"""Functions to export models into the ONNX IR format.

These models can be loaded with the ONNX library and then
converted to models which run on other deep learning frameworks.
"""
from __future__ import annotations

import contextlib
import copy
import inspect
import io
import re
import textwrap
import typing
import warnings
from typing import (
    Any,
    Callable,
    cast,
    Collection,
    Dict,
    List,
    Mapping,
    Optional,
    Sequence,
    Set,
    Tuple,
    Type,
    Union,
)

import torch
import torch._C._onnx as _C_onnx
import torch.jit._trace
import torch.serialization
from torch import _C
from torch.onnx import (  # noqa: F401
    _constants,
    _exporter_states,
    errors,
    symbolic_caffe2,
    symbolic_helper,
)
from torch.onnx._globals import GLOBALS
from torch.onnx._internal import (
    _beartype,
    diagnostics,
    jit_utils,
    onnx_proto_utils,
    registration,
)

__all__ = [
    "is_in_onnx_export",
    "select_model_mode_for_export",
    "disable_apex_o2_state_dict_hook",
    "setup_onnx_logging",
    "exporter_context",
    "export",
    "warn_on_static_input_change",
    "unpack_quantized_tensor",
    "export_to_pretty_string",
    "unconvertible_ops",
    "register_custom_op_symbolic",
    "unregister_custom_op_symbolic",
]


def is_in_onnx_export() -> bool:
    """Returns whether it is in the middle of ONNX export."""
    return GLOBALS.in_onnx_export


# TODO(justinchuby): Remove dependency to this global variable from constant_fold.cpp
# Skip check due to cannot import IValue from torch._C
_params_dict = {}  # type: ignore[var-annotated]


@contextlib.contextmanager
@_beartype.beartype
def select_model_mode_for_export(model, mode: _C_onnx.TrainingMode):
    r"""A context manager to temporarily set the training mode of ``model``
    to ``mode``, resetting it when we exit the with-block.

    Args:
        model: Same type and meaning as ``model`` arg to :func:`export`.
        mode: Same type and meaning as ``training`` arg to :func:`export`.
    """
    if not isinstance(mode, _C_onnx.TrainingMode):
        raise TypeError(
            f"'mode' should be a torch.onnx.TrainingMode enum, but got '{type(mode)}'."
        )
    originally_training: bool = False

    if hasattr(model, "training"):
        originally_training = model.training

        # ONNX opset 12 has better support for training amenable models, with updated
        # versions of the dropout and batch_norm operators
        if mode == _C_onnx.TrainingMode.TRAINING or (
            mode == _C_onnx.TrainingMode.PRESERVE and originally_training
        ):
            GLOBALS.export_training = True
            if GLOBALS.export_onnx_opset_version < 12:
                warnings.warn(
                    "You are exporting the model in training mode with onnx opset "
                    f"version {GLOBALS.export_onnx_opset_version}. "
                    "Opset versions lower than opset 12 will not be able to export "
                    "nodes such as Dropout and BatchNorm correctly."
                )
        else:
            GLOBALS.export_training = False

        GLOBALS.training_mode = mode
        if mode == _C_onnx.TrainingMode.TRAINING:
            model.train(True)
        elif mode == _C_onnx.TrainingMode.EVAL:
            model.train(False)
        # else mode == _C_onnx.TrainingMode.PRESERVE, do nothing

    try:
        yield
    finally:
        if hasattr(model, "training") and not mode == _C_onnx.TrainingMode.PRESERVE:
            model.train(originally_training)


@contextlib.contextmanager
@_beartype.beartype
def disable_apex_o2_state_dict_hook(
    model: Union[torch.nn.Module, torch.jit.ScriptFunction]
):
    # Apex O2 hook state_dict to return fp16 weights as fp32.
    # Exporter cannot identify them as same tensors.
    # Since this hook is only used by optimizer, it is safe to
    # remove this hook while exporting.
    if not isinstance(model, torch.jit.ScriptFunction):
        model_hooks = {}  # type: ignore[var-annotated]
        for module in model.modules():
            for key, hook in module._state_dict_hooks.items():
                if type(hook).__name__ == "O2StateDictHook":
                    if module not in model_hooks:
                        model_hooks[module] = {}
                    model_hooks[module][key] = hook
            if module in model_hooks:
                for key in model_hooks[module]:
                    module._state_dict_hooks.pop(key)
        try:
            yield
        finally:
            # Add the hooks back
            for module, m_map in model_hooks.items():
                for key, hook in m_map.items():
                    module._state_dict_hooks[key] = hook
    else:
        try:
            yield
        finally:
            pass


@contextlib.contextmanager
@_beartype.beartype
def setup_onnx_logging(verbose: bool):
    is_originally_enabled = torch.onnx.is_onnx_log_enabled()
    if is_originally_enabled or verbose:
        torch.onnx.enable_log()
    try:
        yield
    finally:
        if not is_originally_enabled:
            torch.onnx.disable_log()


@contextlib.contextmanager
@_beartype.beartype
def exporter_context(model, mode: _C_onnx.TrainingMode, verbose: bool):
    with select_model_mode_for_export(
        model, mode
    ) as mode_ctx, disable_apex_o2_state_dict_hook(
        model
    ) as apex_ctx, setup_onnx_logging(
        verbose
    ) as log_ctx, diagnostics.create_export_diagnostic_context() as diagnostic_ctx:
        yield (mode_ctx, apex_ctx, log_ctx, diagnostic_ctx)


@_beartype.beartype
def export(
    model: Union[torch.nn.Module, torch.jit.ScriptModule, torch.jit.ScriptFunction],
    args: Union[Tuple[Any, ...], torch.Tensor],
    f: Union[str, io.BytesIO],
    export_params: bool = True,
    verbose: bool = False,
    training: _C_onnx.TrainingMode = _C_onnx.TrainingMode.EVAL,
    input_names: Optional[Sequence[str]] = None,
    output_names: Optional[Sequence[str]] = None,
    operator_export_type: _C_onnx.OperatorExportTypes = _C_onnx.OperatorExportTypes.ONNX,
    opset_version: Optional[int] = None,
    do_constant_folding: bool = True,
    dynamic_axes: Optional[
        Union[Mapping[str, Mapping[int, str]], Mapping[str, Sequence[int]]]
    ] = None,
    keep_initializers_as_inputs: Optional[bool] = None,
    custom_opsets: Optional[Mapping[str, int]] = None,
    export_modules_as_functions: Union[bool, Collection[Type[torch.nn.Module]]] = False,
) -> None:
    r"""Exports a model into ONNX format.

    If ``model`` is not a :class:`torch.jit.ScriptModule` nor a
    :class:`torch.jit.ScriptFunction`, this runs
    ``model`` once in order to convert it to a TorchScript graph to be exported
    (the equivalent of :func:`torch.jit.trace`). Thus this has the same limited support
    for dynamic control flow as :func:`torch.jit.trace`.

    Args:
        model (:class:`torch.nn.Module`, :class:`torch.jit.ScriptModule` or :class:`torch.jit.ScriptFunction`):
            the model to be exported.
        args (tuple or torch.Tensor):

            args can be structured either as:

            1. ONLY A TUPLE OF ARGUMENTS::

                args = (x, y, z)

            The tuple should contain model inputs such that ``model(*args)`` is a valid
            invocation of the model. Any non-Tensor arguments will be hard-coded into the
            exported model; any Tensor arguments will become inputs of the exported model,
            in the order they occur in the tuple.

            2. A TENSOR::

                args = torch.Tensor([1])

            This is equivalent to a 1-ary tuple of that Tensor.

            3. A TUPLE OF ARGUMENTS ENDING WITH A DICTIONARY OF NAMED ARGUMENTS::

                args = (
                    x,
                    {
                        "y": input_y,
                        "z": input_z
                    }
                )

            All but the last element of the tuple will be passed as non-keyword arguments,
            and named arguments will be set from the last element. If a named argument is
            not present in the dictionary, it is assigned the default value, or None if a
            default value is not provided.

            .. note::
                If a dictionary is the last element of the args tuple, it will be
                interpreted as containing named arguments. In order to pass a dict as the
                last non-keyword arg, provide an empty dict as the last element of the args
                tuple. For example, instead of::

                    torch.onnx.export(
                        model,
                        (
                            x,
                            # WRONG: will be interpreted as named arguments
                            {y: z}
                        ),
                        "test.onnx.pb"
                    )

                Write::

                    torch.onnx.export(
                        model,
                        (
                            x,
                            {y: z},
                            {}
                        ),
                        "test.onnx.pb"
                    )

        f: a file-like object (such that ``f.fileno()`` returns a file descriptor)
            or a string containing a file name.  A binary protocol buffer will be written
            to this file.
        export_params (bool, default True): if True, all parameters will
            be exported. Set this to False if you want to export an untrained model.
            In this case, the exported model will first take all of its parameters
            as arguments, with the ordering as specified by ``model.state_dict().values()``
        verbose (bool, default False): if True, prints a description of the
            model being exported to stdout. In addition, the final ONNX graph will include the
            field ``doc_string``` from the exported model which mentions the source code locations
            for ``model``. If True, ONNX exporter logging will be turned on.
        training (enum, default TrainingMode.EVAL):
            * ``TrainingMode.EVAL``: export the model in inference mode.
            * ``TrainingMode.PRESERVE``: export the model in inference mode if model.training is
                False and in training mode if model.training is True.
            * ``TrainingMode.TRAINING``: export the model in training mode. Disables optimizations
                which might interfere with training.
        input_names (list of str, default empty list): names to assign to the
            input nodes of the graph, in order.
        output_names (list of str, default empty list): names to assign to the
            output nodes of the graph, in order.
        operator_export_type (enum, default OperatorExportTypes.ONNX):

            * ``OperatorExportTypes.ONNX``: Export all ops as regular ONNX ops
                (in the default opset domain).
            * ``OperatorExportTypes.ONNX_FALLTHROUGH``: Try to convert all ops
                to standard ONNX ops in the default opset domain. If unable to do so
                (e.g. because support has not been added to convert a particular torch op to ONNX),
                fall back to exporting the op into a custom opset domain without conversion. Applies
                to `custom ops <https://pytorch.org/tutorials/advanced/torch_script_custom_ops.html>`_
                as well as ATen ops. For the exported model to be usable, the runtime must support
                these non-standard ops.
            * ``OperatorExportTypes.ONNX_ATEN``: All ATen ops (in the TorchScript namespace "aten")
                are exported as ATen ops (in opset domain "org.pytorch.aten").
                `ATen <https://pytorch.org/cppdocs/#aten>`_ is PyTorch's built-in tensor library, so
                this instructs the runtime to use PyTorch's implementation of these ops.

                .. warning::

                    Models exported this way are probably runnable only by Caffe2.

                    This may be useful if the numeric differences in implementations of operators are
                    causing large differences in behavior between PyTorch and Caffe2 (which is more
                    common on untrained models).

            * ``OperatorExportTypes.ONNX_ATEN_FALLBACK``: Try to export each ATen op
                (in the TorchScript namespace "aten") as a regular ONNX op. If we are unable to do so
                (e.g. because support has not been added to convert a particular torch op to ONNX),
                fall back to exporting an ATen op. See documentation on OperatorExportTypes.ONNX_ATEN for
                context.
                For example::

                    graph(%0 : Float):
                    %3 : int = prim::Constant[value=0]()
                    # conversion unsupported
                    %4 : Float = aten::triu(%0, %3)
                    # conversion supported
                    %5 : Float = aten::mul(%4, %0)
                    return (%5)

                Assuming ``aten::triu`` is not supported in ONNX, this will be exported as::

                    graph(%0 : Float):
                    %1 : Long() = onnx::Constant[value={0}]()
                    # not converted