text: # Copyright The PyTorch Lightning team.
  #
  # 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.
  """LightningDataModule for loading DataLoaders with ease."""
  
  import functools
  from abc import abstractmethod
  from argparse import ArgumentParser, Namespace
  from typing import Any, List, Mapping, Optional, Sequence, Tuple, Union
  
  from torch.utils.data import DataLoader, Dataset
  
  from pytorch_lightning.core.hooks import CheckpointHooks, DataHooks
  from pytorch_lightning.utilities import rank_zero_only
  from pytorch_lightning.utilities.argparse import add_argparse_args, from_argparse_args, get_init_arguments_and_types
  
  
  class _DataModuleWrapper(type):
  
      def __init__(self, *args, **kwargs):
          super().__init__(*args, **kwargs)
          self.__has_added_checks = False
  
      def __call__(cls, *args, **kwargs):
          """A wrapper for LightningDataModule that:
  
          1. Runs user defined subclass's __init__
          2. Assures prepare_data() runs on rank 0
          3. Lets you check prepare_data and setup to see if they've been called
          """
          if not cls.__has_added_checks:
              cls.__has_added_checks = True
              # Track prepare_data calls and make sure it runs on rank zero
              cls.prepare_data = track_data_hook_calls(rank_zero_only(cls.prepare_data))
              # Track setup calls
              cls.setup = track_data_hook_calls(cls.setup)
  
          # Get instance of LightningDataModule by mocking its __init__ via __call__
          obj = type.__call__(cls, *args, **kwargs)
  
          return obj
  
  
  def track_data_hook_calls(fn):
      """A decorator that checks if prepare_data/setup have been called.
  
      - When ``dm.prepare_data()`` is called, ``dm.has_prepared_data`` gets set to True
      - When ``dm.setup()``, ``dm.has_setup_{fit,validate,test}`` get set to True
      - When ``dm.setup(stage)`` is called, where stage is any of ``{fit,validate,test,predict}``.
          Its corresponding `dm_has_setup_{stage}` attribute gets set to True
  
      Args:
          fn (function): Function that will be tracked to see if it has been called.
  
      Returns:
          function: Decorated function that tracks its call status and saves it to private attrs in its obj instance.
      """
  
      @functools.wraps(fn)
      def wrapped_fn(*args, **kwargs):
  
          # The object instance from which setup or prepare_data was called
          obj = args[0]
  
          # If calling setup, we check the stage and assign stage-specific bool args
          if fn.__name__ == "setup":
  
              # Get stage either by grabbing from args or checking kwargs.
              # If not provided, set call status of 'fit', 'validate', and 'test' to True.
              # We do this so __attach_datamodule in trainer.py doesn't mistakenly call setup('test') on trainer.test()
              stage = args[1] if len(args) > 1 else kwargs.get("stage", None)
  
              if stage is None:
                  for s in ("fit", "validate", "test"):
                      setattr(obj, f"_has_setup_{s}", True)
              else:
                  setattr(obj, f"_has_setup_{stage}", True)
  
          if fn.__name__ == "prepare_data":
              obj._has_prepared_data = True
  
          return fn(*args, **kwargs)
  
      return wrapped_fn
  
  
  class LightningDataModule(CheckpointHooks, DataHooks, metaclass=_DataModuleWrapper):
      """
      A DataModule standardizes the training, val, test splits, data preparation and transforms.
      The main advantage is consistent data splits, data preparation and transforms across models.
  
      Example::
  
          class MyDataModule(LightningDataModule):
              def __init__(self):
                  super().__init__()
              def prepare_data(self):
                  # download, split, etc...
                  # only called on 1 GPU/TPU in distributed
              def setup(self):
                  # make assignments here (val/train/test split)
                  # called on every process in DDP
              def train_dataloader(self):
                  train_split = Dataset(...)
                  return DataLoader(train_split)
              def val_dataloader(self):
                  val_split = Dataset(...)
                  return DataLoader(val_split)
              def test_dataloader(self):
                  test_split = Dataset(...)
                  return DataLoader(test_split)
  
      A DataModule implements 5 key methods:
  
      * **prepare_data** (things to do on 1 GPU/TPU not on every GPU/TPU in distributed mode).
      * **setup**  (things to do on every accelerator in distributed mode).
      * **train_dataloader** the training dataloader.
      * **val_dataloader** the val dataloader(s).
      * **test_dataloader** the test dataloader(s).
  
  
      This allows you to share a full dataset without explaining how to download,
      split transform and process the data
  
      """
  
      name: str = ...
  
      def __init__(
          self,
          train_transforms=None,
          val_transforms=None,
          test_transforms=None,
          dims=None,
      ):
          super().__init__()
          self._train_transforms = train_transforms
          self._val_transforms = val_transforms
          self._test_transforms = test_transforms
          self._dims = dims if dims is not None else ()
  
          # Pointer to the trainer object
          self.trainer = None
  
          # Private attrs to keep track of whether or not data hooks have been called yet
          self._has_prepared_data = False
          self._has_setup_fit = False
          self._has_setup_validate = False
          self._has_setup_test = False
          self._has_setup_predict = False
  
      @property
      def train_transforms(self):
          """
          Optional transforms (or collection of transforms) you can apply to train dataset
          """
          return self._train_transforms
  
      @train_transforms.setter
      def train_transforms(self, t):
          self._train_transforms = t
  
      @property
      def val_transforms(self):
          """
          Optional transforms (or collection of transforms) you can apply to validation dataset
          """
          return self._val_transforms
  
      @val_transforms.setter
      def val_transforms(self, t):
          self._val_transforms = t
  
      @property
      def test_transforms(self):
          """
          Optional transforms (or collection of transforms) you can apply to test dataset
          """
          return self._test_transforms
  
      @test_transforms.setter
      def test_transforms(self, t):
          self._test_transforms = t
  
      @property
      def dims(self):
          """
          A tuple describing the shape of your data. Extra functionality exposed in ``size``.
          """
          return self._dims
  
      @dims.setter
      def dims(self, d):
          self._dims = d
  
      def size(self, dim=None) -> Union[Tuple, int]:
          """
          Return the dimension of each input either as a tuple or list of tuples. You can index this
          just as you would with a torch tensor.
          """
  
          if dim is not None:
              return self.dims[dim]
  
          return self.dims
  
      @property
      def has_prepared_data(self) -> bool:
          """Return bool letting you know if ``datamodule.prepare_data()`` has been called or not.
  
          Returns:
              bool: True if ``datamodule.prepare_data()`` has been called. False by default.
          """
          return self._has_prepared_data
  
      @property
      def has_setup_fit(self) -> bool:
          """Return bool letting you know if ``datamodule.setup(stage='fit')`` has been called or not.
  
          Returns:
              bool: True ``if datamodule.setup(stage='fit')`` has been called. False by default.
          """
          return self._has_setup_fit
  
      @property
      def has_setup_validate(self) -> bool:
          """Return bool letting you know if ``datamodule.setup(stage='validate')`` has been called or not.
  
          Returns:
              bool: True if ``datamodule.setup(stage='validate')`` has been called. False by default.
          """
          return self._has_setup_validate
  
      @property
      def has_setup_test(self) -> bool:
          """Return bool letting you know if ``datamodule.setup(stage='test')`` has been called or not.
  
          Returns:
              bool: True if ``datamodule.setup(stage='test')`` has been called. False by default.
          """
          return self._has_setup_test
  
      @property
      def has_setup_predict(self) -> bool:
          """Return bool letting you know if ``datamodule.setup(stage='predict')`` has been called or not.
  
          Returns:
              bool: True if ``datamodule.setup(stage='predict')`` has been called. False by default.
          """
          return self._has_setup_predict
  
      @abstractmethod
      def prepare_data(self, *args, **kwargs):
          pass
  
      @abstractmethod
      def setup(self, stage: Optional[str] = None):
          pass
  
      @classmethod
      def add_argparse_args(cls, parent_parser: ArgumentParser, **kwargs) -> ArgumentParser:
          """Extends existing argparse by default `LightningDataModule` attributes."""
          return add_argparse_args(cls, parent_parser, **kwargs)
  
      @classmethod
      def from_argparse_args(cls, args: Union[Namespace, ArgumentParser], **kwargs):
          """Create an instance from CLI arguments.
  
          Args:
              args: The parser or namespace to take arguments from. Only known arguments will be
               parsed and passed to the :class:`LightningDataModule`.
              **kwargs: Additional keyword arguments that may override ones in the parser or namespace.
               These must be valid DataModule arguments.
  
          Example::
              parser = ArgumentParser(add_help=False)
              parser = LightningDataModule.add_argparse_args(parser)
              module = LightningDataModule.from_argparse_args(args)
          """
          return from_argparse_args(cls, args, **kwargs)
  
      @classmethod
      def get_init_arguments_and_types(cls) -> List[Tuple[str, Tuple, Any]]:
          r"""Scans the DataModule signature and returns argument names, types and default values.
  
          Returns:
              List with tuples of 3 values:
              (argument name, set with argument types, argument default value).
          """
          return get_init_arguments_and_types(cls)
  
      @classmethod
      def from_datasets(
          cls,
          train_dataset: Optional[Union[Dataset, Sequence[Dataset], Mapping[str, Dataset]]] = None,
          val_dataset: Optional[Union[Dataset, Sequence[Dataset]]] = None,
          test_dataset: Optional[Union[Dataset, Sequence[Dataset]]] = None,
          batch_size: int = 1,
          num_workers: int = 0,
      ):
          r"""
          Create an instance from torch.utils.data.Dataset.
  
          Args:
              train_dataset: (optional) Dataset to be used for train_dataloader()
              val_dataset: (optional) Dataset or list of Dataset to be used for val_dataloader()
              test_dataset: (optional) Dataset or list of Dataset to be used for test_dataloader()
              batch_size: Batch size to use for each dataloader. Default is 1.
              num_workers: Number of subprocesses to use for data loading. 0 means that the
                  data will be loaded in the main process. Number of CPUs available.
  
          """
  
          def dataloader(ds, shuffle=False):
              return DataLoader(
                  ds,
                  batch_size=batch_size,
                  shuffle=shuffle,
                  num_workers=num_workers,
                  pin_memory=True,
              )
  
          def train_dataloader():
              if isinstance(train_dataset, Mapping):
                  return {key: dataloader(ds, shuffle=True) for key, ds in train_dataset.items()}
              if isinstance(train_dataset, Sequence):
                  return [dataloader(ds, shuffle=True) for ds in train_dataset]
              return dataloader(train_dataset, shuffle=True)
  
          def val_dataloader():
              if isinstance(val_dataset, Sequence):
                  return [dataloader(ds) for ds in val_dataset]
              return dataloader(val_dataset)
  
          def test_dataloader():
              if isinstance(test_dataset, Sequence):
                  return [dataloader(ds) for ds in test_dataset]
              return dataloader(test_dataset)
  
          datamodule = cls()
          if train_dataset is not None:
              datamodule.train_dataloader = train_dataloader
          if val_dataset is not None:
              datamodule.val_dataloader = val_dataloader
          if test_dataset is not None:
              datamodule.test_dataloader = test_dataloader
          return datamodule
  