"""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   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							return EnvironmentCommand()






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							return EnvironmentCommand(args.accelerate_config_file				)


class     UpperCamelCase__(			_UpperCAmelCase ):





						@staticmethod
						def 				snake_case__	(       __UpperCAmelCase     )					->				int:
													A__	     =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     )					->				None:
													A__	     =accelerate_config_file





						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     ='not installed'
													if is_safetensors_available():
																				import safetensors

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

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

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

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

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

													A__	     ='not installed'
													A__	     ='NA'
													if is_torch_available():
																				import torch

																				A__	     =torch.__version__
																				A__	     =torch.cuda.is_available()

													A__	     ='not installed'
													A__	     ='NA'
													if is_tf_available():
																				import tensorflow as tf

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

													A__	     ='not installed'
													A__	     ='not installed'
													A__	     ='not installed'
													A__	     ='NA'
													if is_flax_available():
																				import flax
																				import jax
																				import jaxlib

																				A__	     =flax.__version__
																				A__	     =jax.__version__
																				A__	     =jaxlib.__version__
																				A__	     =jax.lib.xla_bridge.get_backend().platform

													A__	     ={
													    '`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     )					->				Dict:
													return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]     ) + "\n"






"""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)





