text: # Copyright 2020 The AutoKeras Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from pathlib import Path
  from typing import Optional
  from typing import Type
  from typing import Union
  
  from autokeras import auto_model
  from autokeras import blocks
  from autokeras import nodes as input_module
  from autokeras.engine import tuner
  from autokeras.tuners import greedy
  from autokeras.tuners import task_specific
  from autokeras.utils import types
  
  
  class SupervisedTextPipeline(auto_model.AutoModel):
      def __init__(self, outputs, **kwargs):
          super().__init__(inputs=input_module.TextInput(), outputs=outputs, **kwargs)
  
  
  class TextClassifier(SupervisedTextPipeline):
      """AutoKeras text classification class.
  
      # Arguments
          num_classes: Int. Defaults to None. If None, it will be inferred from the
              data.
          multi_label: Boolean. Defaults to False.
          loss: A Keras loss function. Defaults to use 'binary_crossentropy' or
              'categorical_crossentropy' based on the number of classes.
          metrics: A list of Keras metrics. Defaults to use 'accuracy'.
          project_name: String. The name of the AutoModel.
              Defaults to 'text_classifier'.
          max_trials: Int. The maximum number of different Keras Models to try.
              The search may finish before reaching the max_trials. Defaults to 100.
          directory: String. The path to a directory for storing the search outputs.
              Defaults to None, which would create a folder with the name of the
              AutoModel in the current directory.
          objective: String. Name of model metric to minimize
              or maximize, e.g. 'val_accuracy'. Defaults to 'val_loss'.
          tuner: String or subclass of AutoTuner. If string, it should be one of
              'greedy', 'bayesian', 'hyperband' or 'random'. It can also be a subclass
              of AutoTuner. If left unspecified, it uses a task specific tuner, which
              first evaluates the most commonly used models for the task before
              exploring other models.
          overwrite: Boolean. Defaults to `False`. If `False`, reloads an existing
              project of the same name if one is found. Otherwise, overwrites the
              project.
          seed: Int. Random seed.
          max_model_size: Int. Maximum number of scalars in the parameters of a
              model. Models larger than this are rejected.
          **kwargs: Any arguments supported by AutoModel.
      """
  
      def __init__(
          self,
          num_classes: Optional[int] = None,
          multi_label: bool = False,
          loss: types.LossType = None,
          metrics: Optional[types.MetricsType] = None,
          project_name: str = "text_classifier",
          max_trials: int = 100,
          directory: Union[str, Path, None] = None,
          objective: str = "val_loss",
          tuner: Union[str, Type[tuner.AutoTuner]] = None,
          overwrite: bool = False,
          seed: Optional[int] = None,
          max_model_size: Optional[int] = None,
          **kwargs
      ):
          if tuner is None:
              tuner = task_specific.TextClassifierTuner
          super().__init__(
              outputs=blocks.ClassificationHead(
                  num_classes=num_classes,
                  multi_label=multi_label,
                  loss=loss,
                  metrics=metrics,
              ),
              max_trials=max_trials,
              directory=directory,
              project_name=project_name,
              objective=objective,
              tuner=tuner,
              overwrite=overwrite,
              seed=seed,
              max_model_size=max_model_size,
              **kwargs
          )
  
      def fit(
          self,
          x=None,
          y=None,
          epochs=None,
          callbacks=None,
          validation_split=0.2,
          validation_data=None,
          **kwargs
      ):
          """Search for the best model and hyperparameters for the AutoModel.
  
          It will search for the best model based on the performances on
          validation data.
  
          # Arguments
              x: numpy.ndarray or tensorflow.Dataset. Training data x. The input data
                  should be numpy.ndarray or tf.data.Dataset. The data should be one
                  dimensional. Each element in the data should be a string which is a
                  full sentence.
              y: numpy.ndarray or tensorflow.Dataset. Training data y. It can be raw
                  labels, one-hot encoded if more than two classes, or binary encoded
                  for binary classification.
              epochs: Int. The number of epochs to train each model during the search.
                  If unspecified, by default we train for a maximum of 1000 epochs,
                  but we stop training if the validation loss stops improving for 10
                  epochs (unless you specified an EarlyStopping callback as part of
                  the callbacks argument, in which case the EarlyStopping callback you
                  specified will determine early stopping).
              callbacks: List of Keras callbacks to apply during training and
                  validation.
              validation_split: Float between 0 and 1. Defaults to 0.2.
                  Fraction of the training data to be used as validation data.
                  The model will set apart this fraction of the training data,
                  will not train on it, and will evaluate
                  the loss and any model metrics
                  on this data at the end of each epoch.
                  The validation data is selected from the last samples
                  in the `x` and `y` data provided, before shuffling. This argument is
                  not supported when `x` is a dataset.
                  The best model found would be fit on the entire dataset including the
                  validation data.
              validation_data: Data on which to evaluate the loss and any model metrics
                  at the end of each epoch. The model will not be trained on this data.
                  `validation_data` will override `validation_split`. The type of the
                  validation data should be the same as the training data.
                  The best model found would be fit on the training dataset without the
                  validation data.
              **kwargs: Any arguments supported by
                  [keras.Model.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit).
  
          # Returns
              history: A Keras History object corresponding to the best model.
                  Its History.history attribute is a record of training
                  loss values and metrics values at successive epochs, as well as
                  validation loss values and validation metrics values (if applicable).
          """
          history = super().fit(
              x=x,
              y=y,
              epochs=epochs,
              callbacks=callbacks,
              validation_split=validation_split,
              validation_data=validation_data,
              **kwargs
          )
          return history
  
  
  class TextRegressor(SupervisedTextPipeline):
      """AutoKeras text regression class.
  
      # Arguments
          output_dim: Int. The number of output dimensions. Defaults to None.
              If None, it will be inferred from the data.
          loss: A Keras loss function. Defaults to use 'mean_squared_error'.
          metrics: A list of Keras metrics. Defaults to use 'mean_squared_error'.
          project_name: String. The name of the AutoModel.
              Defaults to 'text_regressor'.
          max_trials: Int. The maximum number of different Keras Models to try.
              The search may finish before reaching the max_trials. Defaults to 100.
          directory: String. The path to a directory for storing the search outputs.
              Defaults to None, which would create a folder with the name of the
              AutoModel in the current directory.
          objective: String. Name of model metric to minimize
              or maximize, e.g. 'val_accuracy'. Defaults to 'val_loss'.
          tuner: String or subclass of AutoTuner. If string, it should be one of
              'greedy', 'bayesian', 'hyperband' or 'random'. It can also be a subclass
              of AutoTuner. If left unspecified, it uses a task specific tuner, which
              first evaluates the most commonly used models for the task before
              exploring other models.
          overwrite: Boolean. Defaults to `False`. If `False`, reloads an existing
              project of the same name if one is found. Otherwise, overwrites the
              project.
          seed: Int. Random seed.
          max_model_size: Int. Maximum number of scalars in the parameters of a
              model. Models larger than this are rejected.
          **kwargs: Any arguments supported by AutoModel.
      """
  
      def __init__(
          self,
          output_dim: Optional[int] = None,
          loss: types.LossType = "mean_squared_error",
          metrics: Optional[types.MetricsType] = None,
          project_name: str = "text_regressor",
          max_trials: int = 100,
          directory: Union[str, Path, None] = None,
          objective: str = "val_loss",
          tuner: Union[str, Type[tuner.AutoTuner]] = None,
          overwrite: bool = False,
          seed: Optional[int] = None,
          max_model_size: Optional[int] = None,
          **kwargs
      ):
          if tuner is None:
              tuner = greedy.Greedy
          super().__init__(
              outputs=blocks.RegressionHead(
                  output_dim=output_dim, loss=loss, metrics=metrics
              ),
              max_trials=max_trials,
              directory=directory,
              project_name=project_name,
              objective=objective,
              tuner=tuner,
              overwrite=overwrite,
              seed=seed,
              max_model_size=max_model_size,
              **kwargs
          )
  
      def fit(
          self,
          x=None,
          y=None,
          epochs=None,
          callbacks=None,
          validation_split=0.2,
          validation_data=None,
          **kwargs
      ):
          """Search for the best model and hyperparameters for the AutoModel.
  
          It will search for the best model based on the performances on
          validation data.
  
          # Arguments
              x: numpy.ndarray or tensorflow.Dataset. Training data x. The input data
                  should be numpy.ndarray or tf.data.Dataset. The data should be one
                  dimensional. Each element in the data should be a string which is a
                  full sentence.
              y: numpy.ndarray or tensorflow.Dataset. Training data y. The targets
                  passing to the head would have to be tf.data.Dataset, np.ndarray,
                  pd.DataFrame or pd.Series. It can be single-column or multi-column.
                  The values should all be numerical.
              epochs: Int. The number of epochs to train each model during the search.
                  If unspecified, by default we train for a maximum of 1000 epochs,
                  but we stop training if the validation loss stops improving for 10
                  epochs (unless you specified an EarlyStopping callback as part of
                  the callbacks argument, in which case the EarlyStopping callback you
                  specified will determine early stopping).
              callbacks: List of Keras callbacks to apply during training and
                  validation.
              validation_split: Float between 0 and 1. Defaults to 0.2.
                  Fraction of the training data to be used as validation data.
                  The model will set apart this fraction of the training data,
                  will not train on it, and will evaluate
                  the loss and any model metrics
                  on this data at the end of each epoch.
                  The validation data is selected from the last samples
                  in the `x` and `y` data provided, before shuffling. This argument is
                  not supported when `x` is a dataset.
                  The best model found would be fit on the entire dataset including the
                  validation data.
              validation_data: Data on which to evaluate the loss and any model metrics
                  at the end of each epoch. The model will not be trained on this data.
                  `validation_data` will override `validation_split`. The type of the
                  validation data should be the same as the training data.
                  The best model found would be fit on the training dataset without the
                  validation data.
              **kwargs: Any arguments supported by
                  [keras.Model.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit).
          # Returns
              history: A Keras History object corresponding to the best model.
                  Its History.history attribute is a record of training
                  loss values and metrics values at successive epochs, as well as
                  validation loss values and validation metrics values (if applicable).
          """
          history = super().fit(
              x=x,
              y=y,
              epochs=epochs,
              callbacks=callbacks,
              validation_split=validation_split,
              validation_data=validation_data,
              **kwargs
          )
          return history
  