

"""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


UpperCAmelCase													=datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
  import pyspark





@dataclass
class lowerCamelCase__   (				datasets.BuilderConfig    ):



    '''simple docstring'''




    _lowerCamelCase					=   None




def   _A					(	_a	:						"pyspark.sql.DataFrame"     ,   _a	:						List[int]     ,   ):



      """simple docstring"""






      import pyspark

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

      return generate_fn





class lowerCamelCase__   (				_BaseExamplesIterable    ):



    '''simple docstring'''




    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_=None ,)    ->      List[str]:
          A				  =		df
          A				  =		partition_order or range(self.df.rdd.getNumPartitions()    )
          A				  =		_generate_iterable_examples(self.df ,self.partition_order    )




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




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      "SparkExamplesIterable":
          A				  =		list(range(self.df.rdd.getNumPartitions()    )    )
          generator.shuffle(lowerCamelCase_    )
          return SparkExamplesIterable(self.df ,partition_order=lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_    )    ->      "SparkExamplesIterable":
          A				  =		self.split_shard_indices_by_worker(lowerCamelCase_ ,lowerCamelCase_    )
          return SparkExamplesIterable(self.df ,partition_order=lowerCamelCase_    )




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





class lowerCamelCase__   (				datasets.DatasetBuilder    ):



    '''simple docstring'''




    _lowerCamelCase					=   SparkConfig




    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,**lowerCamelCase_ ,)    ->      List[Any]:
          import pyspark

          A				  =		pyspark.sql.SparkSession.builder.getOrCreate()
          A				  =		df
          A				  =		working_dir

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




    def UpperCamelCase__		( self    )    ->      str:
          # Returns the path of the created file.
          def create_cache_and_write_probe(lowerCamelCase_    ):
                # 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_    )
                A				  =		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:
                A				  =		(
                    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 UpperCamelCase__		( self    )    ->      int:
          return datasets.DatasetInfo(features=self.config.features    )




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      List[Any]:
          return [datasets.SplitGenerator(name=datasets.Split.TRAIN    )]




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      Union[str, Any]:
          import pyspark

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

          A				  =		self.df.count()
          A				  =		df_num_rows if df_num_rows <= 1_0_0 else 1_0_0
          # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
          A				  =		(
              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
          )
          A				  =		approx_bytes_per_row * df_num_rows
          if approx_total_size > max_shard_size:
                # Make sure there is at least one row per partition.
                A				  =		min(lowerCamelCase_ ,int(approx_total_size / max_shard_size    )    )
                A				  =		self.df.repartition(lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,)    ->      Iterable[Tuple[int, bool, Union[int, tuple]]]:
          import pyspark

          A				  =		ParquetWriter if file_format == """parquet""" else ArrowWriter
          A				  =		os.path.join(self._working_dir ,os.path.basename(lowerCamelCase_    )    ) if self._working_dir else fpath
          A				  =		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.
          A				  =		self.config.features
          A				  =		self._writer_batch_size
          A				  =		self._fs.storage_options

          def write_arrow(lowerCamelCase_    ):
                # Within the same SparkContext, no two task attempts will share the same attempt ID.
                A				  =		pyspark.TaskContext().taskAttemptId()
                A				  =		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"""] ,)
                A				  =		0
                A				  =		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_ ,)
                A				  =		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:
                            A  ,			A				  =		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
                            A				  =		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_ ,)
                      A				  =		pa.Table.from_batches([batch]    )
                      writer.write_table(lowerCamelCase_    )

                if writer._num_bytes > 0:
                      A  ,			A				  =		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_    )    ):
                            A				  =		os.path.join(os.path.dirname(lowerCamelCase_    ) ,os.path.basename(lowerCamelCase_    )    )
                            shutil.move(lowerCamelCase_ ,lowerCamelCase_    )

          A				  =		(
              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 UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ = "arrow" ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,**lowerCamelCase_ ,)    ->      int:
          self._validate_cache_dir()

          A				  =		convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE    )
          self._repartition_df_if_needed(lowerCamelCase_    )
          A				  =		not is_remote_filesystem(self._fs    )
          A				  =		os.path.join if is_local else posixpath.join

          A				  =		"""-TTTTT-SSSSS-of-NNNNN"""
          A				  =		f'{self.name}-{split_generator.name}{SUFFIX}.{file_format}'
          A				  =		path_join(self._output_dir ,lowerCamelCase_    )

          A				  =		0
          A				  =		0
          A				  =		0
          A				  =		[]
          A				  =		[]

          for task_id, content in self._prepare_split_single(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    ):
                (
                    (
                    A
                )  ,			(
                    A
                )  ,			(
                    A
                )  ,			(
                    A
                )  ,			
                )				  =		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_    )

          A				  =		total_num_examples
          A				  =		total_num_bytes

          # should rename everything at the end
          logger.debug(f'Renaming {total_shards} shards.'    )
          if total_shards > 1:
                A				  =		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.
                A				  =		self._fs

                # use the -SSSSS-of-NNNNN pattern
                def _rename_shard(
                    lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,):
                      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}'    ) ,)

                A				  =		[]
                A				  =		0
                for i in range(len(lowerCamelCase_    )    ):
                      A  ,			A				  =		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
                A				  =		0
                A				  =		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 UpperCamelCase__		( self ,lowerCamelCase_ ,)    ->      SparkExamplesIterable:
          return SparkExamplesIterable(self.df    )







"""simple docstring"""


class lowerCamelCase__   :



    '''simple docstring'''




    def __init__( self    )    ->      Union[str, Any]:
          A				  =		{}




    def UpperCamelCase__		( self    )    ->      None:
          print(self.vertex    )
          for i in self.vertex:
                print(lowerCamelCase_ ,""" -> """ ,""" -> """.join([str(lowerCamelCase_    ) for j in self.vertex[i]]    )    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_    )    ->      None:
          # check if vertex is already present,
          if from_vertex in self.vertex:
                self.vertex[from_vertex].append(lowerCamelCase_    )
          else:
                # else make a new vertex
                A				  =		[to_vertex]




    def UpperCamelCase__		( self    )    ->      None:
          # visited array for storing already visited nodes
          A				  =		[False] * len(self.vertex    )

          # call the recursive helper function
          for i in range(len(self.vertex    )    ):
                if not visited[i]:
                      self.dfs_recursive(lowerCamelCase_ ,lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_    )    ->      None:
          # mark start vertex as visited
          A				  =		True

          print(lowerCamelCase_ ,end=""" """    )

          # Recur for all the vertices that are adjacent to this node
          for i in self.vertex:
                if not visited[i]:
                      self.dfs_recursive(lowerCamelCase_ ,lowerCamelCase_    )


if __name__ == "__main__":
  UpperCAmelCase													=Graph()
  g.add_edge(0, 1)
  g.add_edge(0, 2)
  g.add_edge(1, 2)
  g.add_edge(2, 0)
  g.add_edge(2, 3)
  g.add_edge(3, 3)

  g.print_graph()
  print("DFS:")
  g.dfs()

  # OUTPUT:
  # 0  ->  1 -> 2
  # 1  ->  2
  # 2  ->  0 -> 3
  # 3  ->  3
  # DFS:
  #  0 1 2 3




