

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


lowerCAmelCase     :   Dict						     =  datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
    import pyspark
@dataclass
class  _A		(  datasets.BuilderConfig):
  SCREAMING_SNAKE_CASE    :      Optional[datasets.Features]       =  None

def    A_  (							a					,   a					,   ):

      """simple docstring"""



      import pyspark

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

      return generate_fn
class  _A		(  _BaseExamplesIterable):

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=None ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     df
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     partition_order or range(self.df.rdd.getNumPartitions()					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     _generate_iterable_examples(self.df ,					self.partition_order					)

  def __iter__(				self					):





        """simple docstring"""







        yield from self.generate_examples_fn()

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     list(range(self.df.rdd.getNumPartitions()					)					)
        generator.shuffle(_SCREAMING_SNAKE_CASE					)
        return SparkExamplesIterable(self.df ,					partition_order=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.split_shard_indices_by_worker(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
        return SparkExamplesIterable(self.df ,					partition_order=_SCREAMING_SNAKE_CASE					)



  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return len(self.partition_order					)

class  _A		(  datasets.DatasetBuilder):
  SCREAMING_SNAKE_CASE    :      Dict       =  SparkConfig

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = None ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        import pyspark

        SCREAMING_SNAKE_CASE_				:  List[str]           =     pyspark.sql.SparkSession.builder.getOrCreate()
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     df
        SCREAMING_SNAKE_CASE_				:  Tuple           =     working_dir

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        def create_cache_and_write_probe(_SCREAMING_SNAKE_CASE					):
              # 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=_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     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(_SCREAMING_SNAKE_CASE ,					'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:
              SCREAMING_SNAKE_CASE_				:  Dict           =     (
                  self._spark.sparkContext.parallelize(range(1					) ,					1					).mapPartitions(_SCREAMING_SNAKE_CASE					).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  UpperCAmelCase    (				self					):





        """simple docstring"""







        return datasets.DatasetInfo(features=self.config.features					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        return [datasets.SplitGenerator(name=datasets.Split.TRAIN					)]

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        import pyspark

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

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

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        import pyspark

        SCREAMING_SNAKE_CASE_				:  List[str]           =     ParquetWriter if file_format == 'parquet' else ArrowWriter
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     os.path.join(self._working_dir ,					os.path.basename(_SCREAMING_SNAKE_CASE					)					) if self._working_dir else fpath
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     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.
        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.config.features
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self._writer_batch_size
        SCREAMING_SNAKE_CASE_				:  str           =     self._fs.storage_options

        def write_arrow(_SCREAMING_SNAKE_CASE					):
              # Within the same SparkContext, no two task attempts will share the same attempt ID.
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     pyspark.TaskContext().taskAttemptId()
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     next(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              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'] ,					)
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     0
              SCREAMING_SNAKE_CASE_				:  Dict           =     writer_class(
                  features=_SCREAMING_SNAKE_CASE ,					path=working_fpath.replace('SSSSS' ,					f"{shard_id:05d}"					).replace('TTTTT' ,					f"{task_id:05d}"					) ,					writer_batch_size=_SCREAMING_SNAKE_CASE ,					storage_options=_SCREAMING_SNAKE_CASE ,					embed_local_files=_SCREAMING_SNAKE_CASE ,					)
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     pa.Table.from_batches([first_batch]					)
              writer.write_table(_SCREAMING_SNAKE_CASE					)
              for batch in it:
                    if max_shard_size is not None and writer._num_bytes >= max_shard_size:
                          SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[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
                          SCREAMING_SNAKE_CASE_				:  List[Any]           =     writer_class(
                              features=writer._features ,					path=working_fpath.replace('SSSSS' ,					f"{shard_id:05d}"					).replace('TTTTT' ,					f"{task_id:05d}"					) ,					writer_batch_size=_SCREAMING_SNAKE_CASE ,					storage_options=_SCREAMING_SNAKE_CASE ,					embed_local_files=_SCREAMING_SNAKE_CASE ,					)
                    SCREAMING_SNAKE_CASE_				:  Tuple           =     pa.Table.from_batches([batch]					)
                    writer.write_table(_SCREAMING_SNAKE_CASE					)

              if writer._num_bytes > 0:
                    SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  int           =     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(_SCREAMING_SNAKE_CASE					)					):
                          SCREAMING_SNAKE_CASE_				:  str           =     os.path.join(os.path.dirname(_SCREAMING_SNAKE_CASE					) ,					os.path.basename(_SCREAMING_SNAKE_CASE					)					)
                          shutil.move(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  str           =     (
            self.df.mapInArrow(_SCREAMING_SNAKE_CASE ,					'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  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = "arrow" ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = None ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        self._validate_cache_dir()

        SCREAMING_SNAKE_CASE_				:  Dict           =     convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE					)
        self._repartition_df_if_needed(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     not is_remote_filesystem(self._fs					)
        SCREAMING_SNAKE_CASE_				:  str           =     os.path.join if is_local else posixpath.join

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     '-TTTTT-SSSSS-of-NNNNN'
        SCREAMING_SNAKE_CASE_				:  List[str]           =     f"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     path_join(self._output_dir ,					_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Tuple           =     0
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     0
        SCREAMING_SNAKE_CASE_				:  Dict           =     0
        SCREAMING_SNAKE_CASE_				:  Any           =     []
        SCREAMING_SNAKE_CASE_				:  int           =     []

        for task_id, content in self._prepare_split_single(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):
              (
                  (
                  SCREAMING_SNAKE_CASE_
              )       ,						(
                  SCREAMING_SNAKE_CASE_
              )       ,						(
                  SCREAMING_SNAKE_CASE_
              )       ,						(
                  SCREAMING_SNAKE_CASE_
              )       ,						
              )				:  Dict           =     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(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     total_num_examples
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     total_num_bytes

        # should rename everything at the end
        logger.debug(f"Renaming {total_shards} shards."					)
        if total_shards > 1:
              SCREAMING_SNAKE_CASE_				:  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.
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self._fs

              # use the -SSSSS-of-NNNNN pattern
              def _rename_shard(
                  _SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					):
                    rename(
                        _SCREAMING_SNAKE_CASE ,					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}"					) ,					)

              SCREAMING_SNAKE_CASE_				:  str           =     []
              SCREAMING_SNAKE_CASE_				:  List[str]           =     0
              for i in range(len(_SCREAMING_SNAKE_CASE					)					):
                    SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     task_id_and_num_shards[i]
                    for shard_id in range(_SCREAMING_SNAKE_CASE					):
                          args.append([task_id, shard_id, global_shard_id]					)
                          global_shard_id += 1
              self._spark.sparkContext.parallelize(_SCREAMING_SNAKE_CASE ,					len(_SCREAMING_SNAKE_CASE					)					).map(lambda _SCREAMING_SNAKE_CASE					: _rename_shard(*_SCREAMING_SNAKE_CASE					)					).collect()
        else:
              # don't use any pattern
              SCREAMING_SNAKE_CASE_				:  int           =     0
              SCREAMING_SNAKE_CASE_				:  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(_SCREAMING_SNAKE_CASE ,					''					) ,					)



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        return SparkExamplesIterable(self.df					)









def    A_  (							a					,   a  ):

      """simple docstring"""



      return int((input_a, input_a).count(0  ) != 0  )

def    A_  (							):

      """simple docstring"""



      assert nand_gate(0					,   0  ) == 1
      assert nand_gate(0					,   1  ) == 1
      assert nand_gate(1					,   0  ) == 1
      assert nand_gate(1					,   1  ) == 0


if __name__ == "__main__":
    print(nand_gate(0, 0))
    print(nand_gate(0, 1))
    print(nand_gate(1, 0))
    print(nand_gate(1, 1))






