import os
from typing import Any, Dict, Optional

import numpy as np
import pandas as pd
import yaml
from packaging.version import Version

import mlflow
from mlflow import pyfunc
from mlflow.models import Model, ModelInputExample, ModelSignature
from mlflow.models.model import MLMODEL_FILE_NAME
from mlflow.models.signature import _infer_signature_from_input_example
from mlflow.models.utils import _save_example
from mlflow.tracking.artifact_utils import _download_artifact_from_uri
from mlflow.utils.annotations import deprecated
from mlflow.utils.autologging_utils import (
    autologging_integration,
    batch_metrics_logger,
    safe_patch,
)
from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring
from mlflow.utils.environment import (
    _CONDA_ENV_FILE_NAME,
    _CONSTRAINTS_FILE_NAME,
    _PYTHON_ENV_FILE_NAME,
    _REQUIREMENTS_FILE_NAME,
    _mlflow_conda_env,
    _process_conda_env,
    _process_pip_requirements,
    _PythonEnv,
    _validate_env_arguments,
)
from mlflow.utils.file_utils import write_to
from mlflow.utils.model_utils import (
    _add_code_from_conf_to_system_path,
    _get_flavor_configuration,
    _validate_and_copy_code_paths,
    _validate_and_prepare_target_save_path,
)
from mlflow.utils.requirements_utils import _get_pinned_requirement

FLAVOR_NAME = "gluon"
_MODEL_SAVE_PATH = "net"


_MODEL_DATA_PATH = "data"


@deprecated(since="2.5.0")
def load_model(model_uri, ctx, dst_path=None):
    """
    Load a Gluon model from a local file or a run.

    Args:
        model_uri: The location, in URI format, of the MLflow model. For example:

            - ``/Users/me/path/to/local/model``
            - ``relative/path/to/local/model``
            - ``s3://my_bucket/path/to/model``
            - ``runs:/<mlflow_run_id>/run-relative/path/to/model``
            - ``models:/<model_name>/<model_version>``
            - ``models:/<model_name>/<stage>``

            For more information about supported URI schemes, see
            `Referencing Artifacts <https://www.mlflow.org/docs/latest/concepts.html#
            artifact-locations>`_.
        ctx: Either CPU or GPU.
        dst_path: The local filesystem path to which to download the model artifact.
            This directory must already exist. If unspecified, a local output
            path will be created.

    Returns:
        A Gluon model instance.

    .. code-block:: python
        :caption: Example

        # Load persisted model as a Gluon model, make inferences against an NDArray
        model = mlflow.gluon.load_model("runs:/" + gluon_random_data_run.info.run_id + "/model")
        model(nd.array(np.random.rand(1000, 1, 32)))
    """
    import mxnet as mx
    from mxnet import gluon, sym

    local_model_path = _download_artifact_from_uri(artifact_uri=model_uri, output_path=dst_path)
    flavor_conf = _get_flavor_configuration(model_path=local_model_path, flavor_name=FLAVOR_NAME)
    _add_code_from_conf_to_system_path(local_model_path, flavor_conf)

    model_arch_path = os.path.join(local_model_path, "data", _MODEL_SAVE_PATH) + "-symbol.json"
    model_params_path = os.path.join(local_model_path, "data", _MODEL_SAVE_PATH) + "-0000.params"

    if Version(mx.__version__) >= Version("2.0.0"):
        return gluon.SymbolBlock.imports(
            model_arch_path, input_names=["data"], param_file=model_params_path, ctx=ctx
        )
    else:
        symbol = sym.load(model_arch_path)
        inputs = sym.var("data", dtype="float32")
        net = gluon.SymbolBlock(symbol, inputs)
        net.collect_params().load(model_params_path, ctx)
    return net


class _GluonModelWrapper:
    def __init__(self, gluon_model):
        self.gluon_model = gluon_model

    def predict(
        self,
        data,
        params: Optional[Dict[str, Any]] = None,
    ):
        """This is a docstring. Here is more info.

        Args:
            data: Either a pandas DataFrame or a numpy array containing input array values.
                If the input is a DataFrame, it will be converted to an array first by a
                `ndarray = df.values`.
            params: Additional parameters to pass to the model for inference.

                .. Note:: Experimental: This parameter may change or be removed in a future
                    release without warning.

        Returns:
            Model predictions. If the input is a pandas.DataFrame, the predictions are returned
            in a pandas.DataFrame. If the input is a numpy array, the predictions are returned
            as either a numpy.ndarray or a plain list for hybrid models.

        """
        import mxnet as mx

        if isinstance(data, pd.DataFrame):
            ndarray = mx.nd.array(data.values)
            preds = self.gluon_model(ndarray)
            if isinstance(preds, mx.ndarray.ndarray.NDArray):
                preds = preds.asnumpy()
            return pd.DataFrame(preds)
        elif isinstance(data, np.ndarray):
            if Version(mx.__version__) >= Version("2.0.0"):
                ndarray = mx.np.array(data)
            else:
                ndarray = mx.nd.array(data)
            preds = self.gluon_model(ndarray)
            if isinstance(preds, mx.ndarray.ndarray.NDArray):
                preds = preds.asnumpy()
            return preds
        else:
            raise TypeError("Input data should be pandas.DataFrame or numpy.ndarray")


def _load_pyfunc(path):
    """Load PyFunc implementation. Called by ``pyfunc.load_model``.

    Args:
        path: Local filesystem path to the MLflow Model with the ``gluon`` flavor.

    """
    import mxnet as mx

    m = load_model(path, mx.current_context())
    return _GluonModelWrapper(m)


@deprecated(since="2.5.0")
@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name="mxnet"))
def save_model(
    gluon_model,
    path,
    mlflow_model=None,
    conda_env=None,
    code_paths=None,
    signature: ModelSignature = None,
    input_example: ModelInputExample = None,
    pip_requirements=None,
    extra_pip_requirements=None,
    metadata=None,
):
    """
    Save a Gluon model to a path on the local file system.

    Args:
        gluon_model: Gluon model to be saved. Must be already hybridized.
        path: Local path where the model is to be saved.
        mlflow_model: MLflow model config this flavor is being added to.
        conda_env: {{ conda_env }}
        code_paths: {{ code_paths }}
        signature: {{ signature }}
        input_example: {{ input_example }}
        pip_requirements: {{ pip_requirements }}
        extra_pip_requirements: {{ extra_pip_requirements }}
        metadata: Custom metadata dictionary passed to the model and stored in the MLmodel file.

            .. Note:: Experimental: This parameter may change or be removed in a future
                release without warning.

    .. code-block:: python
        :caption: Example

        from mxnet.gluon import Trainer
        from mxnet.gluon.contrib import estimator
        from mxnet.gluon.loss import SoftmaxCrossEntropyLoss
        from mxnet.gluon.nn import HybridSequential
        from mxnet.metric import Accuracy
        import mlflow

        # Build, compile, and train your model
        gluon_model_path = ...
        net = HybridSequential()
        with net.name_scope():
            ...
        net.hybridize()
        net.collect_params().initialize()
        softmax_loss = SoftmaxCrossEntropyLoss()
        trainer = Trainer(net.collect_params())
        est = estimator.Estimator(
            net=net, loss=softmax_loss, metrics=Accuracy(), trainer=trainer
        )
        est.fit(train_data=train_data, epochs=100, val_data=validation_data)
        # Save the model as an MLflow Model
        mlflow.gluon.save_model(net, gluon_model_path)
    """
    import mxnet as mx

    _validate_env_arguments(conda_env, pip_requirements, extra_pip_requirements)
    path = os.path.abspath(path)
    _validate_and_prepare_target_save_path(path)
    data_subpath = _MODEL_DATA_PATH
    data_path = os.path.join(path, data_subpath)
    os.makedirs(data_path)
    code_dir_subpath = _validate_and_copy_code_paths(code_paths, path)

    if signature is None and input_example is not None:
        wrapped_model = _GluonModelWrapper(gluon_model)
        signature = _infer_signature_from_input_example(input_example, wrapped_model)
    elif signature is False:
        signature = None

    if mlflow_model is None:
        mlflow_model = Model()
    if signature is not None:
        mlflow_model.signature = signature
    if input_example is not None:
        _save_example(mlflow_model, input_example, path)
    if metadata is not None:
        mlflow_model.metadata = metadata

    # The epoch argument of the export method does not play any role in selecting
    # a specific epoch's parameters, and is there only for display purposes.
    gluon_model.export(os.path.join(data_path, _MODEL_SAVE_PATH))

    pyfunc.add_to_model(
        mlflow_model,
        loader_module="mlflow.gluon",
        conda_env=_CONDA_ENV_FILE_NAME,
        python_env=_PYTHON_ENV_FILE_NAME,
        code=code_dir_subpath,
    )
    mlflow_model.add_flavor(FLAVOR_NAME, mxnet_version=mx.__version__, code=code_dir_subpath)
    mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))

    if conda_env is None:
        if pip_requirements is None:
            default_reqs = get_default_pip_requirements()
            inferred_reqs = mlflow.models.infer_pip_requirements(
                path,
                FLAVOR_NAME,
                fallback=default_reqs,
            )
            default_reqs = sorted(set(inferred_reqs).union(default_reqs))
        else:
            default_reqs = None
        conda_env, pip_requirements, pip_constraints = _process_pip_requirements(
            default_reqs,
            pip_requirements,
            extra_pip_requirements,
        )
    else:
        conda_env, pip_requirements, pip_constraints = _process_conda_env(conda_env)

    with open(os.path.join(path, _CONDA_ENV_FILE_NAME), "w") as f:
        yaml.safe_dump(conda_env, stream=f, default_flow_style=False)

    # Save `constraints.txt` if necessary
    if pip_constraints:
        write_to(os.path.join(path, _CONSTRAINTS_FILE_NAME), "\n".join(pip_constraints))

    # Save `requirements.txt`
    write_to(os.path.join(path, _REQUIREMENTS_FILE_NAME), "\n".join(pip_requirements))

    _PythonEnv.current().to_yaml(os.path.join(path, _PYTHON_ENV_FILE_NAME))


def get_default_pip_requirements():
    """
    Returns:
        A list of default pip requirements for MLflow Models produced by this flavor.
        Calls to :func:`save_model()` and :func:`log_model()` produce a pip environment
        that, at minimum, contains these requirements.
    """
    return [_get_pinned_requirement("mxnet")]


def get_default_conda_env():
    """
    Returns:
        The default Conda environment for MLflow Models produced by calls to
        :func:`save_model()` and :func:`log_model()`.
    """
    return _mlflow_conda_env(additional_pip_deps=get_default_pip_requirements())


@deprecated(since="2.5.0")
@format_docstring(LOG_MODEL_PARAM_DOCS.format(package_name="mxnet"))
def log_model(
    gluon_model,
    artifact_path,
    conda_env=None,
    code_paths=None,
    registered_model_name=None,
    signature: ModelSignature = None,
    input_example: ModelInputExample = None,
    pip_requirements=None,
    extra_pip_requirements=None,
    metadata=None,
):
    """
    Log a Gluon model as an MLflow artifact for the current run.

    Args:
        gluon_model: Gluon model to be saved. Must be already hybridized.
        artifact_path: Run-relative artifact path.
        conda_env: {{ conda_env }}
        code_paths: {{ code_paths }}
        registered_model_name: If given, create a model version under
            ``registered_model_name``, also creating a registered model if one
            with the given name does not exist.
        signature: {{ signature }}
        input_example: {{ input_example }}
        pip_requirements: {{ pip_requirements }}
        extra_pip_requirements: {{ extra_pip_requirements }}
        metadata: Custom metadata dictionary passed to the model and stored in the MLmodel file.

            .. Note:: Experimental: This parameter may change or be removed in a future
                release without warning.

    Returns:
        A :py:class:`ModelInfo <mlflow.models.model.ModelInfo>` instance that contains the
        metadata of the logged model.

    .. code-block:: python
        :caption: Example

        from mxnet.gluon import Trainer
        from mxnet.gluon.contrib import estimator
        from mxnet.gluon.loss import SoftmaxCrossEntropyLoss
        from mxnet.gluon.nn import HybridSequential
        from mxnet.metric import Accuracy
        import mlflow

        # Build, compile, and train your model
        net = HybridSequential()
        with net.name_scope():
            ...
        net.hybridize()
        net.collect_params().initialize()
        softmax_loss = SoftmaxCrossEntropyLoss()
        trainer = Trainer(net.collect_params())
        est = estimator.Estimator(
            net=net, loss=softmax_loss, metrics=Accuracy(), trainer=trainer
        )
        # Log metrics and log the model
        with mlflow.start_run():
            est.fit(train_data=train_data, epochs=100, val_data=validation_data)
            mlflow.gluon.log_model(net, "model")
    """
    return Model.log(
        artifact_path=artifact_path,
        flavor=mlflow.gluon,
        gluon_model=gluon_model,
        conda_env=conda_env,
        code_paths=code_paths,
        registered_model_name=registered_model_name,
        signature=signature,
        input_example=input_example,
        pip_requirements=pip_requirements,
        extra_pip_requirements=extra_pip_requirements,
        metadata=metadata,
    )


@deprecated(since="2.5.0")
@autologging_integration(FLAVOR_NAME)
def autolog(
    log_models=True,
    log_datasets=True,
    disable=False,
    exclusive=False,
    disable_for_unsupported_versions=False,
    silent=False,
    registered_model_name=None,
):
    """
    Enables (or disables) and configures autologging from Gluon to MLflow.
    Logs loss and any other metrics specified in the fit
    function, and optimizer data as parameters. Model checkpoints
    are logged as artifacts to a 'models' directory.

    Args:
        log_models: If ``True``, trained models are logged as MLflow model artifacts.
            If ``False``, trained models are not logged.
        log_datasets: If ``True``, dataset information is logged to MLflow Tracking.
            If ``False``, dataset information is not logged.
        disable: If ``True``, disables the MXNet Gluon autologging integration. If ``False``,
            enables the MXNet Gluon autologging integration.
        exclusive: If ``True``, autologged content is not logged to user-created fluent runs.
            If ``False``, autologged content is logged to the active fluent run,
            which may be user-created.
        disable_for_unsupported_versions: If ``True``, disable autologging for versions of
            gluon that have not been tested against this version of the MLflow client
            or are incompatible.
        silent: If ``True``, suppress all event logs and warnings from MLflow during MXNet Gluon
            autologging. If ``False``, show all events and warnings during MXNet Gluon
            autologging.
        registered_model_name: If given, each time a model is trained, it is registered as a
            new model version of the registered model with this name.
            The registered model is created if it does not already exist.

    """

    from mxnet.gluon.contrib.estimator import Estimator

    from mlflow.gluon._autolog import __MlflowGluonCallback

    def getGluonCallback(metrics_logger):
        return __MlflowGluonCallback(log_models, metrics_logger)

    def fit(original, self, *args, **kwargs):
        # Wrap `fit` execution within a batch metrics logger context.
        run_id = mlflow.active_run().info.run_id
        with batch_metrics_logger(run_id) as metrics_logger:
            mlflowGluonCallback = getGluonCallback(metrics_logger)
            if len(args) >= 4:
                args = (*args[:3], args[3] + [mlflowGluonCallback], *args[4:])
            elif "event_handlers" in kwargs:
                kwargs["event_handlers"] += [mlflowGluonCallback]
            else:
                kwargs["event_handlers"] = [mlflowGluonCallback]
            return original(self, *args, **kwargs)

    safe_patch(FLAVOR_NAME, Estimator, "fit", fit, manage_run=True)
