





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


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

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


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , ):
	import pyspark

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

	return generate_fn
class SCREAMING_SNAKE_CASE_						(					_BaseExamplesIterable					):






						def __init__(      self      :      Union[str, Any]		,   _A      :      "pyspark.sql.DataFrame"		,   _A      :      Optional[Any]=None		,   )							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Any											=	df
							snake_case_		:			Any											=	partition_order or range(self.df.rdd.getNumPartitions()	)
							snake_case_		:			int											=	_generate_iterable_examples(self.df		,   self.partition_order	)






						def __iter__(      self      :      List[Any]	)							->    int:







							"""simple docstring"""


							yield from self.generate_examples_fn()






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      np.random.Generator	)							->    "SparkExamplesIterable":







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	list(range(self.df.rdd.getNumPartitions()	)	)
							generator.shuffle(_A	)
							return SparkExamplesIterable(self.df		,   partition_order=_A	)






						def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      int		,   _A      :      int	)							->    "SparkExamplesIterable":







							"""simple docstring"""


							snake_case_		:			Tuple											=	self.split_shard_indices_by_worker(_A		,   _A	)
							return SparkExamplesIterable(self.df		,   partition_order=_A	)






						@property
						def UpperCAmelCase_				(      self      :      List[Any]	)							->    int:







							"""simple docstring"""


							return len(self.partition_order	)
class SCREAMING_SNAKE_CASE_						(					datasets.DatasetBuilder					):
						__magic_name__:			Dict       =       SparkConfig






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







							"""simple docstring"""


							import pyspark

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

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






						def UpperCAmelCase_				(      self      :      List[str]	)							->    Optional[Any]:







							"""simple docstring"""


							def create_cache_and_write_probe(_A      :      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=_A	)
								snake_case_		:			Any											=	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(_A		,   '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:
								snake_case_		:			Any											=	(
								    self._spark.sparkContext.parallelize(range(1	)		,   1	).mapPartitions(_A	).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      :      Union[str, Any]	)							->    List[str]:







							"""simple docstring"""


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






						def UpperCAmelCase_				(      self      :      List[Any]		,   _A      :      datasets.download.download_manager.DownloadManager	)							->    Tuple:







							"""simple docstring"""


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






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      Dict	)							->    Tuple:







							"""simple docstring"""


							import pyspark

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

							snake_case_		:			Union[str, Any]											=	self.df.count()
							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.
							snake_case_		:			int											=	(
							    self.df.limit(_A	)
							    .repartition(1	)
							    .mapInArrow(_A		,   'batch_bytes: long'	)
							    .agg(pyspark.sql.functions.sum('batch_bytes'	).alias('sample_bytes'	)	)
							    .collect()[0]
							    .sample_bytes
							    / sample_num_rows
							)
							snake_case_		:			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.
								snake_case_		:			int											=	min(_A		,   int(approx_total_size / max_shard_size	)	)
								snake_case_		:			str											=	self.df.repartition(_A	)






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      str		,   _A      :      str		,   _A      :      int		,   )							->    Iterable[Tuple[int, bool, Union[int, tuple]]]:







							"""simple docstring"""


							import pyspark

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

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

								if writer._num_bytes > 0:
									snake_case_						,snake_case_		:			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(_A	)	):
										snake_case_		:			str											=	os.path.join(os.path.dirname(_A	)		,   os.path.basename(_A	)	)
										shutil.move(_A		,   _A	)

							snake_case_		:			int											=	(
							    self.df.mapInArrow(_A		,   '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      :      Union[str, Any]		,   _A      :      "datasets.SplitGenerator"		,   _A      :      str = "arrow"		,   _A      :      Optional[Union[str, int]] = None		,   _A      :      Optional[int] = None		,   **_A      :      Optional[int]		,   )							->    Dict:







							"""simple docstring"""


							self._validate_cache_dir()

							snake_case_		:			List[Any]											=	convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE	)
							self._repartition_df_if_needed(_A	)
							snake_case_		:			Tuple											=	not is_remote_filesystem(self._fs	)
							snake_case_		:			str											=	os.path.join if is_local else posixpath.join

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

							snake_case_		:			Any											=	0
							snake_case_		:			List[str]											=	0
							snake_case_		:			Tuple											=	0
							snake_case_		:			Tuple											=	[]
							snake_case_		:			Tuple											=	[]

							for task_id, content in self._prepare_split_single(_A		,   _A		,   _A	):
								(
								    (
								    snake_case_
								)						,(
								    snake_case_
								)						,(
								    snake_case_
								)						,(
								    snake_case_
								)						,
								)		:			int											=	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(_A	)

							snake_case_		:			List[str]											=	total_num_examples
							snake_case_		:			Union[str, Any]											=	total_num_bytes

							# should rename everything at the end
							logger.debug(F"""Renaming {total_shards} shards."""	)
							if total_shards > 1:
								snake_case_		:			Dict											=	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.
								snake_case_		:			Union[str, Any]											=	self._fs

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

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






						def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      "datasets.SplitGenerator"		,   )							->    SparkExamplesIterable:







							"""simple docstring"""


							return SparkExamplesIterable(self.df	)








from statistics import mean

import numpy as np


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
	snake_case_		:			Optional[Any]											=	0
	# Number of processes finished
	snake_case_		:			List[str]											=	0
	# Displays the finished process.
	# If it is 0, the performance is completed if it is 1, before the performance.
	snake_case_		:			str											=	[0] * no_of_process
	# List to include calculation results
	snake_case_		:			Optional[int]											=	[0] * no_of_process

	# Sort by arrival time.
	snake_case_		:			str											=	[burst_time[i] for i in np.argsort(__a   )]
	snake_case_		:			str											=	[process_name[i] for i in np.argsort(__a   )]
	arrival_time.sort()

	while no_of_process > finished_process_count:
		snake_case_		:			int											=	0
		while finished_process[i] == 1:
			i += 1
		if current_time < arrival_time[i]:
			snake_case_		:			str											=	arrival_time[i]

		snake_case_		:			Optional[Any]											=	0
		# Index showing the location of the process being performed
		snake_case_		:			Tuple											=	0
		# Saves the current response ratio.
		snake_case_		:			List[Any]											=	0
		for i in range(0   , __a   ):
			if finished_process[i] == 0 and arrival_time[i] <= current_time:
				snake_case_		:			Optional[Any]											=	(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
				    i
				]
			if response_ratio < temp:
				snake_case_		:			Optional[int]											=	temp
				snake_case_		:			Optional[Any]											=	i

        # Calculate the turn around time
		snake_case_		:			Any											=	current_time + burst_time[loc] - arrival_time[loc]
		current_time += burst_time[loc]
		# Indicates that the process has been performed.
		snake_case_		:			Optional[Any]											=	1
		# Increase finished_process_count by 1
		finished_process_count += 1

	return turn_around_time


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
	snake_case_		:			List[Any]											=	[0] * no_of_process
	for i in range(0   , __a   ):
		snake_case_		:			Optional[int]											=	turn_around_time[i] - burst_time[i]
	return waiting_time


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		5
						_SCREAMING_SNAKE_CASE      	=		["""A""", """B""", """C""", """D""", """E"""]
						_SCREAMING_SNAKE_CASE      	=		[1, 2, 3, 4, 5]
						_SCREAMING_SNAKE_CASE      	=		[1, 2, 3, 4, 5]

						_SCREAMING_SNAKE_CASE      	=		calculate_turn_around_time(
						    process_name, arrival_time, burst_time, no_of_process
						)
						_SCREAMING_SNAKE_CASE      	=		calculate_waiting_time(
						    process_name, turn_around_time, burst_time, no_of_process
						)

						print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
						for i in range(0, no_of_process):
												print(
												    F'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
												    F'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
												)

						print(F'''average waiting time : {mean(waiting_time):.5f}''')
						print(F'''average turn around time : {mean(turn_around_time):.5f}''')

