text: """
  The ``mlflow.fastai`` module provides an API for logging and loading fast.ai models. This module
  exports fast.ai models with the following flavors:
  
  fastai (native) format
      This is the main flavor that can be loaded back into fastai.
  :py:mod:`mlflow.pyfunc`
      Produced for use by generic pyfunc-based deployment tools and batch inference.
  
  .. _fastai.Learner:
      https://docs.fast.ai/basic_train.html#Learner
  .. _fastai.Learner.export:
      https://docs.fast.ai/basic_train.html#Learner.export
  """
  import os
  import yaml
  import gorilla
  import tempfile
  import shutil
  import pandas as pd
  import numpy as np
  
  from mlflow import pyfunc
  from mlflow.models import Model, ModelSignature, ModelInputExample
  import mlflow.tracking
  from mlflow.exceptions import MlflowException
  from mlflow.models.utils import _save_example
  from mlflow.models.model import MLMODEL_FILE_NAME
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils.environment import _mlflow_conda_env
  from mlflow.utils.model_utils import _get_flavor_configuration
  from mlflow.utils.annotations import experimental
  from mlflow.utils.autologging_utils import try_mlflow_log, log_fn_args_as_params
  
  from fastai.tabular import TabularList
  from fastai.basic_data import DatasetType
  
  
  FLAVOR_NAME = "fastai"
  
  
  def get_default_conda_env(include_cloudpickle=False):
      """
      :return: The default Conda environment for MLflow Models produced by calls to
               :func:`save_model()` and :func:`log_model()`.
      """
      import fastai
  
      pip_deps = None
      if include_cloudpickle:
          import cloudpickle
  
          pip_deps = ["cloudpickle=={}".format(cloudpickle.__version__)]
      return _mlflow_conda_env(
          additional_conda_deps=["fastai={}".format(fastai.__version__)],
          additional_pip_deps=pip_deps,
          additional_conda_channels=None,
      )
  
  
  def save_model(
      fastai_learner,
      path,
      conda_env=None,
      mlflow_model=None,
      signature: ModelSignature = None,
      input_example: ModelInputExample = None,
      **kwargs
  ):
      """
      Save a fastai Learner to a path on the local file system.
  
      :param fastai_learner: fastai Learner to be saved.
      :param path: Local path where the model is to be saved.
      :param conda_env: Either a dictionary representation of a Conda environment or the path to a
                        Conda environment yaml file. If provided, this describes the environment
                        this model should be run in. At minimum, it should specify the
                        dependencies contained in :func:`get_default_conda_env()`. If
                        ``None``, the default :func:`get_default_conda_env()` environment is
                        added to the model. The following is an *example* dictionary
                        representation of a Conda environment::
  
                          {
                              'name': 'mlflow-env',
                              'channels': ['defaults'],
                              'dependencies': [
                                  'python=3.7.0',
                                  'fastai=1.0.60',
                              ]
                          }
      :param mlflow_model: MLflow model config this flavor is being added to.
  
      :param signature: (Experimental) :py:class:`ModelSignature <mlflow.models.ModelSignature>`
                        describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
                        The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
                        from datasets with valid model input (e.g. the training dataset with target
                        column omitted) and valid model output (e.g. model predictions generated on
                        the training dataset), for example:
  
                        .. code-block:: python
  
                          from mlflow.models.signature import infer_signature
                          train = df.drop_column("target_label")
                          predictions = ... # compute model predictions
                          signature = infer_signature(train, predictions)
      :param input_example: (Experimental) Input example provides one or several instances of valid
                            model input. The example can be used as a hint of what data to feed the
                            model. The given example will be converted to a Pandas DataFrame and then
                            serialized to json using the Pandas split-oriented format. Bytes are
                            base64-encoded.
  
      :param kwargs: kwargs to pass to ``Learner.save`` method.
      """
      import fastai
      from pathlib import Path
  
      path = os.path.abspath(path)
      if os.path.exists(path):
          raise MlflowException("Path '{}' already exists".format(path))
      model_data_subpath = "model.fastai"
      model_data_path = os.path.join(path, model_data_subpath)
      model_data_path = Path(model_data_path)
      os.makedirs(path)
  
      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)
  
      # Save an Learner
      fastai_learner.export(model_data_path, **kwargs)
  
      conda_env_subpath = "conda.yaml"
  
      if conda_env is None:
          conda_env = get_default_conda_env()
      elif not isinstance(conda_env, dict):
          with open(conda_env, "r") as f:
              conda_env = yaml.safe_load(f)
      with open(os.path.join(path, conda_env_subpath), "w") as f:
          yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
  
      pyfunc.add_to_model(
          mlflow_model, loader_module="mlflow.fastai", data=model_data_subpath, env=conda_env_subpath
      )
      mlflow_model.add_flavor(FLAVOR_NAME, fastai_version=fastai.__version__, data=model_data_subpath)
      mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
  
  
  def log_model(
      fastai_learner,
      artifact_path,
      conda_env=None,
      registered_model_name=None,
      signature: ModelSignature = None,
      input_example: ModelInputExample = None,
      **kwargs
  ):
      """
      Log a fastai model as an MLflow artifact for the current run.
  
      :param fastai_learner: Fastai model (an instance of `fastai.Learner`_) to be saved.
      :param artifact_path: Run-relative artifact path.
      :param conda_env: Either a dictionary representation of a Conda environment or the path to a
                        Conda environment yaml file. If provided, this describes the environment
                        this model should be run in. At minimum, it should specify the dependencies
                        contained in :func:`get_default_conda_env()`. If ``None``, the default
                        :func:`get_default_conda_env()` environment is added to the model.
                        The following is an *example* dictionary representation of a Conda
                        environment::
  
                          {
                              'name': 'mlflow-env',
                              'channels': ['defaults'],
                              'dependencies': [
                                  'python=3.7.0',
                                  'fastai=1.0.60',
                              ]
                          }
      :param registered_model_name: Note:: Experimental: This argument may change or be removed in a
                                    future release without warning. If given, create a model
                                    version under ``registered_model_name``, also creating a
                                    registered model if one with the given name does not exist.
  
      :param signature: (Experimental) :py:class:`ModelSignature <mlflow.models.ModelSignature>`
                        describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
                        The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
                        from datasets with valid model input (e.g. the training dataset with target
                        column omitted) and valid model output (e.g. model predictions generated on
                        the training dataset), for example:
  
                        .. code-block:: python
  
                          from mlflow.models.signature import infer_signature
                          train = df.drop_column("target_label")
                          predictions = ... # compute model predictions
                          signature = infer_signature(train, predictions)
      :param input_example: (Experimental) Input example provides one or several instances of valid
                            model input. The example can be used as a hint of what data to feed the
                            model. The given example will be converted to a Pandas DataFrame and then
                            serialized to json using the Pandas split-oriented format. Bytes are
                            base64-encoded.
  
      :param kwargs: kwargs to pass to `fastai.Learner.export`_ method.
      """
      Model.log(
          artifact_path=artifact_path,
          flavor=mlflow.fastai,
          registered_model_name=registered_model_name,
          fastai_learner=fastai_learner,
          conda_env=conda_env,
          signature=signature,
          input_example=input_example,
          **kwargs
      )
  
  
  def _load_model(path):
      from fastai.basic_train import load_learner
  
      abspath = os.path.abspath(path)
      path, file = os.path.split(abspath)
      return load_learner(path, file)
  
  
  class _FastaiModelWrapper:
      def __init__(self, learner):
          self.learner = learner
  
      def predict(self, dataframe):
          test_data = TabularList.from_df(dataframe, cont_names=self.learner.data.cont_names)
          self.learner.data.add_test(test_data)
          preds, target = self.learner.get_preds(DatasetType.Test)
          preds = pd.Series(map(np.array, preds.numpy()), name="predictions")
          target = pd.Series(target.numpy(), name="target")
          return pd.concat([preds, target], axis="columns")
  
  
  def _load_pyfunc(path):
      """
      Load PyFunc implementation. Called by ``pyfunc.load_pyfunc``.
  
      :param path: Local filesystem path to the MLflow Model with the ``fastai`` flavor.
      """
      return _FastaiModelWrapper(_load_model(path))
  
  
  def load_model(model_uri):
      """
      Load a fastai model from a local file or a run.
  
      :param 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``
  
                        For more information about supported URI schemes, see
                        `Referencing Artifacts <https://www.mlflow.org/docs/latest/tracking.html#
                        artifact-locations>`_.
  
      :return: A fastai model (an instance of `fastai.Learner`_).
      """
      local_model_path = _download_artifact_from_uri(artifact_uri=model_uri)
      flavor_conf = _get_flavor_configuration(model_path=local_model_path, flavor_name=FLAVOR_NAME)
      model_file_path = os.path.join(local_model_path, flavor_conf.get("data", "model.fastai"))
      return _load_model(path=model_file_path)
  
  
  @experimental
  def autolog():
      """
      Enable automatic logging from Fastai 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.
  
      MLflow will also log the parameters of the
      `EarlyStoppingCallback <https://docs.fast.ai/callbacks.html#EarlyStoppingCallback>`_
      and `OneCycleScheduler <https://docs.fast.ai/callbacks.html#OneCycleScheduler>`_ callbacks
      """
      from fastai.basic_train import LearnerCallback, Learner
      from fastai.callbacks.hooks import model_summary, layers_info
      from fastai.callbacks import EarlyStoppingCallback, OneCycleScheduler
  
      class __MLflowFastaiCallback(LearnerCallback):
          """
          Callback for auto-logging metrics and parameters.
          Records model structural information as params when training begins
          """
  
          def __init__(
              self, learner,
          ):
              super().__init__(learner)
              self.learner = learner
              self.opt = self.learn.opt
              self.metrics_names = ["train_loss", "valid_loss"] + [
                  o.__name__ for o in learner.metrics
              ]
  
          def on_epoch_end(self, **kwargs):
              """
              Log loss and other metrics values after each epoch
              """
              if kwargs["smooth_loss"] is None or kwargs["last_metrics"] is None:
                  return
              epoch = kwargs["epoch"]
              metrics = [kwargs["smooth_loss"]] + kwargs["last_metrics"]
              metrics = map(float, metrics)
              metrics = dict(zip(self.metrics_names, metrics))
              try_mlflow_log(mlflow.log_metrics, metrics, step=epoch)
  
          def on_train_begin(self, **kwargs):
              info = layers_info(self.learner)
              try_mlflow_log(mlflow.log_param, "num_layers", len(info))
              try_mlflow_log(mlflow.log_param, "opt_func", self.opt_func.func.__name__)
  
              if hasattr(self.opt, "true_wd"):
                  try_mlflow_log(mlflow.log_param, "true_wd", self.opt.true_wd)
  
              if hasattr(self.opt, "bn_wd"):
                  try_mlflow_log(mlflow.log_param, "bn_wd", self.opt.bn_wd)
  
              if hasattr(self.opt, "train_bn"):
                  try_mlflow_log(mlflow.log_param, "train_bn", self.train_bn)
  
              summary = model_summary(self.learner)
              try_mlflow_log(mlflow.set_tag, "model_summary", summary)
  
              tempdir = tempfile.mkdtemp()
              try:
                  summary_file = os.path.join(tempdir, "model_summary.txt")
                  with open(summary_file, "w") as f:
                      f.write(summary)
                  try_mlflow_log(mlflow.log_artifact, local_path=summary_file)
              finally:
                  shutil.rmtree(tempdir)
  
          def on_train_end(self, **kwargs):
              try_mlflow_log(log_model, self.learner, artifact_path="model")
  
      def _find_callback_of_type(callback_type, callbacks):
          for callback in callbacks:
              if isinstance(callback, callback_type):
                  return callback
          return None
  
      def _log_early_stop_callback_params(callback):
          if callback:
              try:
                  earlystopping_params = {
                      "early_stop_monitor": callback.monitor,
                      "early_stop_min_delta": callback.min_delta,
                      "early_stop_patience": callback.patience,
                      "early_stop_mode": callback.mode,
                  }
                  try_mlflow_log(mlflow.log_params, earlystopping_params)
              except Exception:  # pylint: disable=W0703
                  return
  
      def _log_one_cycle_callback_params(callback):
          if callback:
              try:
                  params = {
                      "lr_max": callback.lr_max,
                      "div_factor": callback.div_factor,
                      "pct_start": callback.pct_start,
                      "final_div": callback.final_div,
                      "tot_epochs": callback.tot_epochs,
                      "start_epoch": callback.start_epoch,
                      "moms": callback.moms,
                  }
                  try_mlflow_log(mlflow.log_params, params)
              except Exception:  # pylint: disable=W0703
                  return
  
      def _run_and_log_function(self, original, args, kwargs, unlogged_params, callback_arg_index):
          if not mlflow.active_run():
              try_mlflow_log(mlflow.start_run)
              auto_end_run = True
          else:
              auto_end_run = False
  
          log_fn_args_as_params(original, [self] + list(args), kwargs, unlogged_params)
  
          callbacks = [cb(self) for cb in self.callback_fns] + (self.callbacks or [])
  
          # Checking if the 'callback' argument of the function is set
          if len(args) > callback_arg_index:
              tmp_list = list(args)
              callbacks += list(args[callback_arg_index])
              tmp_list[callback_arg_index] += [__MLflowFastaiCallback(self)]
              args = tuple(tmp_list)
          elif "callbacks" in kwargs:
              callbacks += list(kwargs["callbacks"])
              kwargs["callbacks"] += [__MLflowFastaiCallback(self)]
          else:
              kwargs["callbacks"] = [__MLflowFastaiCallback(self)]
  
          early_stop_callback = _find_callback_of_type(EarlyStoppingCallback, callbacks)
          one_cycle_callback = _find_callback_of_type(OneCycleScheduler, callbacks)
  
          _log_early_stop_callback_params(early_stop_callback)
          _log_one_cycle_callback_params(one_cycle_callback)
  
          result = original(self, *args, **kwargs)
  
          if auto_end_run:
              try_mlflow_log(mlflow.end_run)
  
          return result
  
      @gorilla.patch(Learner)
      def fit(self, *args, **kwargs):
          original = gorilla.get_original_attribute(Learner, "fit")
          unlogged_params = ["self", "callbacks", "learner"]
          return _run_and_log_function(self, original, args, kwargs, unlogged_params, 3)
  
      settings = gorilla.Settings(allow_hit=True, store_hit=True)
      gorilla.apply(gorilla.Patch(Learner, "fit", fit, settings=settings))
  