




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__ :   Optional[int]					=							datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
   import pyspark





@dataclass
class        lowerCamelCase_							(      datasets.BuilderConfig					):


 '''simple docstring'''







 lowercase_				    =    None


def 					UpperCAmelCase_							(      __UpperCAmelCase						:	"pyspark.sql.DataFrame"			,			__UpperCAmelCase						:	List[int]			,			)					-> int:
      import pyspark

      def generate_fn():
            SCREAMING_SNAKE_CASE_							=			df.select('*'			,			pyspark.sql.functions.spark_partition_id().alias('part_id'							)							)
            for partition_id in partition_order:
                  SCREAMING_SNAKE_CASE_							=			df_with_partition_id.select('*'							).where(f"part_id = {partition_id}"							).drop('part_id'							)
                  SCREAMING_SNAKE_CASE_							=			partition_df.collect()
                  SCREAMING_SNAKE_CASE_							=			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  :		List[str]				,				_lowerCAmelCase  :		"pyspark.sql.DataFrame"				,				_lowerCAmelCase  :		str=None				,				):
       SCREAMING_SNAKE_CASE_							=			df
       SCREAMING_SNAKE_CASE_							=			partition_order or range(self.df.rdd.getNumPartitions()						)
       SCREAMING_SNAKE_CASE_							=			_generate_iterable_examples(self.df				,				self.partition_order						)



 def __iter__(							self  :		Union[str, Any]						):
       yield from self.generate_examples_fn()



 def       lowerCAmelCase_  (							self  :		str				,				_lowerCAmelCase  :		np.random.Generator						):
       SCREAMING_SNAKE_CASE_							=			list(range(self.df.rdd.getNumPartitions()						)						)
       generator.shuffle(_lowerCAmelCase						)
       return SparkExamplesIterable(self.df				,				partition_order=_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		int						):
       SCREAMING_SNAKE_CASE_							=			self.split_shard_indices_by_worker(_lowerCAmelCase				,				_lowerCAmelCase						)
       return SparkExamplesIterable(self.df				,				partition_order=_lowerCAmelCase						)




 @property
 def       lowerCAmelCase_  (							self  :		Dict						):
       return len(self.partition_order						)





class        lowerCamelCase_							(      datasets.DatasetBuilder					):


 '''simple docstring'''







 lowercase_				    =    SparkConfig



 def __init__(							self  :		str				,				_lowerCAmelCase  :		"pyspark.sql.DataFrame"				,				_lowerCAmelCase  :		str = None				,				_lowerCAmelCase  :		str = None				,				**_lowerCAmelCase  :		Optional[Any]				,				):
       import pyspark

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

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



 def       lowerCAmelCase_  (							self  :		Any						):
       # Returns the path of the created file.
       def create_cache_and_write_probe(_lowerCAmelCase  :		Optional[Any]						):
             # 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						)
             SCREAMING_SNAKE_CASE_							=			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:
             SCREAMING_SNAKE_CASE_							=			(
                 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       lowerCAmelCase_  (							self  :		Any						):
       return datasets.DatasetInfo(features=self.config.features						)



 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		datasets.download.download_manager.DownloadManager						):
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN						)]



 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		Optional[int]						):
       import pyspark

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

       SCREAMING_SNAKE_CASE_							=			self.df.count()
       SCREAMING_SNAKE_CASE_							=			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_							=			(
           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
       )
       SCREAMING_SNAKE_CASE_							=			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_							=			min(_lowerCAmelCase				,				int(approx_total_size / max_shard_size						)						)
             SCREAMING_SNAKE_CASE_							=			self.df.repartition(_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Any				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		int				,				):
       import pyspark

       SCREAMING_SNAKE_CASE_							=			ParquetWriter if file_format == 'parquet' else ArrowWriter
       SCREAMING_SNAKE_CASE_							=			os.path.join(self._working_dir				,				os.path.basename(_lowerCAmelCase						)						) if self._working_dir else fpath
       SCREAMING_SNAKE_CASE_							=			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_							=			self.config.features
       SCREAMING_SNAKE_CASE_							=			self._writer_batch_size
       SCREAMING_SNAKE_CASE_							=			self._fs.storage_options

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

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

       SCREAMING_SNAKE_CASE_							=			(
           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       lowerCAmelCase_  (							self  :		Any				,				_lowerCAmelCase  :		"datasets.SplitGenerator"				,				_lowerCAmelCase  :		str = "arrow"				,				_lowerCAmelCase  :		Optional[Union[str, int]] = None				,				_lowerCAmelCase  :		Optional[int] = None				,				**_lowerCAmelCase  :		List[Any]				,				):
       self._validate_cache_dir()

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

       SCREAMING_SNAKE_CASE_							=			'-TTTTT-SSSSS-of-NNNNN'
       SCREAMING_SNAKE_CASE_							=			F"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
       SCREAMING_SNAKE_CASE_							=			path_join(self._output_dir				,				_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			0
       SCREAMING_SNAKE_CASE_							=			0
       SCREAMING_SNAKE_CASE_							=			0
       SCREAMING_SNAKE_CASE_							=			[]
       SCREAMING_SNAKE_CASE_							=			[]

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

       SCREAMING_SNAKE_CASE_							=			total_num_examples
       SCREAMING_SNAKE_CASE_							=			total_num_bytes

       # should rename everything at the end
       logger.debug(F"Renaming {total_shards} shards."						)
       if total_shards > 1:
             SCREAMING_SNAKE_CASE_							=			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_							=			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}"						)				,				)

             SCREAMING_SNAKE_CASE_							=			[]
             SCREAMING_SNAKE_CASE_							=			0
             for i in range(len(_lowerCAmelCase						)						):
                   SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			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
             SCREAMING_SNAKE_CASE_							=			0
             SCREAMING_SNAKE_CASE_							=			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       lowerCAmelCase_  (							self  :		Optional[int]				,				_lowerCAmelCase  :		"datasets.SplitGenerator"				,				):
       return SparkExamplesIterable(self.df						)





from __future__ import annotations

lowerCamelCase__ :   Optional[int]					=							[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCamelCase__ :   List[Any]					=							[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			[]
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)

      for i in range(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_							=			-1
            for j in range(i + 1			,			__UpperCAmelCase							):
                  if arr[i] < arr[j]:
                        SCREAMING_SNAKE_CASE_							=			arr[j]
                        break
            result.append(__UpperCAmelCase							)
      return result

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			[]
      for i, outer in enumerate(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_							=			-1
            for inner in arr[i + 1 :]:
                  if outer < inner:
                        SCREAMING_SNAKE_CASE_							=			inner
                        break
            result.append(__UpperCAmelCase							)
      return result

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			[]
      SCREAMING_SNAKE_CASE_							=			[-1] * arr_size

      for index in reversed(range(__UpperCAmelCase							)							):
            if stack:
                  while stack[-1] <= arr[index]:
                        stack.pop()
                        if not stack:
                              break
            if stack:
                  SCREAMING_SNAKE_CASE_							=			stack[-1]
            stack.append(arr[index]							)
      return result


if __name__ == "__main__":
   from doctest import testmod
   from timeit import timeit

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   lowerCamelCase__ :   List[str]					=							(
       'from __main__ import arr, next_greatest_element_slow, '
       'next_greatest_element_fast, next_greatest_element'
   )
   print(
       'next_greatest_element_slow():',
       timeit('next_greatest_element_slow(arr)', setup=setup),
   )
   print(
       'next_greatest_element_fast():',
       timeit('next_greatest_element_fast(arr)', setup=setup),
   )
   print(
       '     next_greatest_element():',
       timeit('next_greatest_element(arr)', setup=setup),
   )