

'''simple docstring'''






from __future__ import annotations

import pandas as pd


def 						_A	(       A__	,							A__	,							A__ ):






 """simple docstring"""



 __lowercase				    =					[0] * no_of_processes
 __lowercase				    =					[0] * no_of_processes
 # Copy the burst time into remaining_time[]
 for i in range(A__ ):
  __lowercase				    =					burst_time[i]

 __lowercase				    =					0
 __lowercase				    =					0
 __lowercase				    =					999999999
 __lowercase				    =					0
 __lowercase				    =					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:
     __lowercase				    =					remaining_time[j]
     __lowercase				    =					j
     __lowercase				    =					True

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

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

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

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

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

   if waiting_time[short] < 0:
    __lowercase				    =					0

        # Increment time
  increment_time += 1
 return waiting_time


def 						_A	(       A__	,							A__	,							A__ ):






 """simple docstring"""



 __lowercase				    =					[0] * no_of_processes
 for i in range(A__ ):
  __lowercase				    =					burst_time[i] + waiting_time[i]
 return turn_around_time


def 						_A	(       A__	,							A__	,							A__ ):






 """simple docstring"""



 __lowercase				    =					0
 __lowercase				    =					0
 for i in range(A__ ):
  __lowercase				    =					total_waiting_time + waiting_time[i]
  __lowercase				    =					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''')
 lowerCAmelCase__   		=      int(input())
 lowerCAmelCase__   		=      [0] * no_of_processes
 lowerCAmelCase__   		=      [0] * no_of_processes
 lowerCAmelCase__   		=      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))
  lowerCAmelCase__		,     lowerCAmelCase__   		=      map(int, input().split())

 lowerCAmelCase__   		=      calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

 calculate_average_times(waiting_time, turn_around_time, no_of_processes)

 lowerCAmelCase__   		=      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)






import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
				import torch

				from transformers import AutoModelForCausalLM



@require_torch
class        lowerCAmelCase__					(  unittest.TestCase       ):


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[Any]:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase       )
											A      						=    tokenizer.decode(greedy_ids[0]       )

											with CaptureStdout() as cs:
																		A      						=    TextStreamer(__UpperCamelCase       )
																		model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase						, streamer=__UpperCamelCase       )
											# The greedy text should be printed to stdout, except for the final "\n" in the streamer
											A      						=    cs.out[:-1]

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int       )		->     List[Any]:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase       )
											A      						=    tokenizer.decode(greedy_ids[0]       )

											A      						=    TextIteratorStreamer(__UpperCamelCase       )
											A      						=    {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
											A      						=    Thread(target=model.generate						, kwargs=__UpperCamelCase       )
											thread.start()
											A      						=    ''
											for new_text in streamer:
																		streamer_text += new_text

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int       )		->     Tuple:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase       )
											A      						=    greedy_ids[:, input_ids.shape[1] :]
											A      						=    tokenizer.decode(new_greedy_ids[0]       )

											with CaptureStdout() as cs:
																		A      						=    TextStreamer(__UpperCamelCase						, skip_prompt=__UpperCamelCase       )
																		model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase						, streamer=__UpperCamelCase       )
											# The greedy text should be printed to stdout, except for the final "\n" in the streamer
											A      						=    cs.out[:-1]

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Tuple:
											# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
											# with actual models -- the dummy models' tokenizers are not aligned with their models, and
											# `skip_special_tokens=True` has no effect on them
											A      						=    AutoTokenizer.from_pretrained('distilgpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('distilgpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    torch.ones((1, 5)						, device=__UpperCamelCase       ).long() * model.config.bos_token_id
											with CaptureStdout() as cs:
																		A      						=    TextStreamer(__UpperCamelCase						, skip_special_tokens=__UpperCamelCase       )
																		model.generate(__UpperCamelCase						, max_new_tokens=1						, do_sample=__UpperCamelCase						, streamer=__UpperCamelCase       )

											# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
											# re-tokenized, must only contain one token
											A      						=    cs.out[:-1]  # Remove the final "\n"
											A      						=    tokenizer(__UpperCamelCase						, return_tensors='pt'       )
											self.assertEqual(streamer_text_tokenized.input_ids.shape						, (1, 1)       )


				def __UpperCamelCase						(							self      :  Any       )		->     Dict:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    TextIteratorStreamer(__UpperCamelCase						, timeout=0.0_0_1       )
											A      						=    {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
											A      						=    Thread(target=model.generate						, kwargs=__UpperCamelCase       )
											thread.start()

											# The streamer will timeout after 0.001 seconds, so an exception will be raised
											with self.assertRaises(__UpperCamelCase       ):
																		A      						=    ''
																		for new_text in streamer:
																									streamer_text += new_text