class_docstr: The ``VideoClassificationData`` class is a :class:`~flash.core.data.data_module.DataModule` with a set of
  classmethods for loading data for video classification.
docstr: Load the :class:`~flash.video.classification.data.VideoClassificationData` from FiftyOne ``SampleCollection``
  objects.
  
  The supported file extensions are: ``.mp4``, and ``.avi``.
  The targets will be extracted from the ``label_field`` in the ``SampleCollection`` objects and can be in any
  of our :ref:`supported classification target formats <formatting_classification_targets>`.
  To learn how to customize the transforms applied for each stage, read our
  :ref:`customizing transforms guide <customizing_transforms>`.
  
  Args:
      train_dataset: The ``SampleCollection`` to use when training.
      val_dataset: The ``SampleCollection`` to use when validating.
      test_dataset: The ``SampleCollection`` to use when testing.
      predict_dataset: The ``SampleCollection`` to use when predicting.
      label_field: The field in the ``SampleCollection`` objects containing the targets.
      target_formatter: Optionally provide a :class:`~flash.core.data.utilities.classification.TargetFormatter` to
          control how targets are handled. See :ref:`formatting_classification_targets` for more details.
      clip_sampler: The clip sampler to use. One of: ``"uniform"``, ``"random"``, ``"constant_clips_per_video"``.
      clip_duration: The duration of clips to sample.
      clip_sampler_kwargs: Additional keyword arguments to use when constructing the clip sampler.
      video_sampler: Sampler for the internal video container. This defines the order videos are decoded and,
          if necessary, the distributed split.
      decode_audio: If True, also decode audio from video.
      decoder: The decoder to use to decode videos. One of: ``"pyav"``, ``"torchvision"``. Not used for frame
          videos.
      input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
      predict_input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the prediction 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.video.classification.data.VideoClassificationData`.
  
  Examples
  ________
  
  .. testsetup::
  
      >>> import torch
      >>> from torchvision import io
      >>> data = torch.randint(255, (10, 64, 64, 3))
      >>> _ = [io.write_video(f"video_{i}.mp4", data, 5, "libx264rgb", {"crf": "0"}) for i in range(1, 4)]
      >>> _ = [io.write_video(f"predict_video_{i}.mp4", data, 5, "libx264rgb", {"crf": "0"}) for i in range(1, 4)]
  
  .. doctest::
  
      >>> import fiftyone as fo
      >>> from flash import Trainer
      >>> from flash.video import VideoClassifier, VideoClassificationData
      >>> train_dataset = fo.Dataset.from_videos(
      ...     ["video_1.mp4", "video_2.mp4", "video_3.mp4"]
      ... )  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
      <BLANKLINE>
      ...
      >>> samples = [train_dataset[filepath] for filepath in train_dataset.values("filepath")]
      >>> for sample, label in zip(samples, ["cat", "dog", "cat"]):
      ...     sample["ground_truth"] = fo.Classification(label=label)
      ...     sample.save()
      ...
      >>> predict_dataset = fo.Dataset.from_images(
      ...     ["predict_video_1.mp4", "predict_video_2.mp4", "predict_video_3.mp4"]
      ... )  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
      <BLANKLINE>
      ...
      >>> datamodule = VideoClassificationData.from_fiftyone(
      ...     train_dataset=train_dataset,
      ...     predict_dataset=predict_dataset,
      ...     transform_kwargs=dict(image_size=(244, 244)),
      ...     batch_size=2,
      ... )
      >>> datamodule.num_classes
      2
      >>> datamodule.labels
      ['cat', 'dog']
      >>> model = VideoClassifier(backbone="x3d_xs", num_classes=datamodule.num_classes)
      >>> 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::
  
      >>> import os
      >>> _ = [os.remove(f"video_{i}.mp4") for i in range(1, 4)]
      >>> _ = [os.remove(f"predict_video_{i}.mp4") for i in range(1, 4)]
      >>> del train_dataset
      >>> del predict_dataset
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code: def from_fiftyone(
      cls,
      train_dataset: Optional[SampleCollection] = None,
      val_dataset: Optional[SampleCollection] = None,
      test_dataset: Optional[SampleCollection] = None,
      predict_dataset: Optional[SampleCollection] = None,
      target_formatter: Optional[TargetFormatter] = None,
      clip_sampler: Union[str, "ClipSampler"] = "random",
      clip_duration: float = 2,
      clip_sampler_kwargs: Dict[str, Any] = None,
      video_sampler: Type[Sampler] = torch.utils.data.RandomSampler,
      decode_audio: bool = False,
      decoder: str = "pyav",
      label_field: str = "ground_truth",
      input_cls: Type[Input] = VideoClassificationFiftyOneInput,
      predict_input_cls: Type[
          Input
      ] = VideoClassificationFiftyOnePredictInput,
      transform: INPUT_TRANSFORM_TYPE = VideoClassificationInputTransform,
      transform_kwargs: Optional[Dict] = None,
      **data_module_kwargs,
  ) -> "VideoClassificationData":
      """Load the :class:`~flash.video.classification.data.VideoClassificationData` from FiftyOne ``SampleCollection``
      objects.
  
      The supported file extensions are: ``.mp4``, and ``.avi``.
      The targets will be extracted from the ``label_field`` in the ``SampleCollection`` objects and can be in any
      of our :ref:`supported classification target formats <formatting_classification_targets>`.
      To learn how to customize the transforms applied for each stage, read our
      :ref:`customizing transforms guide <customizing_transforms>`.
  
      Args:
          train_dataset: The ``SampleCollection`` to use when training.
          val_dataset: The ``SampleCollection`` to use when validating.
          test_dataset: The ``SampleCollection`` to use when testing.
          predict_dataset: The ``SampleCollection`` to use when predicting.
          label_field: The field in the ``SampleCollection`` objects containing the targets.
          target_formatter: Optionally provide a :class:`~flash.core.data.utilities.classification.TargetFormatter` to
              control how targets are handled. See :ref:`formatting_classification_targets` for more details.
          clip_sampler: The clip sampler to use. One of: ``"uniform"``, ``"random"``, ``"constant_clips_per_video"``.
          clip_duration: The duration of clips to sample.
          clip_sampler_kwargs: Additional keyword arguments to use when constructing the clip sampler.
          video_sampler: Sampler for the internal video container. This defines the order videos are decoded and,
              if necessary, the distributed split.
          decode_audio: If True, also decode audio from video.
          decoder: The decoder to use to decode videos. One of: ``"pyav"``, ``"torchvision"``. Not used for frame
              videos.
          input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
          predict_input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the prediction 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.video.classification.data.VideoClassificationData`.
  
      Examples
      ________
  
      .. testsetup::
  
          >>> import torch
          >>> from torchvision import io
          >>> data = torch.randint(255, (10, 64, 64, 3))
          >>> _ = [io.write_video(f"video_{i}.mp4", data, 5, "libx264rgb", {"crf": "0"}) for i in range(1, 4)]
          >>> _ = [io.write_video(f"predict_video_{i}.mp4", data, 5, "libx264rgb", {"crf": "0"}) for i in range(1, 4)]
  
      .. doctest::
  
          >>> import fiftyone as fo
          >>> from flash import Trainer
          >>> from flash.video import VideoClassifier, VideoClassificationData
          >>> train_dataset = fo.Dataset.from_videos(
          ...     ["video_1.mp4", "video_2.mp4", "video_3.mp4"]
          ... )  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
          <BLANKLINE>
          ...
          >>> samples = [train_dataset[filepath] for filepath in train_dataset.values("filepath")]
          >>> for sample, label in zip(samples, ["cat", "dog", "cat"]):
          ...     sample["ground_truth"] = fo.Classification(label=label)
          ...     sample.save()
          ...
          >>> predict_dataset = fo.Dataset.from_images(
          ...     ["predict_video_1.mp4", "predict_video_2.mp4", "predict_video_3.mp4"]
          ... )  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
          <BLANKLINE>
          ...
          >>> datamodule = VideoClassificationData.from_fiftyone(
          ...     train_dataset=train_dataset,
          ...     predict_dataset=predict_dataset,
          ...     transform_kwargs=dict(image_size=(244, 244)),
          ...     batch_size=2,
          ... )
          >>> datamodule.num_classes
          2
          >>> datamodule.labels
          ['cat', 'dog']
          >>> model = VideoClassifier(backbone="x3d_xs", num_classes=datamodule.num_classes)
          >>> 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::
  
          >>> import os
          >>> _ = [os.remove(f"video_{i}.mp4") for i in range(1, 4)]
          >>> _ = [os.remove(f"predict_video_{i}.mp4") for i in range(1, 4)]
          >>> del train_dataset
          >>> del predict_dataset
  
      """
      ds_kw = {
          "clip_sampler": clip_sampler,
          "clip_duration": clip_duration,
          "clip_sampler_kwargs": clip_sampler_kwargs,
          "decode_audio": decode_audio,
          "decoder": decoder,
      }
  
      train_input = input_cls(
          RunningStage.TRAINING,
          train_dataset,
          video_sampler=video_sampler,
          label_field=label_field,
          target_formatter=target_formatter,
          **ds_kw,
      )
      target_formatter = getattr(train_input, "target_formatter", None)
  
      return cls(
          train_input,
          input_cls(
              RunningStage.VALIDATING,
              val_dataset,
              video_sampler=video_sampler,
              label_field=label_field,
              target_formatter=target_formatter,
              **ds_kw,
          ),
          input_cls(
              RunningStage.TESTING,
              test_dataset,
              video_sampler=video_sampler,
              label_field=label_field,
              target_formatter=target_formatter,
              **ds_kw,
          ),
          predict_input_cls(
              RunningStage.PREDICTING, predict_dataset, **ds_kw
          ),
          transform=transform,
          transform_kwargs=transform_kwargs,
          **data_module_kwargs,
      )
  
imports: from typing import Any, Callable, Collection, Dict, List, Optional, Sequence, Type, Union
  import pandas
  import torch
  from torch.utils.data import Sampler
  from flash.core.data.data_module import DataModule
  from flash.core.data.io.input import Input
  from flash.core.data.io.input_transform import INPUT_TRANSFORM_TYPE
  from flash.core.data.utilities.classification import TargetFormatter
  from flash.core.data.utilities.paths import PATH_TYPE
  from flash.core.integrations.labelstudio.input import LabelStudioVideoClassificationInput, _parse_labelstudio_arguments
  from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _PYTORCHVIDEO_AVAILABLE, _TOPIC_VIDEO_AVAILABLE, requires
  from flash.core.utilities.stages import RunningStage
  from flash.video.classification.input import VideoClassificationCSVInput, VideoClassificationCSVPredictInput, VideoClassificationDataFrameInput, VideoClassificationDataFramePredictInput, VideoClassificationFiftyOneInput, VideoClassificationFiftyOnePredictInput, VideoClassificationFilesInput, VideoClassificationFoldersInput, VideoClassificationPathsPredictInput, VideoClassificationTensorsInput, VideoClassificationTensorsPredictInput
  from flash.video.classification.input_transform import VideoClassificationInputTransform
label_desc: Use image node_id 6 for calling the VideoClassificationData obj's underlying member method code with example usage: obj.from_fiftyone(cls, train_dataset, val_dataset, test_dataset, predict_dataset, target_formatter, clip_sampler, clip_duration, clip_sampler_kwargs, video_sampler, decode_audio, decoder, label_field, input_cls, predict_input_cls, transform, transform_kwargs) and returns: cls