






'''simple docstring'''
import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__magic_name__		:     Optional[int]					=    logging.get_logger(__name__)







class 	UpperCamelCase__					:

		"""simple docstring"""




		def __init__( self						:						Tuple							,    __A						:						Union[str, Any]							,    __A						:						List[Any]    ):







							"""simple docstring"""
							_lowercase			= question_encoder
							_lowercase			= generator
							_lowercase			= self.question_encoder




		def       snake_case     ( self						:						int							,    __A						:						List[str]    ):







							"""simple docstring"""

							if os.path.isfile(a__    ):
												raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""    )
							os.makedirs(a__							,    exist_ok=a__    )
							_lowercase			= os.path.join(a__							,    "question_encoder_tokenizer"    )
							_lowercase			= os.path.join(a__							,    "generator_tokenizer"    )
							self.question_encoder.save_pretrained(a__    )
							self.generator.save_pretrained(a__    )




		@classmethod
		def       snake_case     ( cls						:						Union[str, Any]							,    __A						:						List[Any]							,    **__A						:						Tuple    ):







							"""simple docstring"""
							from ..auto.tokenization_auto import AutoTokenizer

							_lowercase			= kwargs.pop("config"							,    a__    )

							if config is None:
												_lowercase			= RagConfig.from_pretrained(a__    )

							_lowercase			= AutoTokenizer.from_pretrained(
							    a__							,    config=config.question_encoder							,    subfolder="question_encoder_tokenizer"    )
							_lowercase			= AutoTokenizer.from_pretrained(
							    a__							,    config=config.generator							,    subfolder="generator_tokenizer"    )
							return cls(question_encoder=a__							,    generator=a__    )




		def __call__( self						:						Dict							,    *__A						:						List[Any]							,    **__A						:						Optional[int]    ):







							"""simple docstring"""
							return self.current_tokenizer(*a__							,    **a__    )




		def       snake_case     ( self						:						int							,    *__A						:						str							,    **__A						:						int    ):







							"""simple docstring"""
							return self.generator.batch_decode(*a__							,    **a__    )




		def       snake_case     ( self						:						str							,    *__A						:						Any							,    **__A						:						int    ):







							"""simple docstring"""
							return self.generator.decode(*a__							,    **a__    )




		def       snake_case     ( self						:						Union[str, Any]    ):







							"""simple docstring"""
							_lowercase			= self.question_encoder




		def       snake_case     ( self						:						List[str]    ):







							"""simple docstring"""
							_lowercase			= self.generator




		def       snake_case     ( self						:						List[Any]							,    __A						:						List[str]							,    __A						:						Optional[List[str]] = None							,    __A						:						Optional[int] = None							,    __A						:						Optional[int] = None							,    __A						:						str = "longest"							,    __A						:						str = None							,    __A						:						bool = True							,    **__A						:						str							,    ):







							"""simple docstring"""
							warnings.warn(
							    "`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the "
							    "regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` "
							    "context manager to prepare your targets. See the documentation of your specific tokenizer for more "
							    "details"							,    a__							,    )
							if max_length is None:
												_lowercase			= self.current_tokenizer.model_max_length
							_lowercase			= self(
							    a__							,    add_special_tokens=a__							,    return_tensors=a__							,    max_length=a__							,    padding=a__							,    truncation=a__							,    **a__							,    )
							if tgt_texts is None:
												return model_inputs
							# Process tgt_texts
							if max_target_length is None:
												_lowercase			= self.current_tokenizer.model_max_length
							_lowercase			= self(
							    text_target=a__							,    add_special_tokens=a__							,    return_tensors=a__							,    padding=a__							,    max_length=a__							,    truncation=a__							,    **a__							,    )
							_lowercase			= labels["input_ids"]
							return model_inputs



'''simple docstring'''

from __future__ import annotations

import pandas as pd


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Tuple     		=	[0] * no_of_processes
 lowerCAmelCase__     :   Any     		=	[0] * no_of_processes
 # Copy the burst time into remaining_time[]
 for i in range(lowerCamelCase_			):
  lowerCAmelCase__     :   Dict     		=	burst_time[i]

 lowerCAmelCase__     :   str     		=	0
 lowerCAmelCase__     :   Optional[Any]     		=	0
 lowerCAmelCase__     :   Optional[Any]     		=	9_9_9_9_9_9_9_9_9
 lowerCAmelCase__     :   int     		=	0
 lowerCAmelCase__     :   str     		=	False

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

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

  lowerCAmelCase__     :   Optional[Any]     		=	remaining_time[short]
  if minm == 0:
   lowerCAmelCase__     :   Optional[int]     		=	9_9_9_9_9_9_9_9_9

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

   # Find finish time of current process
   lowerCAmelCase__     :   Tuple     		=	increment_time + 1

   # Calculate waiting time
   lowerCAmelCase__     :   Dict     		=	finish_time - arrival_time[short]
   lowerCAmelCase__     :   Optional[Any]     		=	finar - burst_time[short]

   if waiting_time[short] < 0:
    lowerCAmelCase__     :   Tuple     		=	0

        # Increment time
  increment_time += 1
 return waiting_time


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[Any]     		=	[0] * no_of_processes
 for i in range(lowerCamelCase_			):
  lowerCAmelCase__     :   Dict     		=	burst_time[i] + waiting_time[i]
 return turn_around_time


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Union[str, Any]     		=	0
 lowerCAmelCase__     :   Optional[int]     		=	0
 for i in range(lowerCamelCase_			):
  lowerCAmelCase__     :   Tuple     		=	total_waiting_time + waiting_time[i]
  lowerCAmelCase__     :   str     		=	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									=    int(input())
     snake_case									=    [0] * no_of_processes
     snake_case									=    [0] * no_of_processes
     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))
          snake_case					,					snake_case									=    map(int, input().split())

     snake_case									=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

     calculate_average_times(waiting_time, turn_around_time, no_of_processes)

     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)





