




'''simple docstring'''



from __future__ import annotations

import pandas as pd




def    lowercase__(       __UpperCamelCase: list[int]	,__UpperCamelCase: list[int]	,__UpperCamelCase: int  ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					:	Any   =		[0] * no_of_processes
      SCREAMING_SNAKE_CASE					:	int   =		[0] * no_of_processes
      # Copy the burst time into remaining_time[]
      for i in range(snake_case_  ):
            SCREAMING_SNAKE_CASE					:	Any   =		burst_time[i]

      SCREAMING_SNAKE_CASE					:	Tuple   =		0
      SCREAMING_SNAKE_CASE					:	Tuple   =		0
      SCREAMING_SNAKE_CASE					:	Optional[int]   =		9_99_99_99_99
      SCREAMING_SNAKE_CASE					:	Optional[Any]   =		0
      SCREAMING_SNAKE_CASE					:	Dict   =		False

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

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

            SCREAMING_SNAKE_CASE					:	Any   =		remaining_time[short]
            if minm == 0:
                  SCREAMING_SNAKE_CASE					:	Optional[int]   =		9_99_99_99_99

            if remaining_time[short] == 0:
                  complete += 1
                  SCREAMING_SNAKE_CASE					:	Optional[Any]   =		False

                  # Find finish time of current process
                  SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		increment_time + 1

                  # Calculate waiting time
                  SCREAMING_SNAKE_CASE					:	Optional[int]   =		finish_time - arrival_time[short]
                  SCREAMING_SNAKE_CASE					:	List[Any]   =		finar - burst_time[short]

                  if waiting_time[short] < 0:
                        SCREAMING_SNAKE_CASE					:	Dict   =		0

        # Increment time
            increment_time += 1
      return waiting_time




def    lowercase__(       __UpperCamelCase: list[int]	,__UpperCamelCase: int	,__UpperCamelCase: list[int]  ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					:	Dict   =		[0] * no_of_processes
      for i in range(snake_case_  ):
            SCREAMING_SNAKE_CASE					:	Dict   =		burst_time[i] + waiting_time[i]
      return turn_around_time




def    lowercase__(       __UpperCamelCase: list[int]	,__UpperCamelCase: list[int]	,__UpperCamelCase: int  ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					:	List[str]   =		0
      SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		0
      for i in range(snake_case_  ):
            SCREAMING_SNAKE_CASE					:	Optional[Any]   =		total_waiting_time + waiting_time[i]
            SCREAMING_SNAKE_CASE					:	List[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")
      UpperCamelCase_       =							int(input())
      UpperCamelCase_       =							[0] * no_of_processes
      UpperCamelCase_       =							[0] * no_of_processes
      UpperCamelCase_       =							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))
            UpperCamelCase_     ,  UpperCamelCase_       =							map(int, input().split())

      UpperCamelCase_       =							calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

      calculate_average_times(waiting_time, turn_around_time, no_of_processes)

      UpperCamelCase_       =							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"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_ =			logging.get_logger(__name__)

a_ =			{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """bert"""

 def __init__(    self					,						__lowerCamelCase=3_0522					,						__lowerCamelCase=768					,						__lowerCamelCase=12					,						__lowerCamelCase=12					,						__lowerCamelCase=3072					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.1					,						__lowerCamelCase=0.1					,						__lowerCamelCase=512					,						__lowerCamelCase=2					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-1_2					,						__lowerCamelCase=0					,						__lowerCamelCase="absolute"					,						__lowerCamelCase=True					,						__lowerCamelCase=None					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(pad_token_id=__lowerCamelCase					,						**__lowerCamelCase  )

     __A    :						Dict						 =     vocab_size
     __A    :						Any						 =     hidden_size
     __A    :						str						 =     num_hidden_layers
     __A    :						int						 =     num_attention_heads
     __A    :						Optional[int]						 =     hidden_act
     __A    :						List[Any]						 =     intermediate_size
     __A    :						Tuple						 =     hidden_dropout_prob
     __A    :						Optional[int]						 =     attention_probs_dropout_prob
     __A    :						Optional[int]						 =     max_position_embeddings
     __A    :						Optional[Any]						 =     type_vocab_size
     __A    :						Optional[Any]						 =     initializer_range
     __A    :						Dict						 =     layer_norm_eps
     __A    :						Any						 =     position_embedding_type
     __A    :						Optional[int]						 =     use_cache
     __A    :						str						 =     classifier_dropout






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if self.task == "multiple-choice":
         __A    :						str						 =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         __A    :						Tuple						 =     {0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
             ('''token_type_ids''', dynamic_axis),
         ]  )




