
from __future__ import annotations

import pandas as pd






def   lowerCamelCase__							(					a		,		a		,		a      )      ->   list[int]:
   _A:  Optional[Any]					=       [0] * no_of_processes
   _A:  Tuple					=       [0] * no_of_processes
   # Copy the burst time into remaining_time[]
   for i in range(a      ):
      _A:  Optional[int]					=       burst_time[i]

   _A:  Tuple					=       0
   _A:  Union[str, Any]					=       0
   _A:  Tuple					=       9_99_99_99_99
   _A:  Dict					=       0
   _A:  int					=       False

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

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

      _A:  Tuple					=       remaining_time[short]
      if minm == 0:
         _A:  Union[str, Any]					=       9_99_99_99_99

      if remaining_time[short] == 0:
         complete += 1
         _A:  List[Any]					=       False

         # Find finish time of current process
         _A:  List[str]					=       increment_time + 1

         # Calculate waiting time
         _A:  Tuple					=       finish_time - arrival_time[short]
         _A:  Any					=       finar - burst_time[short]

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

        # Increment time
      increment_time += 1
   return waiting_time






def   lowerCamelCase__							(					a		,		a		,		a      )      ->   list[int]:
   _A:  str					=       [0] * no_of_processes
   for i in range(a      ):
      _A:  str					=       burst_time[i] + waiting_time[i]
   return turn_around_time






def   lowerCamelCase__							(					a		,		a		,		a      )      ->   None:
   _A:  Union[str, Any]					=       0
   _A:  Optional[int]					=       0
   for i in range(a      ):
      _A:  Optional[Any]					=       total_waiting_time + waiting_time[i]
      _A:  Any					=       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__		:     List[str]     			=      int(input())
   UpperCAmelCase__		:     Optional[Any]     			=      [0] * no_of_processes
   UpperCAmelCase__		:     Union[str, Any]     			=      [0] * no_of_processes
   UpperCAmelCase__		:     Any     			=      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__		:     List[Any]     			=      map(int, input().split())

   UpperCAmelCase__		:     Union[str, Any]     			=      calculate_waitingtime(arrival_time, burst_time, no_of_processes)

   UpperCAmelCase__		:     Any     			=      burst_time
   UpperCAmelCase__		:     str     			=      no_of_processes
   UpperCAmelCase__		:     str     			=      waiting_time
   UpperCAmelCase__		:     Optional[int]     			=      calculate_turnaroundtime(bt, n, wt)

   calculate_average_times(waiting_time, turn_around_time, no_of_processes)

   UpperCAmelCase__		:     str     			=      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)





from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__		:     Optional[Any]     			=      logging.get_logger(__name__)

UpperCAmelCase__		:     Any     			=      {
    'huggingface/informer-tourism-monthly': (
        'https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    List[Any] 				=    '''informer'''
   __UpperCamelCase		:    List[str] 				=    {
       '''hidden_size''': '''d_model''',
       '''num_attention_heads''': '''encoder_attention_heads''',
       '''num_hidden_layers''': '''encoder_layers''',
   }


   def __init__(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Optional[int] = None			,			lowerCAmelCase_     :							Optional[int] = None			,			lowerCAmelCase_     :							str = "student_t"			,			lowerCAmelCase_     :							str = "nll"			,			lowerCAmelCase_     :							int = 1			,			lowerCAmelCase_     :							List[int] = None			,			lowerCAmelCase_     :							Optional[Union[str, bool]] = "mean"			,			lowerCAmelCase_     :							int = 0			,			lowerCAmelCase_     :							int = 0			,			lowerCAmelCase_     :							int = 0			,			lowerCAmelCase_     :							int = 0			,			lowerCAmelCase_     :							Optional[List[int]] = None			,			lowerCAmelCase_     :							Optional[List[int]] = None			,			lowerCAmelCase_     :							int = 6_4			,			lowerCAmelCase_     :							int = 3_2			,			lowerCAmelCase_     :							int = 3_2			,			lowerCAmelCase_     :							int = 2			,			lowerCAmelCase_     :							int = 2			,			lowerCAmelCase_     :							int = 2			,			lowerCAmelCase_     :							int = 2			,			lowerCAmelCase_     :							bool = True			,			lowerCAmelCase_     :							str = "gelu"			,			lowerCAmelCase_     :							float = 0.05			,			lowerCAmelCase_     :							float = 0.1			,			lowerCAmelCase_     :							float = 0.1			,			lowerCAmelCase_     :							float = 0.1			,			lowerCAmelCase_     :							float = 0.1			,			lowerCAmelCase_     :							int = 1_0_0			,			lowerCAmelCase_     :							float = 0.02			,			lowerCAmelCase_     :							Tuple=True			,			lowerCAmelCase_     :							str = "prob"			,			lowerCAmelCase_     :							int = 5			,			lowerCAmelCase_     :							bool = True			,			**lowerCAmelCase_     :							str			,			):

      """simple docstring"""
      # time series specific configuration
      _A:  Optional[Any]					=       prediction_length
      _A:  Optional[Any]					=       context_length or prediction_length
      _A:  Dict					=       distribution_output
      _A:  List[str]					=       loss
      _A:  int					=       input_size
      _A:  List[str]					=       num_time_features
      _A:  Optional[Any]					=       lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
      _A:  str					=       scaling
      _A:  Optional[Any]					=       num_dynamic_real_features
      _A:  List[Any]					=       num_static_real_features
      _A:  Tuple					=       num_static_categorical_features

      # set cardinality
      if cardinality and num_static_categorical_features > 0:
         if len(lowerCAmelCase_     ) != num_static_categorical_features:
            raise ValueError(
                '''The cardinality should be a list of the same length as `num_static_categorical_features`'''     )
         _A:  str					=       cardinality
      else:
         _A:  Union[str, Any]					=       [0]

      # set embedding_dimension
      if embedding_dimension and num_static_categorical_features > 0:
         if len(lowerCAmelCase_     ) != num_static_categorical_features:
            raise ValueError(
                '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''     )
         _A:  List[str]					=       embedding_dimension
      else:
         _A:  Union[str, Any]					=       [min(5_0			,			(cat + 1) // 2     ) for cat in self.cardinality]

      _A:  int					=       num_parallel_samples

      # Transformer architecture configuration
      _A:  Union[str, Any]					=       input_size * len(self.lags_sequence     ) + self._number_of_features
      _A:  Union[str, Any]					=       d_model
      _A:  Optional[Any]					=       encoder_attention_heads
      _A:  Optional[Any]					=       decoder_attention_heads
      _A:  Optional[Any]					=       encoder_ffn_dim
      _A:  Union[str, Any]					=       decoder_ffn_dim
      _A:  Any					=       encoder_layers
      _A:  str					=       decoder_layers

      _A:  List[str]					=       dropout
      _A:  Any					=       attention_dropout
      _A:  Optional[int]					=       activation_dropout
      _A:  List[Any]					=       encoder_layerdrop
      _A:  str					=       decoder_layerdrop

      _A:  int					=       activation_function
      _A:  Tuple					=       init_std

      _A:  Union[str, Any]					=       use_cache

      # Informer
      _A:  Union[str, Any]					=       attention_type
      _A:  str					=       sampling_factor
      _A:  List[str]					=       distil

      super().__init__(is_encoder_decoder=lowerCAmelCase_			,			**lowerCAmelCase_     )


   @property
   def  __magic_name__				(					self     :							List[str]     ):

      """simple docstring"""
      return (
          sum(self.embedding_dimension     )
          + self.num_dynamic_real_features
          + self.num_time_features
          + self.num_static_real_features
          + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
      )



