),
            fn.cache_key,
            str(sorted(env_vars.items())),
            str(tuning_key),
        ] + [str(c) for c in configs]
        cache_key = hashlib.sha256("-".join(cache_key).encode("utf-8")).hexdigest()
        cache = get_cache_manager(cache_key)
        file_name = f"{fn.__name__[:150]}.autotune.json"
        path = cache.get_file(file_name)
        if path:
            with open(path, "r") as cached_configs:
                timings = json.load(cached_configs)["configs_timings"]
                timings = {Config(**config): timing for config, timing in timings}
                self.cache[tuning_key] = builtins.min(timings, key=timings.get)
                self.configs_timings = timings
            return True

        bench_fn()
        cache.put(
            json.dumps({
                "key":
                tuning_key,
                "configs_timings":
                [(config.__dict__, timings) for config, timings in self.configs_timings.items() if not config.pre_hook],
            }), file_name, binary=False)
        return False

    def run(self, *args, **kwargs):
        self.nargs = dict(zip(self.arg_names, args))
        used_cached_result = True
        if len(self.configs) > 1:
            all_args = {**self.nargs, **kwargs}
            _args = {k: v for (k, v) in all_args.items() if k in self.arg_names}
            key = [_args[key] for key in self.keys if key in _args]
            for _, arg in _args.items():
                if hasattr(arg, "dtype"):
                    key.append(str(arg.dtype))
            key = tuple(key)
            if key not in self.cache:
                used_cached_result = False
                pruned_configs = self.prune_configs(kwargs)

                if len(pruned_configs) == 1:
                    # Match single-config autotune behavior: no benchmarking is needed.
                    self.cache[key] = pruned_configs[0]
                    used_cached_result = True
                else:

                    def benchmark():
                        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)
                        full_nargs = {**self.nargs, **kwargs, **self.cache[key].all_kwargs()}
                        self.pre_hook(full_nargs, reset_only=True)
                        self.configs_timings = timings

                    if self.cache_results:
                        used_cached_result = self.check_disk_cache(key, pruned_configs, benchmark)
                    else:
                        benchmark()

                    if knobs.autotuning.listener is not None:
                        jit_fn = self.fn
                        while not isinstance(jit_fn, JITFunction):
                            jit_fn = jit_fn.fn
                        knobs.autotuning.listener(
                            fn=jit_fn,
                            key=key,
                            best_config=self.cache[key],
                            configs_timings=self.configs_timings,
                            duration=getattr(self, 'bench_time', None) if not used_cached_result else None,
                            cache_hit=used_cached_result,
                        )

            config = self.cache[key]
        else:
            config = self.configs[0]
        self.best_config = config
        if knobs.autotuning.print and not used_cached_result:
            print(f"Triton autotuning for function {self.base_fn.__name__},\nwith key as {key},\n"
                  f"finished after {self.bench_time:.2f}s,\nbest config selected: {self.best_config};")
        if config.pre_hook is not None:
            full_nargs = {**self.nargs, **kwargs, **config.all_kwargs()}
            config.pre_hook(full_nargs)
        ret = self.fn.run(
            *args,
            **kwargs,
            **config.all_kwargs(),
        )
        self.nargs = None
        return ret

    def prune_configs(self, kwargs: Dict) -> List[Config]:
        pruned_configs = self.configs
        if self.early_config_prune:
            pruned_configs = self.early_config_prune(self.configs, self.nargs, **kwargs)
            if not pruned_configs:
                raise AutotunerError(
                    "No valid autotuner configs after pruning. `early_config_prune` should return at least one config.")
        if self.perf_model:
            top_k = self.configs_top_k
            if isinstance(top_k, float) and top_k <= 1.0:
                # Keep at least one config: a small fraction over a small config
                # set rounds down to zero, which would prune everything and crash
                # the later min() on an empty set. early_config_prune already
                # guarantees at least one config; mirror that here.
                top_k = max(1, int(len(pruned_configs) * top_k))
            elif not isinstance(top_k, int):
                # Slice index must be an integer
                raise TypeError("Error while pruning configs, top_k must be either 1) a float <= 1.0 or 2) an int")

            if len(pruned_configs) > top_k:
                est_timing = {
                    config: self.perf_model(
                        **self.nargs,
                        **kwargs,
                        **config.all_kwargs(),
                    )
                    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))
        ret = []
        for autotune_config in self.prune_configs(kwargs):
            ret.append(self.fn.warmup(
                *args,
                **kwargs,
                **autotune_config.all_kwargs(),
            ))
        self.nargs = None
        return ret


class Config:
    """
    An object that represents a possible kernel configuration for the auto-tuner to try.

    :ivar kwargs: a dictionary of meta-parameters to pass to the kernel as keyword arguments.
    :type kwargs: dict[Str, Any]
    :ivar num_warps: the number of warps to use for the kernel when compiled for GPUs. For example, if
                      `num_warps=8`, then each kernel instance will be automatically parallelized to
                      cooperatively execute using `8 * 32 = 256` threads.
    :type num_warps: int
    :ivar num_stages: the number of stages that the compiler should use when software-pipelining loops.
                       Mostly useful for matrix multiplication workloads on SM80+ GPUs.
    :type num_stages: int
    :ivar num_ctas: number of blocks in a block cluster. SM90+ only.
    :type num_ctas: int
    :type maxnreg: Optional[int]
    :ivar maxnreg: maximum number of registers one thread can use.  Corresponds
                       to ptx .maxnreg directive.  Not supported on all platforms.
    :ivar pre_hook: a function that will be called before the kernel is called. Parameters of this
                    function are args.
    :ivar ir_override: filename of a user-defined IR (*.{ttgir|llir|ptx|amdgcn}).
    """

    def __init__(self, kwargs, num_warps=4, num_stages=3, num_ctas=1, maxnreg=None, pre_hook=None, ir_override=None):
        self.kwargs = kwargs
        self.num_warps = num_warps
        self.num_ctas = num_ctas
        self.num_stages = num_stages
        self.maxnreg = maxnreg
        self.pre_hook = pre_hook
        self.ir_override = ir_override

    def __setstate__(self, state):
        self.kwargs = state.get("kwargs", {})
        self.num_warps = state.get("num_warps", 4)
        self.num_stages = state.get("num_stages", 3)
        self.num_ctas = state.get("num_ctas", 1)
        self.maxnreg = state.get("maxnreg", None)
        self.pre_hook = state.get("pre_hook", None)
        self.ir_override = state.get("ir_override", None)

    def all_kwargs(self):
        return {
            **self.kwargs, **{
                k: v
                for (k, v) in (
                    ("num_warps", self.num_warps),
                    ("num_ctas", self.num_ctas),
                    ("num_stages", self.num_stages),
                    ("maxnreg", self.maxnreg),
                    ("ir_override", self.ir_override),
                ) if v is not None
            }
        }

    def __str__(self):
        res = []
        for k, v in self.kwargs.items():
            res.append(f"{k}: {v}")
        res.append(f"num_warps: {self.num_warps}")
        res.append(f"num_ctas: {self.num_ctas}")
        res.append(f"num_stages: {self.num_stages}")
        res.append(f"maxnreg: {self.maxnreg}")
        res.append(f"ir_override: {self.ir_override}")
        return ", ".join(res)

    def __hash__(self):
        return hash((*self.all_kwargs().items(), self.pre_hook))

    def __eq__(self, other):
        self_tuple = tuple((
            *self.all_kwargs().items(),
            self.pre_hook,
        ))
        other_tuple = tuple((
            *other.all_kwargs().items(),
            other.pre_hook,
        ))
        return self_tuple == other_tuple


def autotune(configs, key, prune_configs_by=None, reset_to_zero=None, restore_value=None, pre_hook=None, post_hook=None,
             warmup=None, rep=None, use_cuda_graph=False, do_bench=None, cache_results=False):
    """
    Decorator for auto-tuning a :code:`triton.jit`'d function.

    .. highlight:: python
    .. code-block:: python

        @triton.autotune(configs=[
            triton.Config(kwargs={'BLOCK_SIZE': 128}, num_warps=4),
            triton.Config(kwargs={'BLOCK_SIZE': 1024}, num_warps=8),
          ],
          key=['x_size'] # the two above configs will be evaluated anytime
                         # the value of x_size changes
        )
        @triton.jit
        def kernel(x_ptr, x_size, BLOCK_SIZE: tl.constexpr):
            ...
    :note: When all the configurations are evaluated, the kernel will run multiple times.
           This means that whatever value the kernel updates will be updated multiple times.
           To avoid this undesired behavior, you can use the `reset_to_zero` argument, which
           resets the value of the provided tensor to `zero` before running any configuration.

    If the environment variable :code:`TRITON_PRINT_AUTOTUNING` is set to
    :code:`"1"`, Triton will print a message to stdout after autotuning each
    kernel, including the time spent autotuning and the best configuration.

    :param configs: a list of :code:`triton.Config` objects
    :type configs: list[triton.Config]
    :param key: a list of argument names whose change in value will trigger the evaluation of all provided configs.
    :type key: list[str]
    :param prune_configs_by: a dict of functions that are used to prune configs, fields:
        'perf_model': performance model used to predicate running time with different configs, returns running time
        'top_k': number of configs to bench
        'early_config_prune': a function used to prune configs. It should have the signature
                `prune_configs_by( configs: List[triton.Config], named_args: Dict[str, Any], **kwargs: Dict[str, Any]) -> List[triton.Config]:`
                and return pruned configs. It should return at least one config.
    :param reset_to_zero: a list of argument names whose value will be reset to zero before evaluating any configs.
    :type reset_to_zero: list[str]
    :param restore_value: a list of argument names whose value will be restored after evaluating any configs.
    :type restore_value: list[str]
    :param pre_hook: a function that will be called before the kernel is called.
        This overrides the default pre_hook used for 'reset_to_zero' and 'restore_value'.
        'kwargs': a dict of all arguments passed to the kernel.
        'reset_only': a boolean indicating whether the pre_hook is called to reset the values only, without a corresponding post_hook.
    :type pre_hook: lambda args, reset_only
    :param post_hook: a function that will be called after the kernel is called.
        This overrides the default post_hook used for 'restore_value'.
        'kwargs': a dict of all arguments passed to the kernel.
        'exception': the exception raised by the kernel in case of a compilation or runtime error.
    :type post_hook: lambda args, exception
    :param warmup: warmup time (in ms) to pass to benchmarking (deprecated).
    :type warmup: int
    :param rep: repetition time (in ms) to pass to benchmarking (deprecated).
    :type rep: int
    :param do_bench: a benchmark function to measure the time of each run.
    :type do_bench: lambda fn, quantiles
    :param cache_results: whether to cache autotune timings to disk.  Defaults to False.
    "type cache_results: bool
    """

    def decorator(fn):
        return Autotuner(fn, fn.arg_names, configs, key, reset_to_zero, restore_value, pre_hook=pre_hook,
                         post_hook=post_hook, prune_configs_by=prune_configs_by, warmup=warmup, rep=rep,
                         use_cuda_graph=use_cuda_graph, do_bench=do_bench, cache_results=cache_results)

    return decorator


class Heuristics(KernelInterface):

    def __init__(self, fn, arg_names, values) -> None:
        self.fn = fn
        self.values = values
        self.arg_names = arg_names

    def run(self, *args, **kwargs):
        for v, heur in self.values.items():
            kwargs[v] = heur({**dict(zip(self.arg_names, args)), **kwargs})
        return self.fn.run(*args, **kwargs)


def heuristics(values):
    """
    Decorator for specifying how the values of certain meta-parameters may be computed.
    This is useful for cases where auto-tuning is prohibitively expensive, or just not applicable.

    .. highlight:: python
    .. code-block:: python

        # smallest power-of-two >= x_size
        @triton.heuristics(values={'BLOCK_SIZE': lambda args: triton.next_power_of_2(args['x_size'])})
        @triton.jit
        def kernel(x_ptr, x_size, BLOCK_SIZE: tl.constexpr):
            ...
    :param values: a dictionary of meta-parameter names and functions that compute the value of the meta-parameter.
                   each such function takes a dict of all the arguments passed to the kernel, keyed by argument name, as input.
    :type values: dict[str, Callable[[dict[str, Any]], Any]]
    """

    def decorator(fn):
        return Heuristics(fn, fn.arg_names, values)

    return decorator
