




'''simple docstring'''


from __future__ import annotations

import pandas as pd
def 		snake_case_   (UpperCamelCase	:  list[int]			,						UpperCamelCase	:  list[int]			,						UpperCamelCase	:  int				):





							'''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     					=    9_9999_9999
							_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     					=    9_9999_9999

														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 		snake_case_   (UpperCamelCase	:  list[int]			,						UpperCamelCase	:  int			,						UpperCamelCase	:  list[int]				):





							'''simple docstring'''


							_a     					=    [0] * no_of_processes
							for i in range(UpperCamelCase				):
														_a     					=    burst_time[i] + waiting_time[i]
							return turn_around_time
def 		snake_case_   (UpperCamelCase	:  list[int]			,						UpperCamelCase	:  list[int]			,						UpperCamelCase	:  int				):





							'''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')
				_snake_case				:				Any       							=		int(input())
				_snake_case				:				Union[str, Any]       							=		[0] * no_of_processes
				_snake_case				:				Union[str, Any]       							=		[0] * no_of_processes
				_snake_case				:				Tuple       							=		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))
								_snake_case							,    _snake_case				:				Any       							=		map(int, input().split())

				_snake_case				:				Dict       							=		calculate_waitingtime(arrival_time, burst_time, no_of_processes)

				_snake_case				:				Dict       							=		burst_time
				_snake_case				:				List[Any]       							=		no_of_processes
				_snake_case				:				Optional[Any]       							=		waiting_time
				_snake_case				:				str       							=		calculate_turnaroundtime(bt, n, wt)

				calculate_average_times(waiting_time, turn_around_time, no_of_processes)

				_snake_case				:				Optional[Any]       							=		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)







'''simple docstring'''


import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


_snake_case				:				int       							=		get_tests_dir('fixtures')
_snake_case				:				Tuple       							=		get_tests_dir('fixtures/dummy_feature_extractor_config.json')
_snake_case				:				Optional[int]       							=		get_tests_dir('fixtures/dummy-config.json')

class       A   (		unittest.TestCase   ):







							def        __lowerCAmelCase      (  self	:  int )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    0







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


														"""simple docstring"""


														_a     					=    AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h''' )
														self.assertIsInstance(lowerCAmelCase_							,      lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  str )      ->							Tuple:


														"""simple docstring"""


														_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ )
														self.assertIsInstance(lowerCAmelCase_							,      lowerCAmelCase_ )







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


														"""simple docstring"""


														with tempfile.TemporaryDirectory() as tmpdirname:
																					_a     					=    WavaVecaConfig()

																					# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
																					_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ ).to_dict()

																					config_dict.pop('''feature_extractor_type''' )
																					_a     					=    WavaVecaFeatureExtractor(**lowerCAmelCase_ )

																					# save in new folder
																					model_config.save_pretrained(lowerCAmelCase_ )
																					config.save_pretrained(lowerCAmelCase_ )

																					_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ )

																					# make sure private variable is not incorrectly saved
																					_a     					=    json.loads(config.to_json_string() )
																					self.assertTrue('''_processor_class''' not in dict_as_saved )

														self.assertIsInstance(lowerCAmelCase_							,      lowerCAmelCase_ )







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


														"""simple docstring"""


														_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ )
														self.assertIsInstance(lowerCAmelCase_							,      lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							str:


														"""simple docstring"""


														with self.assertRaisesRegex(
														    lowerCAmelCase_							,      '''bert-base is not a local folder and is not a valid model identifier''' ):
																					_a     					=    AutoFeatureExtractor.from_pretrained('''bert-base''' )







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


														"""simple docstring"""


														with self.assertRaisesRegex(
														    lowerCAmelCase_							,      R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
																					_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_							,      revision='''aaaaaa''' )







							def        __lowerCAmelCase      (  self	:  Any )      ->							Dict:


														"""simple docstring"""


														with self.assertRaisesRegex(
														    lowerCAmelCase_							,      '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''							,      ):
																					_a     					=    AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model''' )







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


														"""simple docstring"""


														with self.assertRaises(lowerCAmelCase_ ):
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor''' )
														# If remote code is disabled, we can't load this config.
														with self.assertRaises(lowerCAmelCase_ ):
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor'''							,      trust_remote_code=lowerCAmelCase_ )

														_a     					=    AutoFeatureExtractor.from_pretrained(
														    '''hf-internal-testing/test_dynamic_feature_extractor'''							,      trust_remote_code=lowerCAmelCase_ )
														self.assertEqual(feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )

														# Test feature extractor can be reloaded.
														with tempfile.TemporaryDirectory() as tmp_dir:
																					feature_extractor.save_pretrained(lowerCAmelCase_ )
																					_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_							,      trust_remote_code=lowerCAmelCase_ )
														self.assertEqual(reloaded_feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )







							def        __lowerCAmelCase      (  self	:  int )      ->							Optional[Any]:


														"""simple docstring"""


														try:
																					AutoConfig.register('''custom'''							,      lowerCAmelCase_ )
																					AutoFeatureExtractor.register(lowerCAmelCase_							,      lowerCAmelCase_ )
																					# Trying to register something existing in the Transformers library will raise an error
																					with self.assertRaises(lowerCAmelCase_ ):
																												AutoFeatureExtractor.register(lowerCAmelCase_							,      lowerCAmelCase_ )

																					# Now that the config is registered, it can be used as any other config with the auto-API
																					_a     					=    CustomFeatureExtractor.from_pretrained(lowerCAmelCase_ )
																					with tempfile.TemporaryDirectory() as tmp_dir:
																												feature_extractor.save_pretrained(lowerCAmelCase_ )
																												_a     					=    AutoFeatureExtractor.from_pretrained(lowerCAmelCase_ )
																												self.assertIsInstance(lowerCAmelCase_							,      lowerCAmelCase_ )

														finally:
																					if "custom" in CONFIG_MAPPING._extra_content:
																												del CONFIG_MAPPING._extra_content["custom"]
																					if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																												del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							Any:


														"""simple docstring"""


														class       A   (		_a   ):
																					lowercase_          =    True

														try:
																					AutoConfig.register('''custom'''							,      lowerCAmelCase_ )
																					AutoFeatureExtractor.register(lowerCAmelCase_							,      lowerCAmelCase_ )
																					# If remote code is not set, the default is to use local
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor''' )
																					self.assertEqual(feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )
																					self.assertTrue(feature_extractor.is_local )

																					# If remote code is disabled, we load the local one.
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor'''							,      trust_remote_code=lowerCAmelCase_ )
																					self.assertEqual(feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )
																					self.assertTrue(feature_extractor.is_local )

																					# If remote is enabled, we load from the Hub
																					_a     					=    AutoFeatureExtractor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_feature_extractor'''							,      trust_remote_code=lowerCAmelCase_ )
																					self.assertEqual(feature_extractor.__class__.__name__							,      '''NewFeatureExtractor''' )
																					self.assertTrue(not hasattr(lowerCAmelCase_							,      '''is_local''' ) )

														finally:
																					if "custom" in CONFIG_MAPPING._extra_content:
																												del CONFIG_MAPPING._extra_content["custom"]
																					if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																												del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]

