"""simple docstring"""




import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
				from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class     UpperCamelCase__(			unittest.TestCase ):





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				Optional[Any]:
													for model_result in results.values():
																				for batch_size, sequence_length in zip(model_result['bs']		,model_result['ss']     ):
																											A__	     =							model_result['result'][batch_size][sequence_length]
																											self.assertIsNotNone(__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							'sshleifer/tiny-gpt2'
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result     )
													self.check_results_dict_not_empty(results.memory_inference_result     )





						def 				snake_case__	(       self     )					->				str:
													A__	     =							'sgugger/tiny-distilbert-classification'
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,only_pretrain_model=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result     )
													self.check_results_dict_not_empty(results.memory_inference_result     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							'sshleifer/tiny-gpt2'
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,torchscript=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result     )
													self.check_results_dict_not_empty(results.memory_inference_result     )





						@unittest.skipIf(torch_device == 'cpu'		,'Cant do half precision'     )
						def 				snake_case__	(       self     )					->				int:
													A__	     =							'sshleifer/tiny-gpt2'
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,fpaa=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result     )
													self.check_results_dict_not_empty(results.memory_inference_result     )





						def 				snake_case__	(       self     )					->				Any:
													A__	     =							'sshleifer/tiny-gpt2'
													A__	     =							AutoConfig.from_pretrained(__UpperCAmelCase     )
													# set architectures equal to `None`
													A__	     =							None
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase		,configs=[config]     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result     )
													self.check_results_dict_not_empty(results.memory_inference_result     )





						def 				snake_case__	(       self     )					->				Tuple:
													A__	     =							'sshleifer/tiny-gpt2'
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_train_result     )
													self.check_results_dict_not_empty(results.memory_train_result     )





						@unittest.skipIf(torch_device == 'cpu'		,'Can\'t do half precision'     )
						def 				snake_case__	(       self     )					->				int:
													A__	     =							'sshleifer/tiny-gpt2'
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,fpaa=__UpperCAmelCase		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_train_result     )
													self.check_results_dict_not_empty(results.memory_train_result     )





						def 				snake_case__	(       self     )					->				int:
													A__	     =							'sshleifer/tiny-gpt2'
													A__	     =							AutoConfig.from_pretrained(__UpperCAmelCase     )
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase		,configs=[config]     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result     )
													self.check_results_dict_not_empty(results.memory_inference_result     )





						def 				snake_case__	(       self     )					->				int:
													A__	     =							'sshleifer/tinier_bart'
													A__	     =							AutoConfig.from_pretrained(__UpperCAmelCase     )
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase		,configs=[config]     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result     )
													self.check_results_dict_not_empty(results.memory_inference_result     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							'sshleifer/tiny-gpt2'
													A__	     =							AutoConfig.from_pretrained(__UpperCAmelCase     )
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase		,configs=[config]     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_train_result     )
													self.check_results_dict_not_empty(results.memory_train_result     )





						def 				snake_case__	(       self     )					->				Optional[int]:
													A__	     =							'sshleifer/tinier_bart'
													A__	     =							AutoConfig.from_pretrained(__UpperCAmelCase     )
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase		,configs=[config]     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_train_result     )
													self.check_results_dict_not_empty(results.memory_train_result     )





						def 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							'sshleifer/tiny-gpt2'
													with tempfile.TemporaryDirectory() as tmp_dir:
																				A__	     =							PyTorchBenchmarkArguments(
																				    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,save_to_csv=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,inference_time_csv_file=os.path.join(__UpperCAmelCase		,'inf_time.csv'     )		,train_memory_csv_file=os.path.join(__UpperCAmelCase		,'train_mem.csv'     )		,inference_memory_csv_file=os.path.join(__UpperCAmelCase		,'inf_mem.csv'     )		,train_time_csv_file=os.path.join(__UpperCAmelCase		,'train_time.csv'     )		,env_info_csv_file=os.path.join(__UpperCAmelCase		,'env.csv'     )		,multi_process=__UpperCAmelCase		,)
																				A__	     =							PyTorchBenchmark(__UpperCAmelCase     )
																				benchmark.run()
																				self.assertTrue(Path(os.path.join(__UpperCAmelCase		,'inf_time.csv'     )     ).exists()     )
																				self.assertTrue(Path(os.path.join(__UpperCAmelCase		,'train_time.csv'     )     ).exists()     )
																				self.assertTrue(Path(os.path.join(__UpperCAmelCase		,'inf_mem.csv'     )     ).exists()     )
																				self.assertTrue(Path(os.path.join(__UpperCAmelCase		,'train_mem.csv'     )     ).exists()     )
																				self.assertTrue(Path(os.path.join(__UpperCAmelCase		,'env.csv'     )     ).exists()     )





						def 				snake_case__	(       self     )					->				str:
													A__	     =							'sshleifer/tiny-gpt2'

													def _check_summary_is_not_empty(__UpperCAmelCase     ):
																				self.assertTrue(hasattr(__UpperCAmelCase		,'sequential'     )     )
																				self.assertTrue(hasattr(__UpperCAmelCase		,'cumulative'     )     )
																				self.assertTrue(hasattr(__UpperCAmelCase		,'current'     )     )
																				self.assertTrue(hasattr(__UpperCAmelCase		,'total'     )     )

													with tempfile.TemporaryDirectory() as tmp_dir:
																				A__	     =							PyTorchBenchmarkArguments(
																				    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,log_filename=os.path.join(__UpperCAmelCase		,'log.txt'     )		,log_print=__UpperCAmelCase		,trace_memory_line_by_line=__UpperCAmelCase		,multi_process=__UpperCAmelCase		,)
																				A__	     =							PyTorchBenchmark(__UpperCAmelCase     )
																				A__	     =							benchmark.run()
																				_check_summary_is_not_empty(result.inference_summary     )
																				_check_summary_is_not_empty(result.train_summary     )
																				self.assertTrue(Path(os.path.join(__UpperCAmelCase		,'log.txt'     )     ).exists()     )






"""simple docstring"""




from __future__ import annotations

import pandas as pd






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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

							A__	     =							0
							A__	     =							0
							A__	     =							999_999_999
							A__	     =							0
							A__	     =							False

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

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

														A__	     =							remaining_time[short]
														if minm == 0:
																					A__	     =							999_999_999

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

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

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

																					if waiting_time[short] < 0:
																												A__	     =							0

        # Increment time
														increment_time += 1
							return waiting_time






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





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






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							0
							A__	     =							0
							for i in range(UpperCamelCase__				):
														A__	     =							total_waiting_time + waiting_time[i]
														A__	     =							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)





