name: ExampleValidator
description: |-
  A TFX component to validate input examples.

      The ExampleValidator component uses [Tensorflow Data
      Validation](https://www.tensorflow.org/tfx/data_validation) to
      validate the statistics of some splits on input examples against a schema.

      The ExampleValidator component identifies anomalies in training and serving
      data. The component can be configured to detect different classes of anomalies
      in the data. It can:
          - perform validity checks by comparing data statistics against a schema that
          codifies expectations of the user.
          - detect data drift by looking at a series of data.
          - detect changes in dataset-wide data (i.e., num_examples) across spans or
          versions.

      Schema Based Example Validation
      The ExampleValidator component identifies any anomalies in the example data by
      comparing data statistics computed by the StatisticsGen component against a
      schema. The schema codifies properties which the input data is expected to
      satisfy, and is provided and maintained by the user.

      Please see https://www.tensorflow.org/tfx/data_validation for more details.

      Args:
          statistics: A Channel of 'ExampleStatistics` type. This should contain at
              least 'eval' split. Other splits are ignored currently.
          schema: A Channel of "Schema' type. _required_
      Returns:
          anomalies: Output channel of 'ExampleAnomalies' type.

      Either `stats` or `statistics` must be present in the arguments.
inputs:
- {name: statistics, type: ExampleStatistics}
- {name: schema, type: Schema}
outputs:
- {name: anomalies, type: ExampleAnomalies}
implementation:
  container:
    image: tensorflow/tfx:0.21.4
    command:
    - python3
    - -u
    - -c
    - |
      def _make_parent_dirs_and_return_path(file_path: str):
          import os
          os.makedirs(os.path.dirname(file_path), exist_ok=True)
          return file_path

      def ExampleValidator(
          statistics_path ,
          schema_path ,

          anomalies_path ,
      ):
          """
          A TFX component to validate input examples.

          The ExampleValidator component uses [Tensorflow Data
          Validation](https://www.tensorflow.org/tfx/data_validation) to
          validate the statistics of some splits on input examples against a schema.

          The ExampleValidator component identifies anomalies in training and serving
          data. The component can be configured to detect different classes of anomalies
          in the data. It can:
              - perform validity checks by comparing data statistics against a schema that
              codifies expectations of the user.
              - detect data drift by looking at a series of data.
              - detect changes in dataset-wide data (i.e., num_examples) across spans or
              versions.

          Schema Based Example Validation
          The ExampleValidator component identifies any anomalies in the example data by
          comparing data statistics computed by the StatisticsGen component against a
          schema. The schema codifies properties which the input data is expected to
          satisfy, and is provided and maintained by the user.

          Please see https://www.tensorflow.org/tfx/data_validation for more details.

          Args:
              statistics: A Channel of 'ExampleStatistics` type. This should contain at
                  least 'eval' split. Other splits are ignored currently.
              schema: A Channel of "Schema' type. _required_
          Returns:
              anomalies: Output channel of 'ExampleAnomalies' type.

          Either `stats` or `statistics` must be present in the arguments.
          """
          from tfx.components.example_validator.component import ExampleValidator as component_class

          #Generated code
          import json
          import os
          import tensorflow
          from google.protobuf import json_format, message
          from tfx.types import Artifact, channel_utils, artifact_utils

          arguments = locals().copy()

          component_class_args = {}

          for name, execution_parameter in component_class.SPEC_CLASS.PARAMETERS.items():
              argument_value_obj = argument_value = arguments.get(name, None)
              if argument_value is None:
                  continue
              parameter_type = execution_parameter.type
              if isinstance(parameter_type, type) and issubclass(parameter_type, message.Message): # Maybe FIX: execution_parameter.type can also be a tuple
                  argument_value_obj = parameter_type()
                  json_format.Parse(argument_value, argument_value_obj)
              component_class_args[name] = argument_value_obj

          for name, channel_parameter in component_class.SPEC_CLASS.INPUTS.items():
              artifact_path = arguments[name + '_path']
              if artifact_path:
                  artifact = channel_parameter.type()
                  artifact.uri = artifact_path + '/' # ?
                  if channel_parameter.type.PROPERTIES and 'split_names' in channel_parameter.type.PROPERTIES:
                      # Recovering splits
                      subdirs = tensorflow.io.gfile.listdir(artifact_path)
                      artifact.split_names = artifact_utils.encode_split_names(sorted(subdirs))
                  component_class_args[name] = channel_utils.as_channel([artifact])

          component_class_instance = component_class(**component_class_args)

          input_dict = {name: channel.get() for name, channel in component_class_instance.inputs.get_all().items()}
          output_dict = {name: channel.get() for name, channel in component_class_instance.outputs.get_all().items()}
          exec_properties = component_class_instance.exec_properties

          # Generating paths for output artifacts
          for name, artifacts in output_dict.items():
              base_artifact_path = arguments[name + '_path']
              # Are there still cases where output channel has multiple artifacts?
              for idx, artifact in enumerate(artifacts):
                  subdir = str(idx + 1) if idx > 0 else ''
                  artifact.uri = os.path.join(base_artifact_path, subdir)  # Ends with '/'

          print('component instance: ' + str(component_class_instance))

          #executor = component_class.EXECUTOR_SPEC.executor_class() # Same
          executor = component_class_instance.executor_spec.executor_class()
          executor.Do(
              input_dict=input_dict,
              output_dict=output_dict,
              exec_properties=exec_properties,
          )

      import argparse
      _parser = argparse.ArgumentParser(prog='ExampleValidator', description='A TFX component to validate input examples.\n\n    The ExampleValidator component uses [Tensorflow Data\n    Validation](https://www.tensorflow.org/tfx/data_validation) to\n    validate the statistics of some splits on input examples against a schema.\n\n    The ExampleValidator component identifies anomalies in training and serving\n    data. The component can be configured to detect different classes of anomalies\n    in the data. It can:\n        - perform validity checks by comparing data statistics against a schema that\n        codifies expectations of the user.\n        - detect data drift by looking at a series of data.\n        - detect changes in dataset-wide data (i.e., num_examples) across spans or\n        versions.\n\n    Schema Based Example Validation\n    The ExampleValidator component identifies any anomalies in the example data by\n    comparing data statistics computed by the StatisticsGen component against a\n    schema. The schema codifies properties which the input data is expected to\n    satisfy, and is provided and maintained by the user.\n\n    Please see https://www.tensorflow.org/tfx/data_validation for more details.\n\n    Args:\n        statistics: A Channel of \'ExampleStatistics` type. This should contain at\n            least \'eval\' split. Other splits are ignored currently.\n        schema: A Channel of "Schema\' type. _required_\n    Returns:\n        anomalies: Output channel of \'ExampleAnomalies\' type.\n\n    Either `stats` or `statistics` must be present in the arguments.')
      _parser.add_argument("--statistics", dest="statistics_path", type=str, required=True, default=argparse.SUPPRESS)
      _parser.add_argument("--schema", dest="schema_path", type=str, required=True, default=argparse.SUPPRESS)
      _parser.add_argument("--anomalies", dest="anomalies_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
      _parsed_args = vars(_parser.parse_args())
      _output_files = _parsed_args.pop("_output_paths", [])

      _outputs = ExampleValidator(**_parsed_args)

      _output_serializers = [

      ]

      import os
      for idx, output_file in enumerate(_output_files):
          try:
              os.makedirs(os.path.dirname(output_file))
          except OSError:
              pass
          with open(output_file, 'w') as f:
              f.write(_output_serializers[idx](_outputs[idx]))
    args:
    - --statistics
    - {inputPath: statistics}
    - --schema
    - {inputPath: schema}
    - --anomalies
    - {outputPath: anomalies}