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 Hugging Face ``Dataset``
  objects containing input text snippets and their corresponding target text snippets.
  
  Input text snippets will be extracted from the ``input_field`` column in the ``Dataset`` objects.
  Target text snippets will be extracted from the ``target_field`` column in the ``Dataset`` objects.
  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 ``Dataset`` objects containing the input text snippets.
      target_field: The field (column name) in the ``Dataset`` objects containing the target text snippets.
      train_hf_dataset: The ``Dataset`` to use when training.
      val_hf_dataset: The ``Dataset`` to use when validating.
      test_hf_dataset: The ``Dataset`` to use when testing.
      predict_hf_dataset: The ``Dataset`` 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.
      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
  ________
  
  .. doctest::
  
      >>> from datasets import Dataset
      >>> from flash import Trainer
      >>> from flash.text import SummarizationTask, SummarizationData
      >>> train_data = Dataset.from_dict(
      ...     {
      ...         "texts": ["A long paragraph", "A news article"],
      ...         "summaries": ["A short paragraph", "A news headline"],
      ...     }
      ... )
      >>> predict_data = Dataset.from_dict(
      ...     {
      ...         "texts": ["A movie review", "A book chapter"],
      ...     }
      ... )
      >>> datamodule = SummarizationData.from_hf_datasets(
      ...     "texts",
      ...     "summaries",
      ...     train_hf_dataset=train_data,
      ...     predict_hf_dataset=predict_data,
      ...     batch_size=2,
      ... )  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
      >>> 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::
  
      >>> del train_data
      >>> del predict_data
docstr_tok: ["Load","the",":","class",":","`","~flash.text.seq2seq.summarization.data.SummarizationData","`","from","Hugging","Face","``","Dataset","``","objects","containing","input","text","snippets","and","their","corresponding","target","text","snippets",".","Input","text","snippets","will","be","extracted","from","the","``","input_field","``","column","in","the","``","Dataset","``","objects",".","Target","text","snippets","will","be","extracted","from","the","``","target_field","``","column","in","the","``","Dataset","``","objects",".","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","``","Dataset","``","objects","containing","the","input","text","snippets",".","target_field",":","The","field","(","column","name",")","in","the","``","Dataset","``","objects","containing","the","target","text","snippets",".","train_hf_dataset",":","The","``","Dataset","``","to","use","when","training",".","val_hf_dataset",":","The","``","Dataset","``","to","use","when","validating",".","test_hf_dataset",":","The","``","Dataset","``","to","use","when","testing",".","predict_hf_dataset",":","The","``","Dataset","``","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",".","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","________","..","doctest",":",":",">",">",">","from","datasets","import","Dataset",">",">",">","from","flash","import","Trainer",">",">",">","from","flash.text","import","SummarizationTask",",","SummarizationData",">",">",">","train_data","=","Dataset.from_dict","(","...","{","...","``","texts","''",":","[","``","A","long","paragraph","''",",","``","A","news","article","''","]",",","...","``","summaries","''",":","[","``","A","short","paragraph","''",",","``","A","news","headline","''","]",",","...","}","...",")",">",">",">","predict_data","=","Dataset.from_dict","(","...","{","...","``","texts","''",":","[","``","A","movie","review","''",",","``","A","book","chapter","''","]",",","...","}","...",")",">",">",">","datamodule","=","SummarizationData.from_hf_datasets","(","...","``","texts","''",",","...","``","summaries","''",",","...","train_hf_dataset=train_data",",","...","predict_hf_dataset=predict_data",",","...","batch_size=2",",","...",")","#","doctest",":","+ELLIPSIS","+NORMALIZE_WHITESPACE",">",">",">","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",":",":",">",">",">","del","train_data",">",">",">","del","predict_data"]
code: def from_hf_datasets(
      cls,
      input_field: str,
      target_field: Optional[str] = None,
      train_hf_dataset: Optional[Dataset] = None,
      val_hf_dataset: Optional[Dataset] = None,
      test_hf_dataset: Optional[Dataset] = None,
      predict_hf_dataset: Optional[Dataset] = None,
      input_cls: Type[Input] = Seq2SeqInputBase,
      transform: INPUT_TRANSFORM_TYPE = InputTransform,
      transform_kwargs: Optional[Dict] = None,
      **data_module_kwargs: Any,
  ) -> "SummarizationData":
      """Load the :class:`~flash.text.seq2seq.summarization.data.SummarizationData` from Hugging Face ``Dataset``
      objects containing input text snippets and their corresponding target text snippets.
  
      Input text snippets will be extracted from the ``input_field`` column in the ``Dataset`` objects.
      Target text snippets will be extracted from the ``target_field`` column in the ``Dataset`` objects.
      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 ``Dataset`` objects containing the input text snippets.
          target_field: The field (column name) in the ``Dataset`` objects containing the target text snippets.
          train_hf_dataset: The ``Dataset`` to use when training.
          val_hf_dataset: The ``Dataset`` to use when validating.
          test_hf_dataset: The ``Dataset`` to use when testing.
          predict_hf_dataset: The ``Dataset`` 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.
          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
      ________
  
      .. doctest::
  
          >>> from datasets import Dataset
          >>> from flash import Trainer
          >>> from flash.text import SummarizationTask, SummarizationData
          >>> train_data = Dataset.from_dict(
          ...     {
          ...         "texts": ["A long paragraph", "A news article"],
          ...         "summaries": ["A short paragraph", "A news headline"],
          ...     }
          ... )
          >>> predict_data = Dataset.from_dict(
          ...     {
          ...         "texts": ["A movie review", "A book chapter"],
          ...     }
          ... )
          >>> datamodule = SummarizationData.from_hf_datasets(
          ...     "texts",
          ...     "summaries",
          ...     train_hf_dataset=train_data,
          ...     predict_hf_dataset=predict_data,
          ...     batch_size=2,
          ... )  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
          >>> 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::
  
          >>> del train_data
          >>> del predict_data
  
      """
  
      ds_kw = {
          "input_key": input_field,
          "target_key": target_field,
      }
  
      return cls(
          input_cls(RunningStage.TRAINING, train_hf_dataset, **ds_kw),
          input_cls(RunningStage.VALIDATING, val_hf_dataset, **ds_kw),
          input_cls(RunningStage.TESTING, test_hf_dataset, **ds_kw),
          input_cls(
              RunningStage.PREDICTING, predict_hf_dataset, **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 3 for calling the SummarizationData obj's underlying member method code with example usage: obj.from_hf_datasets(cls, input_field, target_field, train_hf_dataset, val_hf_dataset, test_hf_dataset, predict_hf_dataset, input_cls, transform, transform_kwargs) and returns: cls