import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch







def  __UpperCamelCase    (						A							):  # picklable for multiprocessing
     return x.sum()







def  __UpperCamelCase    (						A							):  # picklable for multiprocessing
     return i + 1



@dataclass
class     _A   :
  SCREAMING_SNAKE_CASE_	:	int
  SCREAMING_SNAKE_CASE_	:	str



class     _A   (    __UpperCamelCase							):






  def 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      {}
       UpperCamelCase__										=      []
       UpperCamelCase__										=      1
       UpperCamelCase__										=      [1, 2]
       UpperCamelCase__										=      {'''a''': 1, '''b''': 2}
       UpperCamelCase__										=      {'''a''': [1, 2], '''b''': [3, 4]}
       UpperCamelCase__										=      {'''a''': {'''1''': 1}, '''b''': 2}
       UpperCamelCase__										=      {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
       UpperCamelCase__										=      {}
       UpperCamelCase__										=      []
       UpperCamelCase__										=      2
       UpperCamelCase__										=      [2, 3]
       UpperCamelCase__										=      {'''a''': 2, '''b''': 3}
       UpperCamelCase__										=      {'''a''': [2, 3], '''b''': [4, 5]}
       UpperCamelCase__										=      {'''a''': {'''1''': 2}, '''b''': 3}
       UpperCamelCase__										=      {'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}
       self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)
       self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)
       self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)
       self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)
       self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)
       self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)
       self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)
       self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      2
       self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				num_proc=SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)
       self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				num_proc=SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)
       self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				num_proc=SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)
       self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				num_proc=SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)
       self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				num_proc=SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)
       self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				num_proc=SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)
       self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				num_proc=SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)
       self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				num_proc=SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      {'''a''': np.eye(2	), '''b''': np.zeros(3	), '''c''': np.ones(2	)}
       UpperCamelCase__										=      {'''a''': 2, '''b''': 0, '''c''': 2}
       UpperCamelCase__										=      {
           '''a''': np.eye(2	).astype(SCREAMING_SNAKE_CASE_	),
           '''b''': np.zeros(3	).astype(SCREAMING_SNAKE_CASE_	),
           '''c''': np.ones(2	).astype(SCREAMING_SNAKE_CASE_	),
       }
       self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				map_numpy=SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)
       self.assertEqual(
           {k: v.tolist() for k, v in map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				map_numpy=SCREAMING_SNAKE_CASE_	).items()}   ,				{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}   ,				)
       self.assertEqual(map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				map_numpy=SCREAMING_SNAKE_CASE_   ,				num_proc=SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)
       self.assertEqual(
           {k: v.tolist() for k, v in map_nested(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				map_numpy=SCREAMING_SNAKE_CASE_   ,				num_proc=SCREAMING_SNAKE_CASE_	).items()}   ,				{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}   ,				)
       with self.assertRaises(SCREAMING_SNAKE_CASE_	):  # can't pickle a local lambda
            map_nested(lambda SCREAMING_SNAKE_CASE_	: x + 1   ,				SCREAMING_SNAKE_CASE_   ,				num_proc=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      {'''a''': 1, '''b''': 2}
       UpperCamelCase__										=      {'''a''': 3, '''b''': 4}
       UpperCamelCase__										=      {'''a''': 5, '''b''': 6}
       UpperCamelCase__										=      sorted([('''a''', (1, 3, 5)), ('''b''', (2, 4, 6))]	)
       self.assertEqual(sorted(zip_dict(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)	)   ,				SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       class     _A   :
         SCREAMING_SNAKE_CASE_	:	Tuple						="bar"



       UpperCamelCase__										=      Foo()
       self.assertEqual(foo.my_attr   ,				'''bar'''	)
       with temporary_assignment(SCREAMING_SNAKE_CASE_   ,				'''my_attr'''   ,				'''BAR'''	):
            self.assertEqual(foo.my_attr   ,				'''BAR'''	)
       self.assertEqual(foo.my_attr   ,				'''bar'''	)







@pytest.mark.parametrize(
    '''iterable_length, num_proc, expected_num_proc''' ,							[
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ] ,							)
def  __UpperCamelCase    (						A ,							A ,							A							):
     with patch('''datasets.utils.py_utils._single_map_nested'''							) as mock_single_map_nested, patch(
         '''datasets.parallel.parallel.Pool'''							) as mock_multiprocessing_pool:
          UpperCamelCase__										=      {f"{i}": i for i in range(A							)}
          UpperCamelCase__										=      map_nested(lambda A							: x + 10 ,							A ,							num_proc=A ,							parallel_min_length=16							)
          if expected_num_proc == 1:
               assert mock_single_map_nested.called
               assert not mock_multiprocessing_pool.called
          else:
               assert not mock_single_map_nested.called
               assert mock_multiprocessing_pool.called
               assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc



class     _A   (    __UpperCamelCase							):






  @require_tf
  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       import tensorflow as tf
       from tensorflow.keras import layers

       UpperCamelCase__										=      layers.Dense(2	)

       def gen_random_output():
            UpperCamelCase__										=      tf.random.uniform((1, 3)	)
            return model(SCREAMING_SNAKE_CASE_	).numpy()

       with temp_seed(42   ,				set_tensorflow=SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      gen_random_output()
       with temp_seed(42   ,				set_tensorflow=SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      gen_random_output()
       UpperCamelCase__										=      gen_random_output()

       np.testing.assert_equal(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
       self.assertGreater(np.abs(outa - outa	).sum()   ,				0	)






  @require_torch
  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       import torch

       def gen_random_output():
            UpperCamelCase__										=      torch.nn.Linear(3   ,				2	)
            UpperCamelCase__										=      torch.rand(1   ,				3	)
            return model(SCREAMING_SNAKE_CASE_	).detach().numpy()

       with temp_seed(42   ,				set_pytorch=SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      gen_random_output()
       with temp_seed(42   ,				set_pytorch=SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      gen_random_output()
       UpperCamelCase__										=      gen_random_output()

       np.testing.assert_equal(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
       self.assertGreater(np.abs(outa - outa	).sum()   ,				0	)






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       def gen_random_output():
            return np.random.rand(1   ,				3	)

       with temp_seed(42	):
            UpperCamelCase__										=      gen_random_output()
       with temp_seed(42	):
            UpperCamelCase__										=      gen_random_output()
       UpperCamelCase__										=      gen_random_output()

       np.testing.assert_equal(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
       self.assertGreater(np.abs(outa - outa	).sum()   ,				0	)







@pytest.mark.parametrize('''input_data''' ,							[{}]							)
def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      NestedDataStructure(A							).data
     assert output_data == input_data







@pytest.mark.parametrize(
    '''data, expected_output''' ,							[
        ({}, []),
        ([], []),
        ('''foo''', ['''foo''']),
        (['''foo''', '''bar'''], ['''foo''', '''bar''']),
        ([['''foo''', '''bar''']], ['''foo''', '''bar''']),
        ([[['''foo'''], ['''bar''']]], ['''foo''', '''bar''']),
        ([[['''foo'''], '''bar''']], ['''foo''', '''bar''']),
        ({'''a''': 1, '''b''': 2}, [1, 2]),
        ({'''a''': [1, 2], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[1, 2]], '''b''': [[3, 4]]}, [1, 2, 3, 4]),
        ({'''a''': [[1, 2]], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [[[3], [4]]]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [[3, 4]]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [3, [4]]}, [1, 2, 3, 4]),
        ({'''a''': {'''1''': 1}, '''b''': 2}, [1, 2]),
        ({'''a''': {'''1''': [1]}, '''b''': 2}, [1, 2]),
        ({'''a''': {'''1''': [1]}, '''b''': [2]}, [1, 2]),
    ] ,							)
def  __UpperCamelCase    (						A ,							A							):
     UpperCamelCase__										=      NestedDataStructure(A							).flatten()
     assert output == expected_output







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      A(x=1 ,							y='''foobar'''							)
     UpperCamelCase__										=      {'''x''': 1, '''y''': '''foobar'''}
     assert asdict(A							) == expected_output

     UpperCamelCase__										=      {'''a''': {'''b''': A(x=10 ,							y='''foo'''							)}, '''c''': [A(x=20 ,							y='''bar'''							)]}
     UpperCamelCase__										=      {'''a''': {'''b''': {'''x''': 10, '''y''': '''foo'''}}, '''c''': [{'''x''': 20, '''y''': '''bar'''}]}
     assert asdict(A							) == expected_output

     with pytest.raises(A							):
          asdict([1, A(x=10 ,							y='''foo'''							)]							)







def  __UpperCamelCase    (						A							):
     return text.split()







def  __UpperCamelCase    (						A							):
     yield (time.time(), content)
     time.sleep(2							)
     yield (time.time(), content)







def  __UpperCamelCase    (						):
     with Pool(2							) as pool:
          UpperCamelCase__										=      list(iflatmap_unordered(A ,							_split_text ,							kwargs_iterable=[{'''text''': '''hello there'''}] * 10							)							)
          assert out.count('''hello'''							) == 10
          assert out.count('''there'''							) == 10
          assert len(A							) == 20

     # check multiprocess from pathos (uses dill for pickling)
     with multiprocess.Pool(2							) as pool:
          UpperCamelCase__										=      list(iflatmap_unordered(A ,							_split_text ,							kwargs_iterable=[{'''text''': '''hello there'''}] * 10							)							)
          assert out.count('''hello'''							) == 10
          assert out.count('''there'''							) == 10
          assert len(A							) == 20

     # check that we get items as fast as possible
     with Pool(2							) as pool:
          UpperCamelCase__										=      []
          for yield_time, content in iflatmap_unordered(
              A ,							_aseconds_generator_of_aitems_with_timing ,							kwargs_iterable=[{'''content''': '''a'''}, {'''content''': '''b'''}]							):
               assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
               out.append(A							)
          assert out.count('''a'''							) == 2
          assert out.count('''b'''							) == 2
          assert len(A							) == 4




from __future__ import annotations

import pandas as pd







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      [0] * no_of_processes
     UpperCamelCase__										=      [0] * no_of_processes
     # Copy the burst time into remaining_time[]
     for i in range(A							):
          UpperCamelCase__										=      burst_time[i]

     UpperCamelCase__										=      0
     UpperCamelCase__										=      0
     UpperCamelCase__										=      999999999
     UpperCamelCase__										=      0
     UpperCamelCase__										=      False

     # Process until all processes are completed
     while complete != no_of_processes:
          for j in range(A							):
               if arrival_time[j] <= increment_time and remaining_time[j] > 0:
                    if remaining_time[j] < minm:
                         UpperCamelCase__										=      remaining_time[j]
                         UpperCamelCase__										=      j
                         UpperCamelCase__										=      True

          if not check:
               increment_time += 1
               continue
          remaining_time[short] -= 1

          UpperCamelCase__										=      remaining_time[short]
          if minm == 0:
               UpperCamelCase__										=      999999999

          if remaining_time[short] == 0:
               complete += 1
               UpperCamelCase__										=      False

               # Find finish time of current process
               UpperCamelCase__										=      increment_time + 1

               # Calculate waiting time
               UpperCamelCase__										=      finish_time - arrival_time[short]
               UpperCamelCase__										=      finar - burst_time[short]

               if waiting_time[short] < 0:
                    UpperCamelCase__										=      0

        # Increment time
          increment_time += 1
     return waiting_time







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      [0] * no_of_processes
     for i in range(A							):
          UpperCamelCase__										=      burst_time[i] + waiting_time[i]
     return turn_around_time







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      0
     UpperCamelCase__										=      0
     for i in range(A							):
          UpperCamelCase__										=      total_waiting_time + waiting_time[i]
          UpperCamelCase__										=      total_turn_around_time + turn_around_time[i]
     print(f"Average waiting time = {total_waiting_time / no_of_processes:.5f}"							)
     print('''Average turn around time =''' ,							total_turn_around_time / no_of_processes							)


if __name__ == "__main__":
     print('''Enter how many process you want to analyze''')
     __magic_name__								=int(input())
     __magic_name__								=[0] * no_of_processes
     __magic_name__								=[0] * no_of_processes
     __magic_name__								=list(range(1, no_of_processes + 1))

     for i in range(no_of_processes):
          print('''Enter the arrival time and burst time for process:--''' + str(i + 1))
          __magic_name__		,  __magic_name__								=map(int, input().split())

     __magic_name__								=calculate_waitingtime(arrival_time, burst_time, no_of_processes)

     __magic_name__								=burst_time
     __magic_name__								=no_of_processes
     __magic_name__								=waiting_time
     __magic_name__								=calculate_turnaroundtime(bt, n, wt)

     calculate_average_times(waiting_time, turn_around_time, no_of_processes)

     __magic_name__								=pd.DataFrame(
         list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
         columns=[
             '''Process''',
             '''BurstTime''',
             '''ArrivalTime''',
             '''WaitingTime''',
             '''TurnAroundTime''',
         ],
     )

     # Printing the dataFrame
     pd.set_option('''display.max_rows''', fcfs.shape[0] + 1)
     print(fcfs)



