


'''simple docstring'''






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__A					:  Optional[int]											=      logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")






@dataclass
class    __snake_case       :







    """simple docstring"""



    lowercase    =					field(
        default='cifar10'	,metadata={'help': 'Name of a dataset from the datasets package'})
    lowercase    =					field(
        default=_SCREAMING_SNAKE_CASE	,metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'})
    lowercase    =					field(
        default=_SCREAMING_SNAKE_CASE	,metadata={'help': 'The column name of the images in the files.'})
    lowercase    =					field(default=_SCREAMING_SNAKE_CASE	,metadata={'help': 'A folder containing the training data.'})
    lowercase    =					field(default=_SCREAMING_SNAKE_CASE	,metadata={'help': 'A folder containing the validation data.'})
    lowercase    =					field(
        default=0.15	,metadata={'help': 'Percent to split off of train for validation.'})
    lowercase    =					field(
        default=_SCREAMING_SNAKE_CASE	,metadata={
            'help': (
                'For debugging purposes or quicker training, truncate the number of training examples to this '
                'value if set.'
            )
        }	,)
    lowercase    =					field(
        default=_SCREAMING_SNAKE_CASE	,metadata={
            'help': (
                'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
                'value if set.'
            )
        }	,)

    def __lowercase   (   self    :  str		)   ->  Dict:
      lowerCAmelCase_			:      Any			=   {}
      if self.train_dir is not None:
        lowerCAmelCase_			:      Optional[int]			=   self.train_dir
      if self.validation_dir is not None:
        lowerCAmelCase_			:      Optional[int]			=   self.validation_dir
      lowerCAmelCase_			:      Any			=   data_files if data_files else None







@dataclass
class    __snake_case       :







    """simple docstring"""



    lowercase    =					field(
        default=_SCREAMING_SNAKE_CASE	,metadata={
            'help': (
                'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
            )
        }	,)
    lowercase    =					field(
        default=_SCREAMING_SNAKE_CASE	,metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'})
    lowercase    =					field(
        default=_SCREAMING_SNAKE_CASE	,metadata={
            'help': (
                'Override some existing default config settings when a model is trained from scratch. Example: '
                'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
            )
        }	,)
    lowercase    =					field(
        default=_SCREAMING_SNAKE_CASE	,metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'})
    lowercase    =					field(
        default='main'	,metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}	,)
    lowercase    =					field(default=_SCREAMING_SNAKE_CASE	,metadata={'help': 'Name or path of preprocessor config.'})
    lowercase    =					field(
        default=_SCREAMING_SNAKE_CASE	,metadata={
            'help': (
                'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
                'with private models).'
            )
        }	,)
    lowercase    =					field(
        default=0.75	,metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'})
    lowercase    =					field(
        default=_SCREAMING_SNAKE_CASE	,metadata={'help': 'Whether or not to train with normalized pixel values as target.'})







@dataclass
class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					field(
        default=1E-3	,metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'})







def 				UpperCamelCase_			(    A__					:   Optional[int]					):
  '''simple docstring'''





  lowerCAmelCase_			:      Optional[Any]			=   torch.stack([example["""pixel_values"""] for example in examples]					)
  return {"pixel_values": pixel_values}







def 				UpperCamelCase_			(    ):
  '''simple docstring'''





  lowerCAmelCase_			:      List[Any]			=   HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)					)
  if len(sys.argv					) == 2 and sys.argv[1].endswith(""".json"""					):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      List[Any]			=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
  else:
    lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      int			=   parser.parse_args_into_dataclasses()

  # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
  # information sent is the one passed as arguments along with your Python/PyTorch versions.
  send_example_telemetry("""run_mae"""							,    A__							,    A__					)

  # Setup logging
  logging.basicConfig(
      format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""							,    datefmt="""%m/%d/%Y %H:%M:%S"""							,    handlers=[logging.StreamHandler(sys.stdout					)]							,    )

  if training_args.should_log:
    # The default of training_args.log_level is passive, so we set log level at info here to have that default.
    transformers.utils.logging.set_verbosity_info()

  lowerCAmelCase_			:      Dict			=   training_args.get_process_log_level()
  logger.setLevel(A__					)
  transformers.utils.logging.set_verbosity(A__					)
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()

  # Log on each process the small summary:
  logger.warning(
      f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
      + f'distributed training: {bool(training_args.local_rank != -1					)}, 16-bits training: {training_args.fpaa}'					)
  logger.info(f'Training/evaluation parameters {training_args}'					)

  # Detecting last checkpoint.
  lowerCAmelCase_			:      List[str]			=   None
  if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
    lowerCAmelCase_			:      Union[str, Any]			=   get_last_checkpoint(training_args.output_dir					)
    if last_checkpoint is None and len(os.listdir(training_args.output_dir					)					) > 0:
      raise ValueError(
          f'Output directory ({training_args.output_dir}) already exists and is not empty. '
          """Use --overwrite_output_dir to overcome."""					)
    elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
      logger.info(
          f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
          """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""					)

    # Initialize our dataset.
  lowerCAmelCase_			:      Optional[Any]			=   load_dataset(
      data_args.dataset_name							,    data_args.dataset_config_name							,    data_files=data_args.data_files							,    cache_dir=model_args.cache_dir							,    use_auth_token=True if model_args.use_auth_token else None							,    )

  # If we don't have a validation split, split off a percentage of train as validation.
  lowerCAmelCase_			:      Optional[int]			=   None if """validation""" in ds.keys() else data_args.train_val_split
  if isinstance(data_args.train_val_split							,    A__					) and data_args.train_val_split > 0.0:
    lowerCAmelCase_			:      Tuple			=   ds["""train"""].train_test_split(data_args.train_val_split					)
    lowerCAmelCase_			:      List[str]			=   split["""train"""]
    lowerCAmelCase_			:      Tuple			=   split["""test"""]

  # Load pretrained model and image processor
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  lowerCAmelCase_			:      Tuple			=   {
      """cache_dir""": model_args.cache_dir,
      """revision""": model_args.model_revision,
      """use_auth_token""": True if model_args.use_auth_token else None,
  }
  if model_args.config_name:
    lowerCAmelCase_			:      Optional[Any]			=   ViTMAEConfig.from_pretrained(model_args.config_name							,    **A__					)
  elif model_args.model_name_or_path:
    lowerCAmelCase_			:      List[str]			=   ViTMAEConfig.from_pretrained(model_args.model_name_or_path							,    **A__					)
  else:
    lowerCAmelCase_			:      List[Any]			=   ViTMAEConfig()
    logger.warning("""You are instantiating a new config instance from scratch."""					)
    if model_args.config_overrides is not None:
      logger.info(f'Overriding config: {model_args.config_overrides}'					)
      config.update_from_string(model_args.config_overrides					)
      logger.info(f'New config: {config}'					)

    # adapt config
  config.update(
      {
          """mask_ratio""": model_args.mask_ratio,
          """norm_pix_loss""": model_args.norm_pix_loss,
      }					)

  # create image processor
  if model_args.image_processor_name:
    lowerCAmelCase_			:      Optional[Any]			=   ViTImageProcessor.from_pretrained(model_args.image_processor_name							,    **A__					)
  elif model_args.model_name_or_path:
    lowerCAmelCase_			:      List[Any]			=   ViTImageProcessor.from_pretrained(model_args.model_name_or_path							,    **A__					)
  else:
    lowerCAmelCase_			:      Tuple			=   ViTImageProcessor()

  # create model
  if model_args.model_name_or_path:
    lowerCAmelCase_			:      int			=   ViTMAEForPreTraining.from_pretrained(
        model_args.model_name_or_path							,    from_tf=bool(""".ckpt""" in model_args.model_name_or_path					)							,    config=A__							,    cache_dir=model_args.cache_dir							,    revision=model_args.model_revision							,    use_auth_token=True if model_args.use_auth_token else None							,    )
  else:
    logger.info("""Training new model from scratch"""					)
    lowerCAmelCase_			:      Union[str, Any]			=   ViTMAEForPreTraining(A__					)

  if training_args.do_train:
    lowerCAmelCase_			:      List[str]			=   ds["""train"""].column_names
  else:
    lowerCAmelCase_			:      int			=   ds["""validation"""].column_names

  if data_args.image_column_name is not None:
    lowerCAmelCase_			:      str			=   data_args.image_column_name
  elif "image" in column_names:
    lowerCAmelCase_			:      Tuple			=   """image"""
  elif "img" in column_names:
    lowerCAmelCase_			:      Union[str, Any]			=   """img"""
  else:
    lowerCAmelCase_			:      str			=   column_names[0]

  # transformations as done in original MAE paper
  # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
  if "shortest_edge" in image_processor.size:
    lowerCAmelCase_			:      Optional[int]			=   image_processor.size["""shortest_edge"""]
  else:
    lowerCAmelCase_			:      str			=   (image_processor.size["""height"""], image_processor.size["""width"""])
  lowerCAmelCase_			:      Optional[int]			=   Compose(
      [
          Lambda(lambda A__					: img.convert("""RGB"""					) if img.mode != "RGB" else img					),
          RandomResizedCrop(A__							,    scale=(0.2, 1.0)							,    interpolation=InterpolationMode.BICUBIC					),
          RandomHorizontalFlip(),
          ToTensor(),
          Normalize(mean=image_processor.image_mean							,    std=image_processor.image_std					),
      ]					)

  def preprocess_images(A__					:   Any					):
    lowerCAmelCase_			:      List[Any]			=   [transforms(A__					) for image in examples[image_column_name]]
    return examples

  if training_args.do_train:
    if "train" not in ds:
      raise ValueError("""--do_train requires a train dataset"""					)
    if data_args.max_train_samples is not None:
      lowerCAmelCase_			:      Optional[Any]			=   ds["""train"""].shuffle(seed=training_args.seed					).select(range(data_args.max_train_samples					)					)
    # Set the training transforms
    ds["train"].set_transform(A__					)

  if training_args.do_eval:
    if "validation" not in ds:
      raise ValueError("""--do_eval requires a validation dataset"""					)
    if data_args.max_eval_samples is not None:
      lowerCAmelCase_			:      Any			=   (
          ds["""validation"""].shuffle(seed=training_args.seed					).select(range(data_args.max_eval_samples					)					)
      )
    # Set the validation transforms
    ds["validation"].set_transform(A__					)

  # Compute absolute learning rate
  lowerCAmelCase_			:      str			=   (
      training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
  )
  if training_args.base_learning_rate is not None:
    lowerCAmelCase_			:      str			=   training_args.base_learning_rate * total_train_batch_size / 2_56

  # Initialize our trainer
  lowerCAmelCase_			:      List[str]			=   Trainer(
      model=A__							,    args=A__							,    train_dataset=ds["""train"""] if training_args.do_train else None							,    eval_dataset=ds["""validation"""] if training_args.do_eval else None							,    tokenizer=A__							,    data_collator=A__							,    )

  # Training
  if training_args.do_train:
    lowerCAmelCase_			:      Optional[Any]			=   None
    if training_args.resume_from_checkpoint is not None:
      lowerCAmelCase_			:      int			=   training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      lowerCAmelCase_			:      Optional[Any]			=   last_checkpoint
    lowerCAmelCase_			:      Any			=   trainer.train(resume_from_checkpoint=A__					)
    trainer.save_model()
    trainer.log_metrics("""train"""							,    train_result.metrics					)
    trainer.save_metrics("""train"""							,    train_result.metrics					)
    trainer.save_state()

  # Evaluation
  if training_args.do_eval:
    lowerCAmelCase_			:      Any			=   trainer.evaluate()
    trainer.log_metrics("""eval"""							,    A__					)
    trainer.save_metrics("""eval"""							,    A__					)

  # Write model card and (optionally) push to hub
  lowerCAmelCase_			:      Optional[Any]			=   {
      """tasks""": """masked-auto-encoding""",
      """dataset""": data_args.dataset_name,
      """tags""": ["""masked-auto-encoding"""],
  }
  if training_args.push_to_hub:
    trainer.push_to_hub(**A__					)
  else:
    trainer.create_model_card(**A__					)







def 				UpperCamelCase_			(    A__					:   List[str]					):
  '''simple docstring'''





  main()


if __name__ == "__main__":
  main()







'''simple docstring'''






import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


__A					:  Optional[Any]											=      datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
  import pyspark






@dataclass
class    __snake_case       (   datasets.BuilderConfig):







    """simple docstring"""



    lowercase    =					None







def 				UpperCamelCase_			(    A__					:   "pyspark.sql.DataFrame"							,    A__					:   List[int]							,    ):
  '''simple docstring'''





  import pyspark

  def generate_fn():
    lowerCAmelCase_			:      Union[str, Any]			=   df.select("""*"""							,    pyspark.sql.functions.spark_partition_id().alias("""part_id"""					)					)
    for partition_id in partition_order:
      lowerCAmelCase_			:      int			=   df_with_partition_id.select("""*"""					).where(f'part_id = {partition_id}'					).drop("""part_id"""					)
      lowerCAmelCase_			:      Union[str, Any]			=   partition_df.collect()
      lowerCAmelCase_			:      str			=   0
      for row in rows:
        yield f'{partition_id}_{row_id}', row.asDict()
        row_id += 1

  return generate_fn






class    __snake_case       (   _BaseExamplesIterable):







    """simple docstring"""



    def __init__(   self    :  Optional[int]    ,						lowerCamelCase    :  "pyspark.sql.DataFrame"    ,						lowerCamelCase    :  List[Any]=None    ,						)   ->  Optional[Any]:
      lowerCAmelCase_			:      str			=   df
      lowerCAmelCase_			:      List[Any]			=   partition_order or range(self.df.rdd.getNumPartitions()		)
      lowerCAmelCase_			:      List[str]			=   _generate_iterable_examples(self.df    ,						self.partition_order		)

    def __iter__(   self    :  Dict		)   ->  int:
      yield from self.generate_examples_fn()

    def __lowercase   (   self    :  Any    ,						lowerCamelCase    :  np.random.Generator		)   ->  "SparkExamplesIterable":
      lowerCAmelCase_			:      Optional[int]			=   list(range(self.df.rdd.getNumPartitions()		)		)
      generator.shuffle(lowerCamelCase		)
      return SparkExamplesIterable(self.df    ,						partition_order=lowerCamelCase		)

    def __lowercase   (   self    :  Optional[Any]    ,						lowerCamelCase    :  int    ,						lowerCamelCase    :  int		)   ->  "SparkExamplesIterable":
      lowerCAmelCase_			:      Tuple			=   self.split_shard_indices_by_worker(lowerCamelCase    ,						lowerCamelCase		)
      return SparkExamplesIterable(self.df    ,						partition_order=lowerCamelCase		)





    @property
    def __lowercase   (   self    :  str		)   ->  int:
      return len(self.partition_order		)







class    __snake_case       (   datasets.DatasetBuilder):







    """simple docstring"""



    lowercase    =					SparkConfig

    def __init__(   self    :  Union[str, Any]    ,						lowerCamelCase    :  "pyspark.sql.DataFrame"    ,						lowerCamelCase    :  str = None    ,						lowerCamelCase    :  str = None    ,						**lowerCamelCase    :  int    ,						)   ->  Tuple:
      import pyspark

      lowerCAmelCase_			:      Dict			=   pyspark.sql.SparkSession.builder.getOrCreate()
      lowerCAmelCase_			:      List[str]			=   df
      lowerCAmelCase_			:      List[Any]			=   working_dir

      super().__init__(
          cache_dir=lowerCamelCase    ,						config_name=str(self.df.semanticHash()		)    ,						**lowerCamelCase    ,						)

    def __lowercase   (   self    :  Tuple		)   ->  Union[str, Any]:
      # Returns the path of the created file.
      def create_cache_and_write_probe(lowerCamelCase    :  int		):
        # makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
        # already exist.
        os.makedirs(self._cache_dir    ,						exist_ok=lowerCamelCase		)
        lowerCAmelCase_			:      List[str]			=   os.path.join(self._cache_dir    ,						"""fs_test""" + uuid.uuida().hex		)
        # Opening the file in append mode will create a new file unless it already exists, in which case it will not
        # change the file contents.
        open(lowerCamelCase    ,						"""a"""		)
        return [probe_file]

      if self._spark.conf.get("""spark.master"""    ,						""""""		).startswith("""local"""		):
        return

      # If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
      # accessible to the driver.
      # TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
      if self._cache_dir:
        lowerCAmelCase_			:      str			=   (
            self._spark.sparkContext.parallelize(range(1		)    ,						1		).mapPartitions(lowerCamelCase		).collect()
        )
        if os.path.isfile(probe[0]		):
          return

      raise ValueError(
          """When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir"""		)

    def __lowercase   (   self    :  Any		)   ->  List[Any]:
      return datasets.DatasetInfo(features=self.config.features		)

    def __lowercase   (   self    :  str    ,						lowerCamelCase    :  datasets.download.download_manager.DownloadManager		)   ->  Optional[int]:
      return [datasets.SplitGenerator(name=datasets.Split.TRAIN		)]

    def __lowercase   (   self    :  Any    ,						lowerCamelCase    :  Tuple		)   ->  Optional[Any]:
      import pyspark

      def get_arrow_batch_size(lowerCamelCase    :  Any		):
        for batch in it:
          yield pa.RecordBatch.from_pydict({"""batch_bytes""": [batch.nbytes]}		)

      lowerCAmelCase_			:      str			=   self.df.count()
      lowerCAmelCase_			:      Any			=   df_num_rows if df_num_rows <= 1_00 else 1_00
      # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
      lowerCAmelCase_			:      str			=   (
          self.df.limit(lowerCamelCase		)
          .repartition(1		)
          .mapInArrow(lowerCamelCase    ,						"""batch_bytes: long"""		)
          .agg(pyspark.sql.functions.sum("""batch_bytes"""		).alias("""sample_bytes"""		)		)
          .collect()[0]
          .sample_bytes
          / sample_num_rows
      )
      lowerCAmelCase_			:      List[Any]			=   approx_bytes_per_row * df_num_rows
      if approx_total_size > max_shard_size:
        # Make sure there is at least one row per partition.
        lowerCAmelCase_			:      Tuple			=   min(lowerCamelCase    ,						int(approx_total_size / max_shard_size		)		)
        lowerCAmelCase_			:      Dict			=   self.df.repartition(lowerCamelCase		)

    def __lowercase   (   self    :  Optional[int]    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  int    ,						)   ->  Iterable[Tuple[int, bool, Union[int, tuple]]]:
      import pyspark

      lowerCAmelCase_			:      List[str]			=   ParquetWriter if file_format == """parquet""" else ArrowWriter
      lowerCAmelCase_			:      int			=   os.path.join(self._working_dir    ,						os.path.basename(lowerCamelCase		)		) if self._working_dir else fpath
      lowerCAmelCase_			:      Tuple			=   file_format == """parquet"""

      # Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
      # pickling the SparkContext.
      lowerCAmelCase_			:      str			=   self.config.features
      lowerCAmelCase_			:      Tuple			=   self._writer_batch_size
      lowerCAmelCase_			:      Any			=   self._fs.storage_options

      def write_arrow(lowerCamelCase    :  Optional[int]		):
        # Within the same SparkContext, no two task attempts will share the same attempt ID.
        lowerCAmelCase_			:      Union[str, Any]			=   pyspark.TaskContext().taskAttemptId()
        lowerCAmelCase_			:      Union[str, Any]			=   next(lowerCamelCase    ,						lowerCamelCase		)
        if first_batch is None:
          # Some partitions might not receive any data.
          return pa.RecordBatch.from_arrays(
              [[task_id], [0], [0]]    ,						names=["""task_id""", """num_examples""", """num_bytes"""]    ,						)
        lowerCAmelCase_			:      List[Any]			=   0
        lowerCAmelCase_			:      Dict			=   writer_class(
            features=lowerCamelCase    ,						path=working_fpath.replace("""SSSSS"""    ,						F'{shard_id:05d}'		).replace("""TTTTT"""    ,						F'{task_id:05d}'		)    ,						writer_batch_size=lowerCamelCase    ,						storage_options=lowerCamelCase    ,						embed_local_files=lowerCamelCase    ,						)
        lowerCAmelCase_			:      str			=   pa.Table.from_batches([first_batch]		)
        writer.write_table(lowerCamelCase		)
        for batch in it:
          if max_shard_size is not None and writer._num_bytes >= max_shard_size:
            lowerCAmelCase_,							lowerCAmelCase_			:      Any			=   writer.finalize()
            writer.close()
            yield pa.RecordBatch.from_arrays(
                [[task_id], [num_examples], [num_bytes]]    ,						names=["""task_id""", """num_examples""", """num_bytes"""]    ,						)
            shard_id += 1
            lowerCAmelCase_			:      Optional[int]			=   writer_class(
                features=writer._features    ,						path=working_fpath.replace("""SSSSS"""    ,						F'{shard_id:05d}'		).replace("""TTTTT"""    ,						F'{task_id:05d}'		)    ,						writer_batch_size=lowerCamelCase    ,						storage_options=lowerCamelCase    ,						embed_local_files=lowerCamelCase    ,						)
          lowerCAmelCase_			:      Tuple			=   pa.Table.from_batches([batch]		)
          writer.write_table(lowerCamelCase		)

        if writer._num_bytes > 0:
          lowerCAmelCase_,							lowerCAmelCase_			:      List[str]			=   writer.finalize()
          writer.close()
          yield pa.RecordBatch.from_arrays(
              [[task_id], [num_examples], [num_bytes]]    ,						names=["""task_id""", """num_examples""", """num_bytes"""]    ,						)

        if working_fpath != fpath:
          for file in os.listdir(os.path.dirname(lowerCamelCase		)		):
            lowerCAmelCase_			:      str			=   os.path.join(os.path.dirname(lowerCamelCase		)    ,						os.path.basename(lowerCamelCase		)		)
            shutil.move(lowerCamelCase    ,						lowerCamelCase		)

      lowerCAmelCase_			:      Dict			=   (
          self.df.mapInArrow(lowerCamelCase    ,						"""task_id: long, num_examples: long, num_bytes: long"""		)
          .groupBy("""task_id"""		)
          .agg(
              pyspark.sql.functions.sum("""num_examples"""		).alias("""total_num_examples"""		)    ,						pyspark.sql.functions.sum("""num_bytes"""		).alias("""total_num_bytes"""		)    ,						pyspark.sql.functions.count("""num_bytes"""		).alias("""num_shards"""		)    ,						pyspark.sql.functions.collect_list("""num_examples"""		).alias("""shard_lengths"""		)    ,						)
          .collect()
      )
      for row in stats:
        yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)

    def __lowercase   (   self    :  Any    ,						lowerCamelCase    :  "datasets.SplitGenerator"    ,						lowerCamelCase    :  str = "arrow"    ,						lowerCamelCase    :  Optional[Union[str, int]] = None    ,						lowerCamelCase    :  Optional[int] = None    ,						**lowerCamelCase    :  List[str]    ,						)   ->  Optional[int]:
      self._validate_cache_dir()

      lowerCAmelCase_			:      List[str]			=   convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE		)
      self._repartition_df_if_needed(lowerCamelCase		)
      lowerCAmelCase_			:      Optional[int]			=   not is_remote_filesystem(self._fs		)
      lowerCAmelCase_			:      Dict			=   os.path.join if is_local else posixpath.join

      lowerCAmelCase_			:      int			=   """-TTTTT-SSSSS-of-NNNNN"""
      lowerCAmelCase_			:      Any			=   F'{self.name}-{split_generator.name}{SUFFIX}.{file_format}'
      lowerCAmelCase_			:      List[str]			=   path_join(self._output_dir    ,						lowerCamelCase		)

      lowerCAmelCase_			:      Dict			=   0
      lowerCAmelCase_			:      int			=   0
      lowerCAmelCase_			:      Dict			=   0
      lowerCAmelCase_			:      Any			=   []
      lowerCAmelCase_			:      Dict			=   []

      for task_id, content in self._prepare_split_single(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		):
        (
            (
            lowerCAmelCase_
        ),							(
            lowerCAmelCase_
        ),							(
            lowerCAmelCase_
        ),							(
            lowerCAmelCase_
        ),							
        )			:      str			=   content
        if num_bytes > 0:
          total_num_examples += num_examples
          total_num_bytes += num_bytes
          total_shards += num_shards
          task_id_and_num_shards.append((task_id, num_shards)		)
          all_shard_lengths.extend(lowerCamelCase		)

      lowerCAmelCase_			:      Union[str, Any]			=   total_num_examples
      lowerCAmelCase_			:      int			=   total_num_bytes

      # should rename everything at the end
      logger.debug(F'Renaming {total_shards} shards.'		)
      if total_shards > 1:
        lowerCAmelCase_			:      Optional[int]			=   all_shard_lengths

        # Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
        # pickling error due to pickling the SparkContext.
        lowerCAmelCase_			:      List[Any]			=   self._fs

        # use the -SSSSS-of-NNNNN pattern
        def _rename_shard(
            lowerCamelCase    :  int    ,						lowerCamelCase    :  int    ,						lowerCamelCase    :  int    ,						):
          rename(
              lowerCamelCase    ,						fpath.replace("""SSSSS"""    ,						F'{shard_id:05d}'		).replace("""TTTTT"""    ,						F'{task_id:05d}'		)    ,						fpath.replace("""TTTTT-SSSSS"""    ,						F'{global_shard_id:05d}'		).replace("""NNNNN"""    ,						F'{total_shards:05d}'		)    ,						)

        lowerCAmelCase_			:      Any			=   []
        lowerCAmelCase_			:      str			=   0
        for i in range(len(lowerCamelCase		)		):
          lowerCAmelCase_,							lowerCAmelCase_			:      Tuple			=   task_id_and_num_shards[i]
          for shard_id in range(lowerCamelCase		):
            args.append([task_id, shard_id, global_shard_id]		)
            global_shard_id += 1
        self._spark.sparkContext.parallelize(lowerCamelCase    ,						len(lowerCamelCase		)		).map(lambda lowerCamelCase		: _rename_shard(*lowerCamelCase		)		).collect()
      else:
        # don't use any pattern
        lowerCAmelCase_			:      List[Any]			=   0
        lowerCAmelCase_			:      str			=   task_id_and_num_shards[0][0]
        self._rename(
            fpath.replace("""SSSSS"""    ,						F'{shard_id:05d}'		).replace("""TTTTT"""    ,						F'{task_id:05d}'		)    ,						fpath.replace(lowerCamelCase    ,						""""""		)    ,						)





    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  "datasets.SplitGenerator"    ,						)   ->  SparkExamplesIterable:
      return SparkExamplesIterable(self.df		)



