






'''simple docstring'''

from statistics import mean

import numpy as np
def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     list:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				0
     # Number of processes finished
     __lowerCamelCase						:       List[Any]				   =				0
     # Displays the finished process.
     # If it is 0, the performance is completed if it is 1, before the performance.
     __lowerCamelCase						:       int				   =				[0] * no_of_process
     # List to include calculation results
     __lowerCamelCase						:       Optional[int]				   =				[0] * no_of_process

     # Sort by arrival time.
     __lowerCamelCase						:       int				   =				[burst_time[i] for i in np.argsort(_lowercase     )]
     __lowerCamelCase						:       Union[str, Any]				   =				[process_name[i] for i in np.argsort(_lowercase     )]
     arrival_time.sort()

     while no_of_process > finished_process_count:
          __lowerCamelCase						:       Tuple				   =				0
          while finished_process[i] == 1:
               i += 1
          if current_time < arrival_time[i]:
               __lowerCamelCase						:       List[str]				   =				arrival_time[i]

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

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

     return turn_around_time
def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     list:


     """simple docstring"""
     __lowerCamelCase						:       List[Any]				   =				[0] * no_of_process
     for i in range(0,						_lowercase     ):
          __lowerCamelCase						:       Tuple				   =				turn_around_time[i] - burst_time[i]
     return waiting_time


if __name__ == "__main__":
   UpperCAmelCase__						:List[Any]          =			5
   UpperCAmelCase__						:Tuple          =			["""A""", """B""", """C""", """D""", """E"""]
   UpperCAmelCase__						:Tuple          =			[1, 2, 3, 4, 5]
   UpperCAmelCase__						:List[Any]          =			[1, 2, 3, 4, 5]

   UpperCAmelCase__						:Union[str, Any]          =			calculate_turn_around_time(
       process_name, arrival_time, burst_time, no_of_process
   )
   UpperCAmelCase__						:Optional[int]          =			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}''')











'''simple docstring'''

from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)
def     __lowercase  (_lowercase,						_lowercase     )   ->     List[str]:


     """simple docstring"""
     __lowerCamelCase						:       Union[str, Any]				   =				[]
     for part_id in partition_order:
          __lowerCamelCase						:       Union[str, Any]				   =				df.where(f"SPARK_PARTITION_ID() = {part_id}"     ).collect()
          for row_idx, row in enumerate(_lowercase     ):
               expected_row_ids_and_row_dicts.append((f"{part_id}_{row_idx}", row.asDict())     )
     return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def     __lowercase  ()   ->     Optional[int]:


     """simple docstring"""
     __lowerCamelCase						:       int				   =				pyspark.sql.SparkSession.builder.master("""local[*]"""     ).appName("""pyspark"""     ).getOrCreate()
     __lowerCamelCase						:       List[str]				   =				spark.range(100     ).repartition(1     )
     __lowerCamelCase						:       str				   =				Spark(_lowercase     )
     # The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
     # that each partition can hold 2 rows.
     spark_builder._repartition_df_if_needed(max_shard_size=16     )
     # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
     assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def     __lowercase  ()   ->     Optional[Any]:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				pyspark.sql.SparkSession.builder.master("""local[*]"""     ).appName("""pyspark"""     ).getOrCreate()
     __lowerCamelCase						:       Dict				   =				spark.range(10     ).repartition(2     )
     __lowerCamelCase						:       List[Any]				   =				[1, 0]
     __lowerCamelCase						:       Any				   =				_generate_iterable_examples(_lowercase,						_lowercase     )  # Reverse the partitions.
     __lowerCamelCase						:       int				   =				_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase,						_lowercase     )

     for i, (row_id, row_dict) in enumerate(generate_fn()     ):
          __lowerCamelCase   ,      __lowerCamelCase						:       str				   =				expected_row_ids_and_row_dicts[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def     __lowercase  ()   ->     int:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				pyspark.sql.SparkSession.builder.master("""local[*]"""     ).appName("""pyspark"""     ).getOrCreate()
     __lowerCamelCase						:       Union[str, Any]				   =				spark.range(10     ).repartition(1     )
     __lowerCamelCase						:       Dict				   =				SparkExamplesIterable(_lowercase     )
     assert it.n_shards == 1
     for i, (row_id, row_dict) in enumerate(_lowercase     ):
          assert row_id == f"0_{i}"
          assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def     __lowercase  ()   ->     Tuple:


     """simple docstring"""
     __lowerCamelCase						:       int				   =				pyspark.sql.SparkSession.builder.master("""local[*]"""     ).appName("""pyspark"""     ).getOrCreate()
     __lowerCamelCase						:       Optional[Any]				   =				spark.range(30     ).repartition(3     )
     # Mock the generator so that shuffle reverses the partition indices.
     with patch("""numpy.random.Generator"""     ) as generator_mock:
          __lowerCamelCase						:       Dict				   =				lambda _lowercase     : x.reverse()
          __lowerCamelCase						:       Union[str, Any]				   =				_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase,						[2, 1, 0]     )

          __lowerCamelCase						:       Tuple				   =				SparkExamplesIterable(_lowercase     ).shuffle_data_sources(_lowercase     )
          assert shuffled_it.n_shards == 3
          for i, (row_id, row_dict) in enumerate(_lowercase     ):
               __lowerCamelCase   ,      __lowerCamelCase						:       Union[str, Any]				   =				expected_row_ids_and_row_dicts[i]
               assert row_id == expected_row_id
               assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def     __lowercase  ()   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       Union[str, Any]				   =				pyspark.sql.SparkSession.builder.master("""local[*]"""     ).appName("""pyspark"""     ).getOrCreate()
     __lowerCamelCase						:       List[str]				   =				spark.range(20     ).repartition(4     )

     # Partitions 0 and 2
     __lowerCamelCase						:       Union[str, Any]				   =				SparkExamplesIterable(_lowercase     ).shard_data_sources(worker_id=0,						num_workers=2     )
     assert shard_it_a.n_shards == 2
     __lowerCamelCase						:       Optional[int]				   =				_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase,						[0, 2]     )
     for i, (row_id, row_dict) in enumerate(_lowercase     ):
          __lowerCamelCase   ,      __lowerCamelCase						:       int				   =				expected_row_ids_and_row_dicts_a[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict

     # Partitions 1 and 3
     __lowerCamelCase						:       List[str]				   =				SparkExamplesIterable(_lowercase     ).shard_data_sources(worker_id=1,						num_workers=2     )
     assert shard_it_a.n_shards == 2
     __lowerCamelCase						:       int				   =				_get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase,						[1, 3]     )
     for i, (row_id, row_dict) in enumerate(_lowercase     ):
          __lowerCamelCase   ,      __lowerCamelCase						:       int				   =				expected_row_ids_and_row_dicts_a[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def     __lowercase  ()   ->     int:


     """simple docstring"""
     __lowerCamelCase						:       Union[str, Any]				   =				pyspark.sql.SparkSession.builder.master("""local[*]"""     ).appName("""pyspark"""     ).getOrCreate()
     __lowerCamelCase						:       Dict				   =				spark.range(100     ).repartition(1     )
     __lowerCamelCase						:       Any				   =				Spark(_lowercase     )
     # Choose a small max_shard_size for maximum partitioning.
     spark_builder._repartition_df_if_needed(max_shard_size=1     )
     # The new number of partitions should not be greater than the number of rows.
     assert spark_builder.df.rdd.getNumPartitions() == 100



