class_docstr: The ``SummarizationData`` class is a :class:`~flash.core.data.data_module.DataModule` with a set of classmethods
  for loading data for text summarization.
docstr: Load the :class:`~flash.text.seq2seq.summarization.data.SummarizationData` from JSON files containing input
  text snippets and their corresponding target text snippets.
  
  Input text snippets will be extracted from the ``input_field`` column in the JSON files.
  Target text snippets will be extracted from the ``target_field`` column in the JSON files.
  To learn how to customize the transforms applied for each stage, read our
  :ref:`customizing transforms guide <customizing_transforms>`.
  
  Args:
      input_field: The field (column name) in the JSON objects containing the input text snippets.
      target_field: The field (column name) in the JSON objects containing the target text snippets.
      train_file: The JSON file to use when training.
      val_file: The JSON file to use when validating.
      test_file: The JSON file to use when testing.
      predict_file: The JSON file to use when predicting.
      input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
      transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use.
      transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
      field: The field that holds the data in the JSON file.
      data_module_kwargs: Additional keyword arguments to provide to the
          :class:`~flash.core.data.data_module.DataModule` constructor.
  
  Returns:
      The constructed :class:`~flash.text.seq2seq.summarization.data.SummarizationData`.
  
  Examples
  ________
  
  .. testsetup::
  
      >>> import os
      >>> from pandas import DataFrame
      >>> DataFrame.from_dict({
      ...     "texts": ["A long paragraph", "A news article"],
      ...     "summaries": ["A short paragraph", "A news headline"],
      ... }).to_json("train_data.json", orient="records", lines=True)
      >>> DataFrame.from_dict({
      ...     "texts": ["A movie review", "A book chapter"],
      ... }).to_json("predict_data.json", orient="records", lines=True)
  
  The file ``train_data.json`` contains the following:
  
  .. code-block::
  
      {"texts":"A long paragraph","summaries":"A short paragraph"}
      {"texts":"A news article","summaries":"A news headline"}
  
  The file ``predict_data.json`` contains the following:
  
  .. code-block::
  
      {"texts":"A movie review"}
      {"texts":"A book chapter"}
  
  .. doctest::
  
      >>> from flash import Trainer
      >>> from flash.text import SummarizationTask, SummarizationData
      >>> datamodule = SummarizationData.from_json(
      ...     "texts",
      ...     "summaries",
      ...     train_file="train_data.json",
      ...     predict_file="predict_data.json",
      ...     batch_size=2,
      ... )  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
      <BLANKLINE>
      ...
      >>> model = SummarizationTask(backbone="JulesBelveze/t5-small-headline-generator")
      >>> trainer = Trainer(fast_dev_run=True)
      >>> trainer.fit(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
      Training...
      >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
      Predicting...
  
  .. testcleanup::
  
      >>> os.remove("train_data.json")
      >>> os.remove("predict_data.json")
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code: def from_json(
      cls,
      input_field: str,
      target_field: Optional[str] = None,
      train_file: Optional[PATH_TYPE] = None,
      val_file: Optional[PATH_TYPE] = None,
      test_file: Optional[PATH_TYPE] = None,
      predict_file: Optional[PATH_TYPE] = None,
      input_cls: Type[Input] = Seq2SeqJSONInput,
      transform: INPUT_TRANSFORM_TYPE = InputTransform,
      transform_kwargs: Optional[Dict] = None,
      field: Optional[str] = None,
      **data_module_kwargs: Any,
  ) -> "SummarizationData":
      """Load the :class:`~flash.text.seq2seq.summarization.data.SummarizationData` from JSON files containing input
      text snippets and their corresponding target text snippets.
  
      Input text snippets will be extracted from the ``input_field`` column in the JSON files.
      Target text snippets will be extracted from the ``target_field`` column in the JSON files.
      To learn how to customize the transforms applied for each stage, read our
      :ref:`customizing transforms guide <customizing_transforms>`.
  
      Args:
          input_field: The field (column name) in the JSON objects containing the input text snippets.
          target_field: The field (column name) in the JSON objects containing the target text snippets.
          train_file: The JSON file to use when training.
          val_file: The JSON file to use when validating.
          test_file: The JSON file to use when testing.
          predict_file: The JSON file to use when predicting.
          input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
          transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use.
          transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
          field: The field that holds the data in the JSON file.
          data_module_kwargs: Additional keyword arguments to provide to the
              :class:`~flash.core.data.data_module.DataModule` constructor.
  
      Returns:
          The constructed :class:`~flash.text.seq2seq.summarization.data.SummarizationData`.
  
      Examples
      ________
  
      .. testsetup::
  
          >>> import os
          >>> from pandas import DataFrame
          >>> DataFrame.from_dict({
          ...     "texts": ["A long paragraph", "A news article"],
          ...     "summaries": ["A short paragraph", "A news headline"],
          ... }).to_json("train_data.json", orient="records", lines=True)
          >>> DataFrame.from_dict({
          ...     "texts": ["A movie review", "A book chapter"],
          ... }).to_json("predict_data.json", orient="records", lines=True)
  
      The file ``train_data.json`` contains the following:
  
      .. code-block::
  
          {"texts":"A long paragraph","summaries":"A short paragraph"}
          {"texts":"A news article","summaries":"A news headline"}
  
      The file ``predict_data.json`` contains the following:
  
      .. code-block::
  
          {"texts":"A movie review"}
          {"texts":"A book chapter"}
  
      .. doctest::
  
          >>> from flash import Trainer
          >>> from flash.text import SummarizationTask, SummarizationData
          >>> datamodule = SummarizationData.from_json(
          ...     "texts",
          ...     "summaries",
          ...     train_file="train_data.json",
          ...     predict_file="predict_data.json",
          ...     batch_size=2,
          ... )  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
          <BLANKLINE>
          ...
          >>> model = SummarizationTask(backbone="JulesBelveze/t5-small-headline-generator")
          >>> trainer = Trainer(fast_dev_run=True)
          >>> trainer.fit(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
          Training...
          >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
          Predicting...
  
      .. testcleanup::
  
          >>> os.remove("train_data.json")
          >>> os.remove("predict_data.json")
  
      """
  
      ds_kw = {
          "input_key": input_field,
          "target_key": target_field,
          "field": field,
      }
  
      return cls(
          input_cls(RunningStage.TRAINING, train_file, **ds_kw),
          input_cls(RunningStage.VALIDATING, val_file, **ds_kw),
          input_cls(RunningStage.TESTING, test_file, **ds_kw),
          input_cls(RunningStage.PREDICTING, predict_file, **ds_kw),
          transform=transform,
          transform_kwargs=transform_kwargs,
          **data_module_kwargs,
      )
  
file_path: lightning-flash/src/flash/text/seq2seq/summarization/data.py
imports: import os
  from typing import Any, Dict, List, Optional, Type
  from flash.core.data.data_module import DataModule
  from flash.core.data.io.input import Input
  from flash.core.data.io.input_transform import InputTransform
  from flash.core.data.utilities.paths import PATH_TYPE
  from flash.core.utilities.imports import _TOPIC_TEXT_AVAILABLE
  from flash.core.utilities.stages import RunningStage
  from flash.core.utilities.types import INPUT_TRANSFORM_TYPE
  from flash.text.seq2seq.core.input import Seq2SeqCSVInput, Seq2SeqInputBase, Seq2SeqJSONInput, Seq2SeqListInput
label_desc: Use image node_id 2 for calling the SummarizationData obj's underlying member method code with example usage: obj.from_json(cls, input_field, target_field, train_file, val_file, test_file, predict_file, input_cls, transform, transform_kwargs, field) and returns: cls