# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# pyre-ignore-all-errors[2,3,58]

import logging
import os
from collections import defaultdict
from typing import Any, cast, Dict, IO, Iterable, List, NamedTuple, Optional, Tuple

import numpy as np
import torch
import torch.nn as nn
from iopath.common.file_io import HTTPURLHandler, PathManager
from termcolor import colored
from torch.nn.parallel import DataParallel, DistributedDataParallel


TORCH_VERSION: Tuple[int, ...] = tuple(int(x) for x in torch.__version__.split(".")[:2])
if TORCH_VERSION >= (1, 11):
    from torch.ao import quantization
    from torch.ao.quantization import FakeQuantizeBase, ObserverBase
elif (
    TORCH_VERSION >= (1, 8)
    and hasattr(torch.quantization, "FakeQuantizeBase")
    and hasattr(torch.quantization, "ObserverBase")
):
    from torch import quantization
    from torch.quantization import FakeQuantizeBase, ObserverBase

__all__ = ["Checkpointer", "PeriodicCheckpointer"]


TORCH_VERSION: Tuple[int, ...] = tuple(int(x) for x in torch.__version__.split(".")[:2])


class _IncompatibleKeys(
    NamedTuple(
        "IncompatibleKeys",
        [
            ("missing_keys", List[str]),
            ("unexpected_keys", List[str]),
            ("incorrect_shapes", List[Tuple[str, Tuple[int], Tuple[int]]]),
        ],
    )
):
    pass


class Checkpointer:
    """
    A checkpointer that can save/load model as well as extra checkpointable
    objects.
    """

    def __init__(
        self,
        model: nn.Module,
        save_dir: str = "",
        *,
        save_to_disk: bool = True,
        **checkpointables: Any,
    ) -> None:
        """
        Args:
            model (nn.Module): model.
            save_dir (str): a directory to save and find checkpoints.
            save_to_disk (bool): if True, save checkpoint to disk, otherwise
                disable saving for this checkpointer.
            checkpointables (object): any checkpointable objects, i.e., objects
                that have the ``state_dict()`` and ``load_state_dict()`` method. For
                example, it can be used like
                `Checkpointer(model, "dir", optimizer=optimizer)`.
        """
        if isinstance(model, (DistributedDataParallel, DataParallel)):
            model = model.module
        self.model = model
        self.checkpointables: Dict[str, Any] = {}
        for k, v in checkpointables.items():
            self.add_checkpointable(k, v)
        self.logger: logging.Logger = logging.getLogger(__name__)
        self.save_dir = save_dir
        self.save_to_disk = save_to_disk
        # Default PathManager, support HTTP URLs (for backward compatibility in open source).
        # A user may want to use a different project-specific PathManager
        self.path_manager: PathManager = PathManager()
        self.path_manager.register_handler(HTTPURLHandler())

    def add_checkpointable(self, key: str, checkpointable: Any) -> None:
        """
        Add checkpointable object for this checkpointer to track.

        Args:
            key (str): the key used to save the object
            checkpointable: any object with ``state_dict()`` and
                ``load_state_dict()`` method
        """
        if key in self.checkpointables:
            raise KeyError(f"Key {key} already used in the Checkpointer")
        if not hasattr(checkpointable, "state_dict"):
            raise TypeError(
                "add_checkpointable needs an object with 'state_dict()' method."
            )
        self.checkpointables[key] = checkpointable

    def save(self, name: str, **kwargs: Any) -> None:
        """
        Dump model and checkpointables to a file.

        Args:
            name (str): name of the file.
            kwargs (dict): extra arbitrary data to save.
        """
        if not self.save_dir or not self.save_to_disk:
            return

        data = {}
        data["model"] = self.model.state_dict()
        for key, obj in self.checkpointables.items():
            data[key] = obj.state_dict()
        data.update(kwargs)

        basename = "{}.pth".format(name)
        save_file = os.path.join(self.save_dir, basename)
        assert os.path.basename(save_file) == basename, basename
        self.logger.info("Saving checkpoint to {}".format(save_file))
        with self.path_manager.open(save_file, "wb") as f:
            torch.save(data, cast(IO[bytes], f))
        self.tag_last_checkpoint(basename)

    def load(
        self, path: str, checkpointables: Optional[List[str]] = None
    ) -> Dict[str, Any]:
        """
        Load from the given checkpoint.

        Args:
            path (str): path or url to the checkpoint. If empty, will not load
                anything.
            checkpointables (list): List of checkpointable names to load. If not
                specified (None), will load all the possible checkpointables.
        Returns:
            dict:
                extra data loaded from the checkpoint that has not been
                processed. For example, those saved with
                :meth:`.save(**extra_data)`.
        """
        if not path:
            # no checkpoint provided
            self.logger.info("No checkpoint found. Initializing model from scratch")
            return {}
        self.logger.info("[Checkpointer] Loading from {} ...".format(path))
        if not os.path.isfile(path):
            path = self.path_manager.get_local_path(path)
            assert os.path.isfile(path), "Checkpoint {} not found!".format(path)

        checkpoint = self._load_file(path)
        incompatible = self._load_model(checkpoint)
        if (
            incompatible is not None
        ):  # handle some existing subclasses that returns None
            self._log_incompatible_keys(incompatible)

        for key in self.checkpointables if checkpointables is None else checkpointables:
            if key in checkpoint:
                self.logger.info("Loading {} from {} ...".format(key, path))
                obj = self.checkpointables[key]
                obj.load_state_dict(checkpoint.pop(key))

        # return any further checkpoint data
        return checkpoint

    def has_checkpoint(self) -> bool:
        """
        Returns:
            bool: whether a checkpoint exists in the target directory.
        """
        save_file = os.path.join(self.save_dir, "last_checkpoint")
        return self.path_manager.exists(save_file)

    def get_checkpoint_file(self) -> str:
        """
        Returns:
            str: The latest checkpoint file in target directory.
        """
        save_file = os.path.join(self.save_dir, "last_checkpoint")
        try:
            with self.path_manager.open(save_file, "r") as f:
                last_saved = f.read().strip()
        except IOError:
            # if file doesn't exist, maybe because it has just been
            # deleted by a separate process
            return ""
        return os.path.join(self.save_dir, last_saved)

    def get_all_checkpoint_files(self) -> List[str]:
        """
        Returns:
            list: All available checkpoint files (.pth files) in target
                directory.
        """
        all_model_checkpoints = [
            os.path.join(self.save_dir, file)
            for file in self.path_manager.ls(self.save_dir)
            if self.path_manager.isfile(os.path.join(self.save_dir, file))
            and file.endswith(".pth")
        ]
        return all_model_checkpoints

    def resume_or_load(self, path: str, *, resume: bool = True) -> Dict[str, Any]:
        """
        If `resume` is True, this method attempts to resume from the last
        checkpoint, if exists. Otherwise, load checkpoint from the given path.
        This is useful when restarting an interrupted training job.

        Args:
            path (str): path to the checkpoint.
            resume (bool): if True, resume from the last checkpoint if it exists
                and load the model together with all the checkpointables. Otherwise
                only load the model without loading any checkpointables.

        Returns:
            same as :meth:`load`.
        """
        if resume and self.has_checkpoint():
            path = self.get_checkpoint_file()
            return self.load(path)
        else:
            return self.load(path, checkpointables=[])

    def tag_last_checkpoint(self, last_filename_basename: str) -> None:
        """
        Tag the last checkpoint.

        Args:
            last_filename_basename (str): the basename of the last filename.
        """
        save_file = os.path.join(self.save_dir, "last_checkpoint")
        with self.path_manager.open(save_file, "w") as f:
            f.write(last_filename_basename)  # pyre-ignore

    def _load_file(self, f: str) -> Dict[str, Any]:
        """
        Load a checkpoint file. Can be overwritten by subclasses to support
        different formats.

        Args:
            f (str): a locally mounted file path.
        Returns:
            dict: with keys "model" and optionally others that are saved by
                the checkpointer dict["model"] must be a dict which maps strings
                to torch.Tensor or numpy arrays.
        """
        return torch.load(f, map_location=torch.device("cpu"))

    def _load_model(self, checkpoint: Any) -> _IncompatibleKeys:
        """
        Load weights from a checkpoint.

        Args:
            checkpoint (Any): checkpoint contains the weights.

        Returns:
            ``NamedTuple`` with ``missing_keys``, ``unexpected_keys``,
                and ``incorrect_shapes`` fields:
                * **missing_keys** is a list of str containing the missing keys
                * **unexpected_keys** is a list of str containing the unexpected keys
                * **incorrect_shapes** is a list of (key, shape in checkpoint, shape in model)

            This is just like the return value of
            :func:`torch.nn.Module.load_state_dict`, but with extra support
            for ``incorrect_shapes``.
        """
        checkpoint_state_dict = checkpoint.pop("model")
        self._convert_ndarray_to_tensor(checkpoint_state_dict)

        # if the state_dict comes from a model that was wrapped in a
        # DataParallel or DistributedDataParallel during serialization,
        # remove the "module" prefix before performing the matching.
        _strip_prefix_if_present(checkpoint_state_dict, "module.")

        # workaround https://github.com/pytorch/pytorch/issues/24139
        model_state_dict = self.model.state_dict()
        incorrect_shapes = []
        for k in list(checkpoint_state_dict.keys()):
            if k in model_state_dict:
                model_param = model_state_dict[k]
                # Allow mismatch for uninitialized parameters
                if TORCH_VERSION >= (1, 8) and isinstance(
                    model_param, nn.parameter.UninitializedParameter
                ):
                    continue
                shape_model = tuple(model_param.shape)
                shape_checkpoint = tuple(checkpoint_state_dict[k].shape)
                if shape_model != shape_checkpoint:

                    has_observer_base_classes = (
                        TORCH_VERSION >= (1, 8)
                        and hasattr(quantization, "ObserverBase")
                        and hasattr(quantization, "FakeQuantizeBase")
                    )
                    if has_observer_base_classes:
                        # Handle the special case of quantization per channel observers,
                        # where buffer shape mismatches are expected.
                        def _get_module_for_key(
                            model: torch.nn.Module, key: str
                        ) -> torch.nn.Module:
                            # foo.bar.param_or_buffer_name -> [foo, bar]
                            key_parts = key.split(".")[:-1]
                            cur_module = model
                            for key_part in key_parts:
                                cur_module = getattr(cur_module, key_part)
                            return cur_module

                        cls_to_skip = (
                            ObserverBase,
                            FakeQuantizeBase,
                        )
                        target_module = _get_module_for_key(self.model, k)
                        if isinstance(target_module, cls_to_skip):
                            # Do not remove modules with expected shape mismatches
                            # them from the state_dict loading. They have special logic
                            # in _load_from_state_dict to handle the mismatches.
                            continue

                    incorrect_shapes.append((k, shape_checkpoint, shape_model))
                    checkpoint_state_dict.pop(k)
        incompatible = self.model.load_state_dict(checkpoint_state_dict, strict=False)
        return _IncompatibleKeys(
            missing_keys=incompatible.missing_keys,
            unexpected_keys=incompatible.unexpected_keys,
            incorrect_shapes=incorrect_shapes,
        )

    def _log_incompatible_keys(self, incompatible: _IncompatibleKeys) -> None:
        """
        Log information about the incompatible keys returned by ``_load_model``.
        """
        for k, shape_checkpoint, shape_model in incompatible.incorrect_shapes:
            self.logger.warning(
                "Skip loading parameter '{}' to the model due to incompatible "
                "shapes: {} in the checkpoint but {} in the "
                "model! You might want to double check if this is expected.".format(
                    k, shape_checkpoint, shape_model
                )
            )
        if incompatible.missing_keys:
            missing_keys = _filter_reused_missing_keys(
                self.model, incompatible.missing_keys
            )
            if missing_keys:
                self.logger.warning(get_missing_parameters_message(missing_keys))
        if incompatible.unexpected_keys:
            self.logger.warning(
                get_unexpected_parameters_message(incompatible.unexpected_keys)
            )

    def _convert_ndarray_to_tensor(self, state_dict: Dict[str, Any]) -> None:
        """
        In-place convert all numpy arrays in the state_dict to torch tensor.
        Args:
            state_dict (dict): a state-dict to be loaded to the model.
                Will be modified.
        """
        # model could be an OrderedDict with _metadata attribute
        # (as returned by Pytorch's state_dict()). We should preserve these
        # properties.
        for k in list(state_dict.keys()):
            v = state_dict[k]
            if not isinstance(v, np.ndarray) and not isinstance(v, torch.Tensor):
                raise ValueError(
                    "Unsupported type found in checkpoint! {}: {}".format(k, type(v))
                )
            if not isinstance(v, torch.Tensor):
                state_dict[k] = torch.from_numpy(v)


class PeriodicCheckpointer:
    """
    Save checkpoints periodically. When `.step(iteration)` is called, it will
    execute `checkpointer.save` on the given checkpointer, if iteration is a
    multiple of period or if `max_iter` is reached.

    Attributes:
        checkpointer (Checkpointer): the underlying checkpointer object
    """

    def __init__(
        self,
        checkpointer: Checkpointer,
        period: int,
        max_iter: Optional[int] = None,
        max_to_keep: Optional[int] = None,
        file_prefix: str = "model",
    ) -> None:
        """
        Args:
            checkpointer: the checkpointer object used to save checkpoints.
            period (int): the period to save checkpoint.
            max_iter (int): maximum number of iterations. When it is reached,
                a checkpoint named "{file_prefix}_final" will be saved.
            max_to_keep (int): maximum number of most current checkpoints to keep,
                previous checkpoints will be deleted
            file_prefix (str): the prefix of checkpoint's filename
        """
        self.checkpointer = checkpointer
        self.period = int(period)
        self.max_iter = max_iter
        if max_to_keep is not None:
            assert max_to_keep > 0
        self.max_to_keep = max_to_keep
        self.recent_checkpoints: List[str] = []
        self.path_manager: PathManager = checkpointer.path_manager
        self.file_prefix = file_prefix

    def step(self, iteration: int, **kwargs: Any) -> None:
        """
        Perform the appropriate action at the given iteration.

        Args:
            iteration (int): the current iteration, ranged in [0, max_iter-1].
            kwargs (Any): extra data to save, same as in
                :meth:`Checkpointer.save`.
        """
        iteration = int(iteration)
        additional_state = {"iteration": iteration}
        additional_state.update(kwargs)

        if (iteration + 1) % self.period == 0:
            self.checkpointer.save(
                "{}_{:07d}".format(self.file_prefix, iteration), **additional_state
            )

            if self.max_to_keep is not None:
                self.recent_checkpoints.append(self.checkpointer.get_checkpoint_file())
                if len(self.recent_checkpoints) > self.max_to_keep:
                    file_to_delete = self.recent_checkpoints.pop(0)
                    if self.path_manager.exists(
                        file_to_delete
                    ) and not file_to_delete.endswith(f"{self.file_prefix}_final.pth"):
                        self.path_manager.rm(file_to_delete)

        if self.max_iter is not None:
            if iteration >= self.max_iter - 1:
                self.checkpointer.save(f"{self.file_prefix}_final", **additional_state)

    def save(self, name: str, **kwargs: Any) -> None:
        """
        Same argument as :meth:`Checkpointer.save`.
        Use this method to manually save checkpoints outside the schedule.

        Args:
            name (str): file name.
            kwargs (Any): extra data to save, same as in
                :meth:`Checkpointer.save`.
        """
        self.checkpointer.save(name, **kwargs)


def _filter_reused_missing_keys(model: nn.Module, keys: List[str]) -> List[str]:
    """
    Filter "missing keys" to not include keys that have been loaded with another name.
    """
    keyset = set(keys)
    param_to_names = defaultdict(set)  # param -> names that points to it
    for module_prefix, module in _named_modules_with_dup(model):
        for name, param in list(module.named_parameters(recurse=False)) + list(
            module.named_buffers(recurse=False)
        ):
            full_name = (module_prefix + "." if module_prefix else "") + name
            param_to_names[param].add(full_name)
    for names in param_to_names.values():
        # if one name appears missing but its alias exists, then this
        # name is not considered missing
        if any(n in keyset for n in names) and not all(n in keyset for n in names):
            [keyset.remove(n) for n in names if n in keyset]
    return list(keyset)


def get_missing_parameters_message(keys: List[str]) -> str:
    """
    Get a logging-friendly message to report parameter names (keys) that are in
    the model but not found in a checkpoint.
    Args:
        keys (list[str]): List of keys that were not found in the checkpoint.
    Returns:
        str: message.
    """
    groups = _group_checkpoint_keys(keys)
    msg_per_group = sorted(k + _group_to_str(v) for k, v in groups.items())
    msg = "Some model parameters or buffers are not found in the checkpoint:\n"
    msg += "\n".join([colored(x, "blue") for x in msg_per_group])
    return msg


def get_unexpected_parameters_message(keys: List[str]) -> str:
    """
    Get a logging-friendly message to report parameter names (keys) that are in
    the checkpoint but not found in the model.
    Args:
        keys (list[str]): List of keys that were not found in the model.
    Returns:
        str: message.
    """
    groups = _group_checkpoint_keys(keys)
    msg = "The checkpoint state_dict contains keys that are not used by the model:\n"
    msg += "\n".join(
        "  " + colored(k + _group_to_str(v), "magenta") for k, v in groups.items()
    )
    return msg


def _strip_prefix_if_present(state_dict: Dict[str, Any], prefix: str) -> None:
    """
    Strip the prefix in metadata, if any.
    Args:
        state_dict (OrderedDict): a state-dict to be loaded to the model.
        prefix (str): prefix.
    """
    keys = sorted(state_dict.keys())
    if not all(len(key) == 0 or key.startswith(prefix) for key in keys):
        return

    for key in keys:
        newkey = key[len(prefix) :]
        state_dict[newkey] = state_dict.pop(key)

    # also strip the prefix in metadata, if any..
    try:
        metadata = state_dict._metadata  # pyre-ignore
    except AttributeError:
        pass
    else:
        for key in list(metadata.keys()):
            # for the metadata dict, the key can be:
            # '': for the DDP module, which we want to remove.
            # 'module': for the actual model.
            # 'module.xx.xx': for the rest.

            if len(key) == 0:
                continue
            newkey = key[len(prefix) :]
            metadata[newkey] = metadata.pop(key)


def _group_checkpoint_keys(keys: List[str]) -> Dict[str, List[str]]:
    """
    Group keys based on common prefixes. A prefix is the string up to the final
    "." in each key.
    Args:
        keys (list[str]): list of parameter names, i.e. keys in the model
            checkpoint dict.
    Returns:
        dict[list]: keys with common prefixes are grouped into lists.
    """
    groups = defaultdict(list)
    for key in keys:
        pos = key.rfind(".")
        if pos >= 0:
            head, tail = key[:pos], [key[pos + 1 :]]
        else:
            head, tail = key, []
        groups[head].extend(tail)
    return groups


def _group_to_str(group: List[str]) -> str:
    """
    Format a group of parameter name suffixes into a loggable string.
    Args:
        group (list[str]): list of parameter name suffixes.
    Returns:
        str: formated string.
    """
    if len(group) == 0:
        return ""

    if len(group) == 1:
        return "." + group[0]

    return ".{" + ", ".join(sorted(group)) + "}"


def _named_modules_with_dup(
    model: nn.Module, prefix: str = ""
) -> Iterable[Tuple[str, nn.Module]]:
    """
    The same as `model.named_modules()`, except that it includes
    duplicated modules that have more than one name.
    """
    yield prefix, model
    for name, module in model._modules.items():
        if module is None:
            continue
        submodule_prefix = prefix + ("." if prefix else "") + name
        yield from _named_modules_with_dup(module, submodule_prefix)
