text: import functools
  import time
  from typing import Any, Callable, List, Iterator, Iterable, Generic, Union, \
      Optional, TYPE_CHECKING
  
  import ray
  from ray.data.dataset import Dataset, T, U, BatchType
  from ray.data.impl.pipeline_executor import PipelineExecutor, \
      PipelineSplitExecutorCoordinator
  from ray.data.impl import progress_bar
  from ray.util.annotations import PublicAPI, DeveloperAPI
  
  if TYPE_CHECKING:
      import pyarrow
  
  # Operations that can be naively applied per dataset row in the pipeline.
  PER_DATASET_OPS = [
      "map", "map_batches", "flat_map", "filter", "write_json", "write_csv",
      "write_parquet", "write_datasource"
  ]
  
  # Operations that apply to each dataset holistically in the pipeline.
  HOLISTIC_PER_DATASET_OPS = ["repartition", "random_shuffle", "sort"]
  
  # Similar to above but we should force evaluation immediately.
  PER_DATASET_OUTPUT_OPS = [
      "write_json", "write_csv", "write_parquet", "write_datasource"
  ]
  
  # Operations that operate over the stream of output batches from the pipeline.
  OUTPUT_ITER_OPS = ["take", "show", "iter_rows", "to_tf", "to_torch"]
  
  
  @PublicAPI(stability="beta")
  class DatasetPipeline(Generic[T]):
      """Implements a pipeline of Datasets.
  
      Unlike Datasets, which execute all transformations synchronously,
      DatasetPipelines implement pipelined execution. This allows for the
      overlapped execution of data input (e.g., reading files), computation
      (e.g. feature preprocessing), and output (e.g., distributed ML training).
  
      A DatasetPipeline can be created by either repeating a Dataset
      (``ds.repeat(times=None)``), by turning a single Dataset into a pipeline
      (``ds.window(blocks_per_window=10)``), or defined explicitly using
      ``DatasetPipeline.from_iterable()``.
  
      DatasetPipeline supports the all the per-record transforms of Datasets
      (e.g., map, flat_map, filter), holistic transforms (e.g., repartition),
      and output methods (e.g., iter_rows, to_tf, to_torch, write_datasource).
      """
  
      def __init__(self,
                   base_iterable: Iterable[Callable[[], Dataset[T]]],
                   stages: List[Callable[[Dataset[Any]], Dataset[Any]]] = None,
                   length: int = None,
                   progress_bars: bool = progress_bar._enabled,
                   _executed: List[bool] = None):
          """Construct a DatasetPipeline (internal API).
  
          The constructor is not part of the DatasetPipeline API. Use the
          ``Dataset.repeat()``, ``Dataset.window()``, or
          ``DatasetPipeline.from_iterable()`` methods to construct a pipeline.
          """
          self._base_iterable = base_iterable
          self._stages = stages or []
          self._length = length
          self._progress_bars = progress_bars
          self._uuid = None  # For testing only.
          # Whether the pipeline execution has started.
          # This variable is shared across all pipelines descending from this.
          self._executed = _executed or [False]
  
      def iter_batches(self,
                       *,
                       prefetch_blocks: int = 0,
                       batch_size: int = None,
                       batch_format: str = "pandas",
                       drop_last: bool = False) -> Iterator[BatchType]:
          """Return a local batched iterator over the data in the pipeline.
  
          Examples:
              >>> for pandas_df in ray.data.range(1000000).iter_batches():
              ...     print(pandas_df)
  
          Time complexity: O(1)
  
          Args:
              prefetch_blocks: The number of blocks to prefetch ahead of the
                  current block during the scan.
              batch_size: Record batch size, or None to let the system pick.
              batch_format: The format in which to return each batch.
                  Specify "pandas" to select ``pandas.DataFrame`` or "pyarrow" to
                  select ``pyarrow.Table``. Default is "pandas".
              drop_last: Whether to drop the last batch if it's incomplete.
  
          Returns:
              A list of iterators over record batches.
          """
  
          def gen_batches() -> Iterator[BatchType]:
              for ds in self.iter_datasets():
                  for batch in ds.iter_batches(
                          prefetch_blocks=prefetch_blocks,
                          batch_size=batch_size,
                          batch_format=batch_format,
                          drop_last=drop_last):
                      yield batch
  
          return gen_batches()
  
      def split(self,
                n: int,
                *,
                equal: bool = False,
                locality_hints: List[Any] = None) -> List["DatasetPipeline[T]"]:
          """Split the pipeline into ``n`` disjoint pipeline shards.
  
          This returns a list of sub-pipelines that can be passed to Ray tasks
          and actors and used to read the pipeline records in parallel.
  
          Examples:
              >>> # Split up a pipeline to process over `n` worker actors.
              >>> shards = pipe.split(len(workers), locality_hints=workers)
              >>> for shard, worker in zip(shards, workers):
              ...     worker.consume.remote(shard)
  
          Time complexity: O(1)
  
          Implementation detail: this launches a coordinator actor that is used
          to execute the pipeline and push data blocks to each pipeline shard.
          Reading from an individual shard will be blocked if other shards are
          falling behind. A warning will be printed if a shard has been blocked
          on read for more than 10 seconds.
  
          Args:
              n: Number of child pipelines to return.
              equal: Whether to guarantee each split has an equal
                  number of records. This may drop records if they cannot be
                  divided equally among the splits.
              locality_hints: A list of Ray actor handles of size ``n``. The
                  system will try to co-locate the blocks of the ith pipeline
                  shard with the ith actor to maximize data locality.
  
          Returns:
              A list of ``n`` disjoint pipeline splits.
          """
          return self._split(
              n,
              lambda ds: ds.split(n, equal=equal, locality_hints=locality_hints))
  
      def split_at_indices(self,
                           indices: List[int]) -> List["DatasetPipeline[T]"]:
          """Split the datasets within the pipeline at the given indices
          (like np.split).
  
          This will split each dataset contained within this pipeline, thereby
          producing len(indices) + 1 pipelines with the first pipeline containing
          the [0, indices[0]) slice from each dataset, the second pipeline
          containing the [indices[0], indices[1]) slice from each dataset, and so
          on, with the final pipeline will containing the
          [indices[-1], self.count()) slice from each dataset.
  
          Examples:
              >>> p1, p2, p3 = ray.data.range(
                      8).repeat(2).split_at_indices([2, 5])
              >>> p1.take()
              [0, 1, 0, 1]
              >>> p2.take()
              [2, 3, 4, 2, 3, 4]
              >>> p3.take()
              [5, 6, 7, 5, 6, 7]
  
          Time complexity: O(num splits)
  
          See also: ``DatasetPipeline.split``
  
          Args:
              indices: List of sorted integers which indicate where the pipeline
                  will be split. If an index exceeds the length of the pipeline,
                  an empty pipeline will be returned.
  
          Returns:
              The pipeline splits.
          """
  
          if len(indices) < 1:
              raise ValueError("indices must be at least of length 1")
          if sorted(indices) != indices:
              raise ValueError("indices must be sorted")
          if indices[0] < 0:
              raise ValueError("indices must be positive")
  
          return self._split(
              len(indices) + 1, lambda ds: ds.split_at_indices(indices))
  
      def _split(self, n: int,
                 splitter: Callable[[Dataset], "DatasetPipeline[T]"]):
  
          coordinator = PipelineSplitExecutorCoordinator.remote(
              self, n, splitter)
          if self._executed[0]:
              raise RuntimeError("Pipeline cannot be read multiple times.")
          self._executed[0] = True
  
          class SplitIterator:
              def __init__(self, split_index, coordinator):
                  self.split_index = split_index
                  self.coordinator = coordinator
                  self.warn_threshold = 100
                  self.wait_delay_s = 0.1
  
              def __iter__(self):
                  return self
  
              def __next__(self):
                  ds = None
                  tries = 0
                  while ds is None:
                      ds = ray.get(
                          self.coordinator.next_dataset_if_ready.remote(
                              self.split_index))
                      # Wait for other shards to catch up reading.
                      if not ds:
                          time.sleep(self.wait_delay_s)
                          tries += 1
                      if tries > self.warn_threshold:
                          print("Warning: reader on shard {} of the pipeline "
                                "has been blocked more than {}s waiting for "
                                "other readers to catch up. All pipeline shards "
                                "must be read from concurrently.".format(
                                    self.split_index,
                                    self.wait_delay_s * self.warn_threshold))
                          self.warn_threshold *= 2
                  return lambda: ds
  
          return [
              # Disable progress bars for the split readers since they would
              # overwhelm the console.
              DatasetPipeline(
                  SplitIterator(idx, coordinator), progress_bars=False)
              for idx in range(n)
          ]
  
      def rewindow(self, *, blocks_per_window: int) -> "DatasetPipeline[T]":
          """Change the windowing (blocks per dataset) of this pipeline.
  
          Changes the windowing of this pipeline to the specified size. For
          example, if the current pipeline has two blocks per dataset, and
          `.rewindow(blocks_per_window=4)` is requested, adjacent datasets will
          be merged until each dataset is 4 blocks. If
          `.rewindow(blocks_per_window)` was requested the datasets will be
          split into smaller windows.
  
          Args:
              blocks_per_window: The new target blocks per window.
          """
  
          class WindowIterator:
              def __init__(self, original_iter):
                  self._original_iter = original_iter
                  self._buffer: Optional[Dataset[T]] = None
  
              def __next__(self) -> Dataset[T]:
                  try:
                      # Merge windows until we meet the requested window size.
                      if self._buffer is None:
                          self._buffer = next(self._original_iter)
                      while self._buffer.num_blocks() < blocks_per_window:
                          self._buffer = self._buffer.union(
                              next(self._original_iter))
                      # Slice off the left-most chunk and return it.
                      res, self._buffer = self._buffer._divide(blocks_per_window)
                      assert res.num_blocks() <= blocks_per_window, res
                      return lambda: res
                  except StopIteration:
                      # Return the left-over data as a single window.
                      if self._buffer and self._buffer.num_blocks() > 0:
                          res = self._buffer
                          assert res.num_blocks() <= blocks_per_window, res
                          self._buffer = None
                          return lambda: res
                      else:
                          raise
  
          class WindowIterable:
              def __init__(self, original_iter):
                  self._original_iter = original_iter
  
              def __iter__(self):
                  return WindowIterator(self._original_iter)
  
          return DatasetPipeline(
              WindowIterable(self.iter_datasets()), length=None)
  
      def repeat(self, times: int = None) -> "DatasetPipeline[T]":
          """Repeat this pipeline a given number or times, or indefinitely.
  
          This operation is only allowed for pipelines of a finite length. An
          error will be raised for pipelines of infinite length.
  
          Transformations prior to the call to ``repeat()`` are evaluated once.
          Transformations done on the repeated pipeline are evaluated on each
          loop of the pipeline over the base pipeline.
  
          Args:
              times: The number of times to loop over this pipeline, or None
                  to repeat indefinitely.
          """
  
          if self._length == float("inf"):
              raise ValueError("Cannot repeat a pipeline of infinite length.")
  
          class RepeatIterator:
              def __init__(self, original_iter):
                  self._original_iter = original_iter
                  # Holds results to repeat.
                  self._results = []
                  # Incrementing cursor over results.
                  self._i = 0
                  # This is calculated later.
                  self._max_i = None
  
              def __next__(self) -> Dataset[T]:
                  # Still going through the original pipeline.
                  if self._original_iter:
                      try:
                          res = next(self._original_iter)
                          self._results.append(res)
                          return lambda: res
                      except StopIteration:
                          self._original_iter = None
                          # Calculate the cursor limit.
                          if times:
                              self._max_i = len(self._results) * (times - 1)
                          else:
                              self._max_i = float("inf")
                  # Going through a repeat of the pipeline.
                  if self._i < self._max_i:
                      res = self._results[self._i % len(self._results)]
                      self._i += 1
                      return lambda: res
                  else:
                      raise StopIteration
  
          class RepeatIterable:
              def __init__(self, original_iter):
                  self._original_iter = original_iter
  
              def __iter__(self):
                  return RepeatIterator(self._original_iter)
  
          if not times:
              length = float("inf")
          elif times and self._length:
              length = times * self._length
          else:
              length = None
  
          return DatasetPipeline(
              RepeatIterable(self.iter_datasets()), length=length)
  
      def schema(self) -> Union[type, "pyarrow.lib.Schema"]:
          """Return the schema of the dataset pipeline.
  
          For datasets of Arrow records, this will return the Arrow schema.
          For dataset of Python objects, this returns their Python type.
  
          Time complexity: O(1)
  
          Returns:
              The Python type or Arrow schema of the records, or None if the
              schema is not known.
          """
          return next(self.iter_datasets()).schema()
  
      def count(self) -> int:
          """Count the number of records in the dataset pipeline.
  
          This blocks until the entire pipeline is fully executed.
  
          Time complexity: O(dataset size / parallelism)
  
          Returns:
              The number of records in the dataset pipeline.
          """
          pipe = self.map_batches(lambda batch: [len(batch)])
          total = 0
          for elem in pipe.iter_rows():
              total += elem
          return total
  
      def sum(self) -> int:
          """Sum the records in the dataset pipeline.
  
          This blocks until the entire pipeline is fully executed.
  
          Time complexity: O(dataset size / parallelism)
  
          Returns:
              The sum of the records in the dataset pipeline.
          """
          pipe = self.map_batches(
              lambda batch: [batch.sum()[0]], batch_format="pandas")
          total = 0
          for elem in pipe.iter_rows():
              total += elem
          return total
  
      def show_windows(self, limit_per_dataset: int = 10) -> None:
          """Print up to the given number of records from each window/dataset.
  
          This is helpful as a debugging tool for understanding the structure of
          dataset pipelines.
  
          Args:
              limit_per_dataset: Rows to print per window/dataset.
          """
          for i, ds in enumerate(self.iter_datasets()):
              print("=== Window {} ===".format(i))
              ds.show(limit_per_dataset)
  
      @DeveloperAPI
      def iter_datasets(self) -> Iterator[Dataset[T]]:
          """Iterate over the output datasets of this pipeline.
  
          Returns:
              Iterator over the datasets outputted from this pipeline.
          """
          if self._executed[0]:
              raise RuntimeError("Pipeline cannot be read multiple times.")
          self._executed[0] = True
          return PipelineExecutor(self)
  
      @DeveloperAPI
      def foreach_window(self, fn: Callable[[Dataset[T]], Dataset[U]]
                         ) -> "DatasetPipeline[U]":
          """Apply a transform to each dataset/window in this pipeline.
  
          Args:
              fn: The function to transform each dataset with.
  
          Returns:
              The transformed DatasetPipeline.
          """
          if self._executed[0]:
              raise RuntimeError("Pipeline cannot be read multiple times.")
          return DatasetPipeline(
              self._base_iterable,
              self._stages + [fn],
              self._length,
              self._progress_bars,
              _executed=self._executed)
  
      def foreach_dataset(self, *a, **kw) -> None:
          raise DeprecationWarning(
              "`foreach_dataset` has been renamed to `foreach_window`.")
  
      @staticmethod
      def from_iterable(iterable: Iterable[Callable[[], Dataset[T]]],
                        ) -> "DatasetPipeline[T]":
          """Create a pipeline from an sequence of Dataset producing functions.
  
          Args:
              iterable: A finite or infinite-length sequence of functions that
                  each produce a Dataset when called.
          """
          if hasattr(iterable, "__len__"):
              length = len(iterable)
          else:
              length = None
          return DatasetPipeline(iterable, length=length)
  
      def __repr__(self) -> str:
          return "DatasetPipeline(num_windows={}, num_stages={})".format(
              self._length, 1 + len(self._stages))
  
      def __str__(self) -> str:
          return repr(self)
  
      def _get_uuid(self) -> str:
          return self._uuid
  
      def _set_uuid(self, uuid: str) -> None:
          self._uuid = uuid
  
  
  for method in PER_DATASET_OPS:
  
      def make_impl(method):
          delegate = getattr(Dataset, method)
  
          @functools.wraps(delegate)
          def impl(self, *args, **kwargs):
              return self.foreach_window(
                  lambda ds: getattr(ds, method)(*args, **kwargs))
  
          if impl.__annotations__.get("return"):
              impl.__annotations__["return"] = impl.__annotations__[
                  "return"].replace("Dataset", "DatasetPipeline")
  
          return impl
  
      setattr(DatasetPipeline, method, make_impl(method))
  
  for method in HOLISTIC_PER_DATASET_OPS:
  
      def make_impl(method):
          delegate = getattr(Dataset, method)
  
          @functools.wraps(delegate)
          def impl(self, *args, **kwargs):
              return self.foreach_window(
                  lambda ds: getattr(ds, method)(*args, **kwargs))
  
          if impl.__annotations__.get("return"):
              impl.__annotations__["return"] = impl.__annotations__[
                  "return"].replace("Dataset", "DatasetPipeline")
  
          return impl
  
      def deprecation_warning(method: str):
          def impl(*a, **kw):
              raise DeprecationWarning(
                  "`{}` has been renamed to `{}_each_window`.".format(
                      method, method))
  
          return impl
  
      setattr(DatasetPipeline, method, deprecation_warning(method))
      setattr(DatasetPipeline, method + "_each_window", make_impl(method))
  
  for method in PER_DATASET_OUTPUT_OPS:
  
      def make_impl(method):
          delegate = getattr(Dataset, method)
  
          @functools.wraps(delegate)
          def impl(self, *args, **kwargs):
              uuid = None
              for i, ds in enumerate(self.iter_datasets()):
                  if uuid is None:
                      uuid = self._get_uuid() or ds._get_uuid()
                  ds._set_uuid(f"{uuid}_{i:06}")
                  getattr(ds, method)(*args, **kwargs)
  
          return impl
  
      setattr(DatasetPipeline, method, make_impl(method))
  
  for method in OUTPUT_ITER_OPS:
  
      def make_impl(method):
          delegate = getattr(Dataset, method)
  
          @functools.wraps(delegate)
          def impl(self, *args, **kwargs):
              return delegate(self, *args, **kwargs)
  
          return impl
  
      setattr(DatasetPipeline, method, make_impl(method))
  