





import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
						import tensorflow as tf

						from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






						def UpperCAmelCase_			( self : Optional[int]						,				_A : Dict		)							->      Union[str, Any]:







							"""simple docstring"""


							for model_result in results.values():
								for batch_size, sequence_length in zip(model_result['bs']						,				model_result['ss']		):
									snake_case_						:     Dict     =		model_result['result'][batch_size][sequence_length]
									self.assertIsNotNone(_A		)






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







							"""simple docstring"""


							snake_case_						:     Tuple     =		'sshleifer/tiny-gpt2'
							snake_case_						:     Optional[Any]     =		TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]						,				training=_A						,				inference=_A						,				sequence_lengths=[8]						,				batch_sizes=[1]						,				eager_mode=_A						,				multi_process=_A						,				)
							snake_case_						:     Any     =		TensorFlowBenchmark(_A		)
							snake_case_						:     str     =		benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result		)
							self.check_results_dict_not_empty(results.memory_inference_result		)






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







							"""simple docstring"""


							snake_case_						:     int     =		'sgugger/tiny-distilbert-classification'
							snake_case_						:     List[Any]     =		TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]						,				training=_A						,				inference=_A						,				sequence_lengths=[8]						,				batch_sizes=[1]						,				multi_process=_A						,				only_pretrain_model=_A						,				)
							snake_case_						:     List[str]     =		TensorFlowBenchmark(_A		)
							snake_case_						:     List[str]     =		benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result		)
							self.check_results_dict_not_empty(results.memory_inference_result		)






						def UpperCAmelCase_			( self : Tuple		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     str     =		'sshleifer/tiny-gpt2'
							snake_case_						:     Optional[int]     =		TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]						,				training=_A						,				inference=_A						,				sequence_lengths=[8]						,				batch_sizes=[1]						,				multi_process=_A						,				)
							snake_case_						:     Union[str, Any]     =		TensorFlowBenchmark(_A		)
							snake_case_						:     Any     =		benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result		)
							self.check_results_dict_not_empty(results.memory_inference_result		)






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







							"""simple docstring"""


							snake_case_						:     List[str]     =		'sshleifer/tiny-gpt2'
							snake_case_						:     str     =		AutoConfig.from_pretrained(_A		)
							snake_case_						:     List[str]     =		TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]						,				training=_A						,				inference=_A						,				sequence_lengths=[8]						,				batch_sizes=[1]						,				eager_mode=_A						,				multi_process=_A						,				)
							snake_case_						:     int     =		TensorFlowBenchmark(_A						,				[config]		)
							snake_case_						:     Any     =		benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result		)
							self.check_results_dict_not_empty(results.memory_inference_result		)






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







							"""simple docstring"""


							snake_case_						:     Any     =		'sshleifer/tiny-gpt2'
							snake_case_						:     List[Any]     =		AutoConfig.from_pretrained(_A		)
							snake_case_						:     Dict     =		TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]						,				training=_A						,				inference=_A						,				sequence_lengths=[8]						,				batch_sizes=[1]						,				multi_process=_A						,				)
							snake_case_						:     List[Any]     =		TensorFlowBenchmark(_A						,				[config]		)
							snake_case_						:     Optional[int]     =		benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result		)
							self.check_results_dict_not_empty(results.memory_inference_result		)






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







							"""simple docstring"""


							snake_case_						:     List[Any]     =		'sshleifer/tiny-gpt2'
							snake_case_						:     Tuple     =		TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]						,				training=_A						,				inference=_A						,				sequence_lengths=[8]						,				batch_sizes=[1]						,				multi_process=_A						,				)
							snake_case_						:     List[Any]     =		TensorFlowBenchmark(_A		)
							snake_case_						:     Optional[int]     =		benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result		)
							self.check_results_dict_not_empty(results.memory_train_result		)






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







							"""simple docstring"""


							snake_case_						:     str     =		'sshleifer/tiny-gpt2'
							snake_case_						:     Any     =		AutoConfig.from_pretrained(_A		)
							snake_case_						:     str     =		TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]						,				training=_A						,				inference=_A						,				sequence_lengths=[8]						,				batch_sizes=[1]						,				multi_process=_A						,				)
							snake_case_						:     List[Any]     =		TensorFlowBenchmark(_A						,				[config]		)
							snake_case_						:     List[Any]     =		benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result		)
							self.check_results_dict_not_empty(results.memory_train_result		)






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







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		'patrickvonplaten/t5-tiny-random'
							snake_case_						:     Optional[Any]     =		AutoConfig.from_pretrained(_A		)
							snake_case_						:     Any     =		TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]						,				training=_A						,				inference=_A						,				sequence_lengths=[8]						,				batch_sizes=[1]						,				multi_process=_A						,				)
							snake_case_						:     Tuple     =		TensorFlowBenchmark(_A						,				configs=[config]		)
							snake_case_						:     Optional[Any]     =		benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result		)
							self.check_results_dict_not_empty(results.memory_inference_result		)






						@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('GPU'		)		) == 0						,				'Cannot do xla on CPU.'		)
						def UpperCAmelCase_			( self : str		)							->      Any:







							"""simple docstring"""


							snake_case_						:     Union[str, Any]     =		'sshleifer/tiny-gpt2'
							snake_case_						:     Any     =		TensorFlowBenchmarkArguments(
							    models=[MODEL_ID]						,				training=_A						,				inference=_A						,				sequence_lengths=[8]						,				batch_sizes=[1]						,				use_xla=_A						,				multi_process=_A						,				)
							snake_case_						:     Any     =		TensorFlowBenchmark(_A		)
							snake_case_						:     Any     =		benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result		)
							self.check_results_dict_not_empty(results.memory_inference_result		)






						def UpperCAmelCase_			( self : str		)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     List[str]     =		'sshleifer/tiny-gpt2'
							with tempfile.TemporaryDirectory() as tmp_dir:
								snake_case_						:     Tuple     =		TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]						,				inference=_A						,				save_to_csv=_A						,				sequence_lengths=[8]						,				batch_sizes=[1]						,				inference_time_csv_file=os.path.join(_A						,				'inf_time.csv'		)						,				inference_memory_csv_file=os.path.join(_A						,				'inf_mem.csv'		)						,				env_info_csv_file=os.path.join(_A						,				'env.csv'		)						,				multi_process=_A						,				)
								snake_case_						:     Union[str, Any]     =		TensorFlowBenchmark(_A		)
								benchmark.run()
								self.assertTrue(Path(os.path.join(_A						,				'inf_time.csv'		)		).exists()		)
								self.assertTrue(Path(os.path.join(_A						,				'inf_mem.csv'		)		).exists()		)
								self.assertTrue(Path(os.path.join(_A						,				'env.csv'		)		).exists()		)






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







							"""simple docstring"""


							snake_case_						:     Union[str, Any]     =		'sshleifer/tiny-gpt2'

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

							with tempfile.TemporaryDirectory() as tmp_dir:
								snake_case_						:     int     =		TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]						,				inference=_A						,				sequence_lengths=[8]						,				batch_sizes=[1]						,				log_filename=os.path.join(_A						,				'log.txt'		)						,				log_print=_A						,				trace_memory_line_by_line=_A						,				eager_mode=_A						,				multi_process=_A						,				)
								snake_case_						:     int     =		TensorFlowBenchmark(_A		)
								snake_case_						:     Dict     =		benchmark.run()
								_check_summary_is_not_empty(result.inference_summary		)
								self.assertTrue(Path(os.path.join(_A						,				'log.txt'		)		).exists()		)








from __future__ import annotations

import pandas as pd


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     Optional[Any]     =		[0] * no_of_processes
	snake_case_						:     Tuple     =		[0] * no_of_processes
	# Copy the burst time into remaining_time[]
	for i in range(__a				):
		snake_case_						:     Union[str, Any]     =		burst_time[i]

	snake_case_						:     Optional[Any]     =		0
	snake_case_						:     Dict     =		0
	snake_case_						:     Any     =		9_99_99_99_99
	snake_case_						:     Tuple     =		0
	snake_case_						:     List[Any]     =		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:
					snake_case_						:     str     =		remaining_time[j]
					snake_case_						:     Any     =		j
					snake_case_						:     List[str]     =		True

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

		snake_case_						:     Any     =		remaining_time[short]
		if minm == 0:
			snake_case_						:     Dict     =		9_99_99_99_99

		if remaining_time[short] == 0:
			complete += 1
			snake_case_						:     List[str]     =		False

			# Find finish time of current process
			snake_case_						:     List[str]     =		increment_time + 1

			# Calculate waiting time
			snake_case_						:     Any     =		finish_time - arrival_time[short]
			snake_case_						:     Any     =		finar - burst_time[short]

			if waiting_time[short] < 0:
				snake_case_						:     Optional[int]     =		0

        # Increment time
		increment_time += 1
	return waiting_time


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     Tuple     =		[0] * no_of_processes
	for i in range(__a				):
		snake_case_						:     str     =		burst_time[i] + waiting_time[i]
	return turn_around_time


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     int     =		0
	snake_case_						:     Optional[Any]     =		0
	for i in range(__a				):
		snake_case_						:     int     =		total_waiting_time + waiting_time[i]
		snake_case_						:     Optional[Any]     =		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""")
						_SCREAMING_SNAKE_CASE 		=						int(input())
						_SCREAMING_SNAKE_CASE 		=						[0] * no_of_processes
						_SCREAMING_SNAKE_CASE 		=						[0] * no_of_processes
						_SCREAMING_SNAKE_CASE 		=						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))
												_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE 		=						map(int, input().split())

						_SCREAMING_SNAKE_CASE 		=						calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

						calculate_average_times(waiting_time, turn_around_time, no_of_processes)

						_SCREAMING_SNAKE_CASE 		=						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)

