





'''simple docstring'''






from statistics import mean

import numpy as np
def 							_lowerCamelCase(  UpperCamelCase__    : list    ,       UpperCamelCase__    : list    ,       UpperCamelCase__    : list    ,       UpperCamelCase__    : int		)		->  list:
    A       :       Optional[int]							=						0
    # Number of processes finished
    A       :       Tuple							=						0
    # Displays the finished process.
    # If it is 0, the performance is completed if it is 1, before the performance.
    A       :       Dict							=						[0] * no_of_process
    # List to include calculation results
    A       :       Union[str, Any]							=						[0] * no_of_process

    # Sort by arrival time.
    A       :       Tuple							=						[burst_time[i] for i in np.argsort(UpperCamelCase__		)]
    A       :       Dict							=						[process_name[i] for i in np.argsort(UpperCamelCase__		)]
    arrival_time.sort()

    while no_of_process > finished_process_count:
        A       :       Any							=						0
        while finished_process[i] == 1:
            i += 1
        if current_time < arrival_time[i]:
            A       :       str							=						arrival_time[i]

        A       :       Dict							=						0
        # Index showing the location of the process being performed
        A       :       Optional[Any]							=						0
        # Saves the current response ratio.
        A       :       Optional[int]							=						0
        for i in range(0    ,       UpperCamelCase__		):
            if finished_process[i] == 0 and arrival_time[i] <= current_time:
                A       :       Tuple							=						(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
                    i
                ]
            if response_ratio < temp:
                A       :       Optional[int]							=						temp
                A       :       Union[str, Any]							=						i

        # Calculate the turn around time
        A       :       List[Any]							=						current_time + burst_time[loc] - arrival_time[loc]
        current_time += burst_time[loc]
        # Indicates that the process has been performed.
        A       :       Any							=						1
        # Increase finished_process_count by 1
        finished_process_count += 1

    return turn_around_time
def 							_lowerCamelCase(  UpperCamelCase__    : list    ,       UpperCamelCase__    : list    ,       UpperCamelCase__    : list    ,       UpperCamelCase__    : int		)		->  list:
    A       :       Dict							=						[0] * no_of_process
    for i in range(0    ,       UpperCamelCase__		):
        A       :       Any							=						turn_around_time[i] - burst_time[i]
    return waiting_time


if __name__ == "__main__":
     snake_case_     =	5
     snake_case_     =	["""A""", """B""", """C""", """D""", """E"""]
     snake_case_     =	[1, 2, 3, 4, 5]
     snake_case_     =	[1, 2, 3, 4, 5]

     snake_case_     =	calculate_turn_around_time(
         process_name, arrival_time, burst_time, no_of_process
     )
     snake_case_     =	calculate_waiting_time(
         process_name, turn_around_time, burst_time, no_of_process
     )

     print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
     for i in range(0, no_of_process):
          print(
              f'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
              f'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
          )

     print(f'''average waiting time : {mean(waiting_time):.5f}''')
     print(f'''average turn around time : {mean(turn_around_time):.5f}''')







'''simple docstring'''






import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[int]		)		->  List[str]:
    return EnvironmentCommand()
def 							_lowerCamelCase(  UpperCamelCase__    : List[Any]		)		->  Tuple:
    return EnvironmentCommand(args.accelerate_config_file		)





class   _lowercase						(							a				):


   @staticmethod
   def 					snake_case				( _UpperCAmelCase					):
       A       :       Optional[Any]							=						parser.add_parser('''env'''					)
       download_parser.set_defaults(func=_UpperCAmelCase					)
       download_parser.add_argument(
           '''--accelerate-config_file'''    ,   default=_UpperCAmelCase    ,   help='''The accelerate config file to use for the default values in the launching script.'''    ,   )
       download_parser.set_defaults(func=_UpperCAmelCase					)


   def __init__( self    ,   _UpperCAmelCase    ,   *_UpperCAmelCase					):
       A       :       Any							=						accelerate_config_file


   def 					snake_case				( self					):
       A       :       Any							=						'''not installed'''
       if is_safetensors_available():
           import safetensors

           A       :       Dict							=						safetensors.__version__
       elif importlib.util.find_spec('''safetensors'''					) is not None:
           import safetensors

           A       :       int							=						f'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

       A       :       Tuple							=						'''not installed'''
       A       :       Tuple							=						'''not found'''
       if is_accelerate_available():
           import accelerate
           from accelerate.commands.config import default_config_file, load_config_from_file

           A       :       Any							=						accelerate.__version__
           # Get the default from the config file.
           if self._accelerate_config_file is not None or os.path.isfile(_UpperCAmelCase					):
               A       :       int							=						load_config_from_file(self._accelerate_config_file					).to_dict()

           A       :       Tuple							=						(
               '''\n'''.join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]					)
               if isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					)
               else f'''\t{accelerate_config}'''
           )

       A       :       str							=						'''not installed'''
       A       :       Optional[Any]							=						'''NA'''
       if is_torch_available():
           import torch

           A       :       Optional[int]							=						torch.__version__
           A       :       List[Any]							=						torch.cuda.is_available()

       A       :       Dict							=						'''not installed'''
       A       :       Any							=						'''NA'''
       if is_tf_available():
           import tensorflow as tf

           A       :       Optional[int]							=						tf.__version__
           try:
               # deprecated in v2.1
               A       :       int							=						tf.test.is_gpu_available()
           except AttributeError:
               # returns list of devices, convert to bool
               A       :       Optional[int]							=						bool(tf.config.list_physical_devices('''GPU'''					)					)

       A       :       str							=						'''not installed'''
       A       :       Dict							=						'''not installed'''
       A       :       int							=						'''not installed'''
       A       :       Tuple							=						'''NA'''
       if is_flax_available():
           import flax
           import jax
           import jaxlib

           A       :       Any							=						flax.__version__
           A       :       Tuple							=						jax.__version__
           A       :       int							=						jaxlib.__version__
           A       :       List[Any]							=						jax.lib.xla_bridge.get_backend().platform

       A       :       Union[str, Any]							=						{
           '''`transformers` version''': version,
           '''Platform''': platform.platform(),
           '''Python version''': platform.python_version(),
           '''Huggingface_hub version''': huggingface_hub.__version__,
           '''Safetensors version''': f'''{safetensors_version}''',
           '''Accelerate version''': f'''{accelerate_version}''',
           '''Accelerate config''': f'''{accelerate_config_str}''',
           '''PyTorch version (GPU?)''': f'''{pt_version} ({pt_cuda_available})''',
           '''Tensorflow version (GPU?)''': f'''{tf_version} ({tf_cuda_available})''',
           '''Flax version (CPU?/GPU?/TPU?)''': f'''{flax_version} ({jax_backend})''',
           '''Jax version''': f'''{jax_version}''',
           '''JaxLib version''': f'''{jaxlib_version}''',
           '''Using GPU in script?''': '''<fill in>''',
           '''Using distributed or parallel set-up in script?''': '''<fill in>''',
       }

       print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''					)
       print(self.format_dict(_UpperCAmelCase					)					)

       return info


   @staticmethod
   def 					snake_case				( _UpperCAmelCase					):
       return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]					) + "\n"
