old_contents: # Copyright 2021 The Kubeflow Authors. All Rights Reserved.
  #
  # 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.
  """Google Cloud Pipeline Experimental Components."""
  
  from .custom_job.custom_job import run_as_vertex_ai_custom_job
  from kfp.components import load_component_from_file
  from .tensorflow_probability.anomaly_detection import tfp_anomaly_detection
  
  __all__ = [
      'ForecastingPreprocessingOp',
      'ForecastingValidationOp',
  ]
  
  
  def ForecastingPreprocessingOp(**kwargs):
    """Preprocesses BigQuery tables for training or prediction.
  
    Creates a BigQuery table for training or prediction based on the input tables.
    For training, a primary table is required. Optionally, you can include some
    attribute tables. For prediction, you need to include all the tables that were
    used in the training, plus a plan table.
  
    Accepted kwargs:
        input_tables (str): Serialized Json array that specifies input BigQuery
        tables and specs.
        preprocess_metadata (str): Path to a file used by the component to save
        the output BigQuery table uri and column metadata. Do not set value to
        this arg. The value will be automatically overriden by Managed Pipeline.
  
    Args:
      **kwargs: Arbitrary keyword arguments.
  
    Returns:
      None
    """
    # TODO(yzhaozh): update the documentation with Json object reference and
    # example.
    return load_component_from_file(
        './forecasting/preprocess/component.yaml')(**kwargs)
  
  
  def ForecastingValidationOp(**kwargs):
    """Validates BigQuery tables for training or prediction.
  
    Validates BigQuery tables for training or prediction based on predefined
    requirements. For training, a primary table is required. Optionally, you
    can include some attribute tables. For prediction, you need to include all
    the tables that were used in the training, plus a plan table.
  
    Accepted kwargs:
        input_tables (str): Serialized Json array that specifies input BigQuery
        tables and specs.
  
    Args:
      **kwargs: Arbitrary keyword arguments.
  
    Returns:
      None
    """
    # TODO(yzhaozh): update the documentation with Json object reference, example
    # and predefined validation requirements.
    return load_component_from_file(
        './forecasting/validate/component.yaml')(**kwargs)
  
new_contents: # Copyright 2021 The Kubeflow Authors. All Rights Reserved.
  #
  # 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.
  """Google Cloud Pipeline Experimental Components."""
  
  import os
  from .custom_job.custom_job import run_as_vertex_ai_custom_job
  from kfp.components import load_component_from_file
  from .tensorflow_probability.anomaly_detection import tfp_anomaly_detection
  
  __all__ = [
      'ForecastingPreprocessingOp',
      'ForecastingValidationOp',
  ]
  
  
  def ForecastingPreprocessingOp(**kwargs):
    """Preprocesses BigQuery tables for training or prediction.
  
    Creates a BigQuery table for training or prediction based on the input tables.
    For training, a primary table is required. Optionally, you can include some
    attribute tables. For prediction, you need to include all the tables that were
    used in the training, plus a plan table.
  
    Accepted kwargs:
        input_tables (str): Serialized Json array that specifies input BigQuery
        tables and specs.
        preprocess_metadata (str): Path to a file used by the component to save
        the output BigQuery table uri and column metadata. Do not set value to
        this arg. The value will be automatically overriden by Managed Pipeline.
  
    Args:
      **kwargs: See Accepted kwargs.
  
    Returns:
      None
    """
    # TODO(yzhaozh): update the documentation with Json object reference and
    # example.
    return load_component_from_file(
        os.path.join(
            os.path.dirname(__file__),
            'forecasting/preprocess/component.yaml'))(**kwargs)
  
  
  def ForecastingValidationOp(**kwargs):
    """Validates BigQuery tables for training or prediction.
  
    Validates BigQuery tables for training or prediction based on predefined
    requirements. For training, a primary table is required. Optionally, you
    can include some attribute tables. For prediction, you need to include all
    the tables that were used in the training, plus a plan table.
  
    Accepted kwargs:
        input_tables (str): Serialized Json array that specifies input BigQuery
        tables and specs.
  
    Args:
      **kwargs: See Accepted kwargs.
  
    Returns:
      None
    """
    # TODO(yzhaozh): update the documentation with Json object reference, example
    # and predefined validation requirements.
    return load_component_from_file(
        os.path.join(
            os.path.dirname(__file__),
            'forecasting/validate/component.yaml'))(**kwargs)
  
subject: Fix FileNotFoundError when loading component yaml from file. (#6396)