from collections import defaultdict, abc
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple, cast
import inspect
import warnings

import torch
from .common import amp_definitely_not_available


__all__ = ["OptState", "GradScaler"]

class _MultiDeviceReplicator:
    """
    Lazily serves copies of a tensor to requested devices.  Copies are cached per-device.
    """
    def __init__(self, master_tensor: torch.Tensor) -> None:
        assert master_tensor.is_cuda or master_tensor.device.type == 'xla'
        self.master = master_tensor
        self._per_device_tensors: Dict[torch.device, torch.Tensor] = {}

    def get(self, device) -> torch.Tensor:
        retval = self._per_device_tensors.get(device, None)
        if retval is None:
            retval = self.master.to(device=device, non_blocking=True, copy=True)
            self._per_device_tensors[device] = retval
        return retval


# Defines default_factory for GradScaler's _per_optimizer_states defaultdict,
# as well as associated "enum" values.  Prefers defining these at top level because
# - Lambdas can't be pickled, so we don't want to supply a lambda as the factory.
# - Defining READY, UNSCALED, STEPPED and _refresh_per_optimizer_state within GradScaler
#   causes a circular reference, which we'd rather avoid.
class OptState(Enum):
    READY = 0
    UNSCALED = 1
    STEPPED = 2


def _refresh_per_optimizer_state():
    return {"stage": OptState.READY, "found_inf_per_device": {}}


class GradScaler:
    _scale: Optional[torch.Tensor]
    _grows_tracker: Optional[torch.Tensor]
    _per_optimizer_states: Dict[int, Dict[str, Any]]
    """
    An instance ``scaler`` of :class:`GradScaler` helps perform the steps of gradient scaling
    conveniently.

    * ``scaler.scale(loss)`` multiplies a given loss by ``scaler``'s current scale factor.
    * ``scaler.step(optimizer)`` safely unscales gradients and calls ``optimizer.step()``.
    * ``scaler.update()`` updates ``scaler``'s scale factor.

    Example::

        # Creates a GradScaler once at the beginning of training.
        scaler = GradScaler()

        for epoch in epochs:
            for input, target in data:
                optimizer.zero_grad()
                output = model(input)
                loss = loss_fn(output, target)

                # Scales loss.  Calls backward() on scaled loss to create scaled gradients.
                scaler.scale(loss).backward()

                # scaler.step() first unscales gradients of the optimizer's params.
                # If gradients don't contain infs/NaNs, optimizer.step() is then called,
                # otherwise, optimizer.step() is skipped.
                scaler.step(optimizer)

                # Updates the scale for next iteration.
                scaler.update()

    See the :ref:`Automatic Mixed Precision examples<amp-examples>` for usage
    (along with autocasting) in more complex cases like gradient clipping, gradient accumulation, gradient penalty,
    and multiple losses/optimizers.

    ``scaler`` dynamically estimates the scale factor each iteration.  To minimize gradient underflow,
    a large scale factor should be used.  However, ``float16`` values can "overflow" (become inf or NaN) if
    the scale factor is too large.  Therefore, the optimal scale factor is the largest factor that can be used
    without incurring inf or NaN gradient values.
    ``scaler`` approximates the optimal scale factor over time by checking the gradients for infs and NaNs during every
    ``scaler.step(optimizer)`` (or optional separate ``scaler.unscale_(optimizer)``, see :meth:`unscale_`).

    * If infs/NaNs are found, ``scaler.step(optimizer)`` skips the underlying ``optimizer.step()`` (so the params
      themselves remain uncorrupted) and ``update()`` multiplies the scale by ``backoff_factor``.

    * If no infs/NaNs are found, ``scaler.step(optimizer)`` runs the underlying ``optimizer.step()`` as usual.
      If ``growth_interval`` unskipped iterations occur consecutively, ``update()`` multiplies the scale by
      ``growth_factor``.

    The scale factor often causes infs/NaNs to appear in gradients for the first few iterations as its
    value calibrates.  ``scaler.step`` will skip the underlying ``optimizer.step()`` for these
    iterations.  After that, step skipping should occur rarely (once every few hundred or thousand iterations).

    Args:
        init_scale (float, optional, default=2.**16):  Initial scale factor.
        growth_factor (float, optional, default=2.0):  Factor by which the scale is multiplied during
            :meth:`update` if no inf/NaN gradients occur for ``growth_interval`` consecutive iterations.
        backoff_factor (float, optional, default=0.5):  Factor by which the scale is multiplied during
            :meth:`update` if inf/NaN gradients occur in an iteration.
        growth_interval (int, optional, default=2000):  Number of consecutive iterations without inf/NaN gradients
            that must occur for the scale to be multiplied by ``growth_factor``.
        enabled (bool, optional):  If ``False``, disables gradient scaling. :meth:`step` simply
            invokes the underlying ``optimizer.step()``, and other methods become no-ops.
            Default: ``True``
    """
    def __init__(self,
                 init_scale=2.**16,
                 growth_factor=2.0,
                 backoff_factor=0.5,
                 growth_interval=2000,
                 enabled=True):
        if enabled and amp_definitely_not_available():
            warnings.warn("torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.")
            self._enabled = False
        else:
            self._enabled = enabled

        if self._enabled:
            assert growth_factor > 1.0, "The growth factor must be > 1.0."
            assert backoff_factor < 1.0, "The backoff factor must be < 1.0."

            self._init_scale = init_scale
            # self._scale will be lazily initialized during the first call to scale()
            self._scale = None
            self._growth_factor = growth_factor
            self._backoff_factor = backoff_factor
            self._growth_interval = growth_interval
            self._init_growth_tracker = 0
            # self._growth_tracker will be lazily initialized during the first call to scale()
            self._growth_tracker = None
            self._per_optimizer_states = defaultdict(_refresh_per_optimizer_state)

    def _check_scale_growth_tracker(self, funcname) -> Tuple[torch.Tensor, torch.Tensor]:
        fix = "This may indicate your script did not use scaler.scale(loss or outputs) earlier in the iteration."
        assert self._scale is not None, "Attempted {} but _scale is None.  ".format(funcname) + fix
        assert self._growth_tracker is not None, "Attempted {} but _growth_tracker is None.  ".format(funcname) + fix
        return (self._scale, self._growth_tracker)

    def _lazy_init_scale_growth_tracker(self, dev):
        assert self._growth_tracker is None, "_growth_tracker initialized before _scale"
        self._scale = torch.full((1,), self._init_scale, dtype=torch.float32, device=dev)
        self._growth_tracker = torch.full((1,), self._init_growth_tracker, dtype=torch.int32, device=dev)

    def scale(self, outputs):
        """
        Multiplies ('scales') a tensor or list of tensors by the scale factor.

        Returns scaled outputs.  If this instance of :class:`GradScaler` is not enabled, outputs are returned
        unmodified.

        Args:
            outputs (Tensor or iterable of Tensors):  Outputs to scale.
        """
        if not self._enabled:
            return outputs

        # Short-circuit for the common case.
        if isinstance(outputs, torch.Tensor):
            assert outputs.is_cuda or outputs.device.type == 'xla'
            if self._scale is None:
                self._lazy_init_scale_growth_tracker(outputs.device)
            assert self._scale is not None
            return outputs * self._scale.to(device=outputs.device, non_blocking=True)

        # Invoke the more complex machinery only if we're treating multiple outputs.
        stash: List[_MultiDeviceReplicator] = []  # holds a reference that can be overwritten by apply_scale

        def apply_scale(val):
            if isinstance(val, torch.Tensor):
                assert val.is_cuda or val.device.type == 'xla'
                if len(stash) == 0:
                    if self._scale is None:
                        self._lazy_init_scale_growth_tracker(val.device)
                    assert self._scale is not None
                    stash.append(_MultiDeviceReplicator(self._scale))
                return val * stash[0].get(val.device)
            elif isinstance(val, abc.Iterable):
                iterable = map(apply_scale, val)
                if isinstance(val, (list, tuple)):
                    return type(val)(iterable)
                else:
                    return iterable
            else:
                raise ValueError("outputs must be a Tensor or an iterable of Tensors")

        return apply_scale(outputs)

    def _unscale_grads_(self, optimizer, inv_scale, found_inf, allow_fp16):
        per_device_inv_scale = _MultiDeviceReplicator(inv_scale)
        per_device_found_inf = _MultiDeviceReplicator(found_inf)

        # To set up _amp_foreach_non_finite_check_and_unscale_, split grads by device and dtype.
        # There could be hundreds of grads, so we'd like to iterate through them just once.
        # However, we don't know their devices or dtypes in advance.

        # https://stackoverflow.com/questions/5029934/defaultdict-of-defaultdict
        # Google says mypy struggles with defaultdicts type annotations.
        per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list))  # type: ignore[var-annotated]
        with torch.no_grad():
            for group in optimizer.param_groups:
                for param in group["params"]:
                    if param.grad is None:
                        continue
                    if (not allow_fp16) and param.grad.dtype == torch.float16:
                        raise ValueError("Attempting to unscale FP16 gradients.")
                    if param.grad.is_sparse:
                        # is_coalesced() == False means the sparse grad has values with duplicate indices.
                        # coalesce() deduplicates indices and adds all values that have the same index.
                        # For scaled fp16 values, there's a good chance coalescing will cause overflow,
                        # so we should check the coalesced _values().
                        if param.grad.dtype is torch.float16:
                            param.grad = param.grad.coalesce()
                        to_unscale = param.grad._values()
                    else:
                        to_unscale = param.grad

                    # TODO: is there a way to split by device and dtype without appending in the inner loop?
                    per_device_and_dtype_grads[to_unscale.device][to_unscale.dtype].append(to_unscale)

            for device, per_dtype_grads in per_device_and_dtype_grads.items():
                for grads in per_dtype_grads.values():
                    torch._amp_foreach_non_finite_check_and_unscale_(grads,
                                                                     per_device_found_inf.get(device),
                                                                     per_device_inv_scale.get(device))

        return per_device_found_inf._per_device_tensors

    def unscale_(self, optimizer):
        """
        Divides ("unscales") the optimizer's gradient tensors by the scale factor.

        :meth:`unscale_` is optional, serving cases where you need to
        :ref:`modify or inspect gradients<working-with-unscaled-gradients>`
        between the backward pass(es) and :meth:`step`.
        If :meth:`unscale_` is not called explicitly,  gradients will be unscaled  automatically during :meth:`step`.

        Simple example, using :meth:`unscale_` to enable clipping of unscaled gradients::

            ...
            scaler.scale(loss).backward()
            scaler.unscale_(optimizer)
            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
            scaler.step(optimizer)
            scaler.update()

        Args:
            optimizer (torch.optim.Optimizer):  Optimizer that owns the gradients to be unscaled.

        .. note::
            :meth:`unscale_` does not incur a CPU-GPU sync.

        .. warning::
            :meth:`unscale_` should only be called once per optimizer per :meth:`step` call,
            and only after all gradients for that optimizer's assigned parameters have been accumulated.
            Calling :meth:`unscale_` twice for a given optimizer between each :meth:`step` triggers a RuntimeError.

        .. warning::
            :meth:`unscale_` may unscale sparse gradients out of place, replacing the ``.grad`` attribute.
        """
        if not self._enabled:
            return

        self._check_scale_growth_tracker("unscale_")

        optimizer_state = self._per_optimizer_states[id(optimizer)]

        if optimizer_state["stage"] is OptState.UNSCALED:
            raise RuntimeError("unscale_() has already been called on this optimizer since the last update().")
        elif optimizer_state["stage"] is OptState.STEPPED:
            raise RuntimeError("unscale_() is being called after step().")

        # FP32 division can be imprecise for certain compile options, so we carry out the reciprocal in FP64.
        assert self._scale is not None
        inv_scale = self._scale.double().reciprocal().float()
        found_inf = torch.full((1,), 0.0, dtype=torch.float32, device=self._scale.device)

        optimizer_state["found_inf_per_device"] = self._unscale_grads_(optimizer, inv_scale, found_inf, False)
        optimizer_state["stage"] = OptState.UNSCALED

    def _maybe_opt_step(self, optimizer, optimizer_state, *args, **kwargs):
        retval = None
        if not sum(v.item() for v in optimizer_state["found_inf_per_device"].values()):
            retval = optimizer.step(*args, **kwargs)
        return retval

    def step(self, optimizer, *args, **kwargs):
        """
        :meth:`step` carries out the following two operations:

        1.  Internally invokes ``unscale_(optimizer)`` (unless :meth:`unscale_` was explicitly called for ``optimizer``
            earlier in the iteration).  As part of the :meth:`unscale_`, gradients are checked for infs/NaNs.
        2.  If no inf/NaN gradients are found, invokes ``optimizer.step()`` using the unscaled
            gradients.  Otherwise, ``optimizer.step()`` is skipped to avoid corrupting the params.

        ``*args`` and ``**kwargs`` are forwarded to ``optimizer.step()``.

        Returns the return value of ``optimizer.step(*args, **kwargs)``.

        Args:
            optimizer (torch.optim.Optimizer):  Optimizer that applies the gradients.
            args:  Any arguments.
            kwargs:  Any keyword arguments.

        .. warning::
            Closure use is not currently supported.
        """
        if (not self._enabled):
            return optimizer.step(*args, **kwargs)

        if "closure" in kwargs:
            raise RuntimeError("Closure use is not currently supported if GradScaler is enabled.")

        self._check_scale_growth_tracker("step")

        optimizer_state = self._per_optimizer_states[id(optimizer)]

        if optimizer_state["stage"] is OptState.STEPPED:
            raise RuntimeError("step() has already been called since the last update().")

        retval = None

        if (hasattr(optimizer, "_step_supports_amp_scaling") and optimizer._step_supports_amp_scaling):
            # This optimizer has customized scale-handling logic, so we can call optimizer.step() directly.
            # The contract with custom optimizers is that their step() should accept an additional,
            # optional grad_scaler kwarg.  We append self to the kwargs so the custom optimizer has full information:
            # it can query its own state, invoke unscale_ on itself, etc
            # The contract above is being deprecated to avoid introducing `grad_scaler: GradScaler` argument
            # to `Optimizer.step`. The new behavior is going to add two Tensor attributes of `grad_scale`
            # and `found_inf` to the passed optimizer so that the optimizer can utilize those
            # to skip the parameter updates or unscale gradients before updating parameters in
            # the fused kernel, e.g. `FusedAdamMathFunctor`.
            # In this behavior, `GradScaler._check_inf_per_device` is called if `OptState.READY`,
            # while the method is expected to be called by users side, i.e. their optimizers.
            kwargs_ = kwargs
            has_grad_scaler_kwarg = "grad_scaler" in inspect.signature(optimizer.step).parameters
            if has_grad_scaler_kwarg:
                warnings.warn(
                    "GradScaler is going to stop passing itself as a keyword argument to the passed "