


'''simple docstring'''

import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
     from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
     from tensorflow.keras.optimizers import Adam





class   snake_case					(					tf.keras.optimizers.schedules.LearningRateSchedule    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase = 1.0    ,						UpperCamelCase = None    ,						):







   """simple docstring"""
   super().__init__()
   lowerCamelCase_  				=						initial_learning_rate
   lowerCamelCase_  				=						warmup_steps
   lowerCamelCase_  				=						power
   lowerCamelCase_  				=						decay_schedule_fn
   lowerCamelCase_  				=						name






  def __call__(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   with tf.name_scope(self.name or "WarmUp"  ) as name:
    # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
    # learning rate will be `global_step/num_warmup_steps * init_lr`.
    lowerCamelCase_  				=						tf.cast(UpperCamelCase    ,						tf.floataa  )
    lowerCamelCase_  				=						tf.cast(self.warmup_steps    ,						tf.floataa  )
    lowerCamelCase_  				=						global_step_float / warmup_steps_float
    lowerCamelCase_  				=						self.initial_learning_rate * tf.math.pow(UpperCamelCase    ,						self.power  )
    return tf.cond(
        global_step_float < warmup_steps_float    ,						lambda: warmup_learning_rate    ,						lambda: self.decay_schedule_fn(step - self.warmup_steps  )    ,						name=UpperCamelCase    ,						)







  def 						snake_case	(					self  ):







   """simple docstring"""
   return {
       "initial_learning_rate": self.initial_learning_rate,
       "decay_schedule_fn": self.decay_schedule_fn,
       "warmup_steps": self.warmup_steps,
       "power": self.power,
       "name": self.name,
   }





def __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		float = 0.0      ,       UpperCAmelCase_		:		float = 0.9      ,       UpperCAmelCase_		:		float = 0.999      ,       UpperCAmelCase_		:		float = 1E-8      ,       UpperCAmelCase_		:		Optional[float] = None      ,       UpperCAmelCase_		:		Optional[float] = None      ,       UpperCAmelCase_		:		float = 0.0      ,       UpperCAmelCase_		:		float = 1.0      ,       UpperCAmelCase_		:		Optional[List[str]] = None      ,       ):
 lowerCamelCase_  				=						tf.keras.optimizers.schedules.PolynomialDecay(
     initial_learning_rate=UpperCAmelCase_      ,       decay_steps=num_train_steps - num_warmup_steps      ,       end_learning_rate=init_lr * min_lr_ratio      ,       power=UpperCAmelCase_      ,       )
 if num_warmup_steps:
  lowerCamelCase_  				=						WarmUp(
      initial_learning_rate=UpperCAmelCase_      ,       decay_schedule_fn=UpperCAmelCase_      ,       warmup_steps=UpperCAmelCase_      ,       )
 if weight_decay_rate > 0.0:
  lowerCamelCase_  				=						AdamWeightDecay(
      learning_rate=UpperCAmelCase_      ,       weight_decay_rate=UpperCAmelCase_      ,       beta_a=UpperCAmelCase_      ,       beta_a=UpperCAmelCase_      ,       epsilon=UpperCAmelCase_      ,       clipnorm=UpperCAmelCase_      ,       global_clipnorm=UpperCAmelCase_      ,       exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]      ,       include_in_weight_decay=UpperCAmelCase_      ,       )
 else:
  lowerCamelCase_  				=						tf.keras.optimizers.Adam(
      learning_rate=UpperCAmelCase_      ,       beta_a=UpperCAmelCase_      ,       beta_a=UpperCAmelCase_      ,       epsilon=UpperCAmelCase_      ,       clipnorm=UpperCAmelCase_      ,       global_clipnorm=UpperCAmelCase_      ,       )
 # We return the optimizer and the LR scheduler in order to better track the
 # evolution of the LR independently of the optimizer.
 return optimizer, lr_schedule





class   snake_case					(					lowercase    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase = 0.001    ,						UpperCamelCase = 0.9    ,						UpperCamelCase = 0.999    ,						UpperCamelCase = 1e-7    ,						UpperCamelCase = False    ,						UpperCamelCase = 0.0    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase = "AdamWeightDecay"    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						**UpperCamelCase  )
   lowerCamelCase_  				=						weight_decay_rate
   lowerCamelCase_  				=						include_in_weight_decay
   lowerCamelCase_  				=						exclude_from_weight_decay






  @classmethod
  def 						snake_case	(					cls    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						{"WarmUp": WarmUp}
   return super(UpperCamelCase    ,						cls  ).from_config(UpperCamelCase    ,						custom_objects=UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   super(UpperCamelCase    ,						self  )._prepare_local(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  )
   lowerCamelCase_  				=						tf.constant(
       self.weight_decay_rate    ,						name="adam_weight_decay_rate"  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						self._do_use_weight_decay(var.name  )
   if do_decay:
    return var.assign_sub(
        learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["weight_decay_rate"]    ,						use_locking=self._use_locking    ,						)
   return tf.no_op()






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase=None    ,						**UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_				,lowerCamelCase_  				=						list(zip(*UpperCamelCase  )  )
   return super(UpperCamelCase    ,						self  ).apply_gradients(zip(UpperCamelCase    ,						UpperCamelCase  )    ,						name=UpperCamelCase    ,						**UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   if apply_state is None:
    return self._decayed_lr_t[var_dtype], {}

   lowerCamelCase_  				=						apply_state or {}
   lowerCamelCase_  				=						apply_state.get((var_device, var_dtype)  )
   if coefficients is None:
    lowerCamelCase_  				=						self._fallback_apply_state(UpperCamelCase    ,						UpperCamelCase  )
    lowerCamelCase_  				=						coefficients

   return coefficients["lr_t"], {"apply_state": apply_state}






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase=None  ):







   """simple docstring"""
   lowerCamelCase_				,lowerCamelCase_  				=						self._get_lr(var.device    ,						var.dtype.base_dtype    ,						UpperCamelCase  )
   lowerCamelCase_  				=						self._decay_weights_op(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  )
   with tf.control_dependencies([decay]  ):
    return super(UpperCamelCase    ,						self  )._resource_apply_dense(UpperCamelCase    ,						UpperCamelCase    ,						**UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase=None  ):







   """simple docstring"""
   lowerCamelCase_				,lowerCamelCase_  				=						self._get_lr(var.device    ,						var.dtype.base_dtype    ,						UpperCamelCase  )
   lowerCamelCase_  				=						self._decay_weights_op(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  )
   with tf.control_dependencies([decay]  ):
    return super(UpperCamelCase    ,						self  )._resource_apply_sparse(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						**UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						super().get_config()
   config.update({"weight_decay_rate": self.weight_decay_rate}  )
   return config







  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   if self.weight_decay_rate == 0:
    return False

   if self._include_in_weight_decay:
    for r in self._include_in_weight_decay:
     if re.search(UpperCamelCase    ,						UpperCamelCase  ) is not None:
      return True

   if self._exclude_from_weight_decay:
    for r in self._exclude_from_weight_decay:
     if re.search(UpperCamelCase    ,						UpperCamelCase  ) is not None:
      return False
   return True





class   snake_case					(					lowercase    ):





  """simple docstring"""




  def __init__(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						[]
   lowerCamelCase_  				=						None






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   if self._accum_steps is None:
    lowerCamelCase_  				=						tf.Variable(
        tf.constant(0    ,						dtype=tf.intaa  )    ,						trainable=UpperCamelCase    ,						synchronization=tf.VariableSynchronization.ON_READ    ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA    ,						)

   return self._accum_steps.value()






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   if not self._gradients:
    raise ValueError("The accumulator should be called first to initialize the gradients"  )
   return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]






  def __call__(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   if not self._gradients:
    lowerCamelCase_  				=						self.step  # Create the step variable.
    self._gradients.extend(
        [
            tf.Variable(
                tf.zeros_like(UpperCamelCase  )    ,						trainable=UpperCamelCase    ,						synchronization=tf.VariableSynchronization.ON_READ    ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA    ,						)
            if gradient is not None
            else gradient
            for gradient in gradients
        ]  )
   if len(UpperCamelCase  ) != len(self._gradients  ):
    raise ValueError(f'''Expected {len(self._gradients  )} gradients, but got {len(UpperCamelCase  )}'''  )

   for accum_gradient, gradient in zip(self._gradients    ,						UpperCamelCase  ):
    if accum_gradient is not None and gradient is not None:
     accum_gradient.assign_add(UpperCamelCase  )

   self._accum_steps.assign_add(1  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   if not self._gradients:
    return
   self._accum_steps.assign(0  )
   for gradient in self._gradients:
    if gradient is not None:
     gradient.assign(tf.zeros_like(UpperCamelCase  )  )







'''simple docstring'''

from __future__ import annotations

import pandas as pd





def __snake_case      (   UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		int	):
 lowerCamelCase_  				=						[0] * no_of_processes
 lowerCamelCase_  				=						[0] * no_of_processes
 # Copy the burst time into remaining_time[]
 for i in range(UpperCAmelCase_	):
  lowerCamelCase_  				=						burst_time[i]

 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						999999999
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						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:
     lowerCamelCase_  				=						remaining_time[j]
     lowerCamelCase_  				=						j
     lowerCamelCase_  				=						True

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

  lowerCamelCase_  				=						remaining_time[short]
  if minm == 0:
   lowerCamelCase_  				=						999999999

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

   # Find finish time of current process
   lowerCamelCase_  				=						increment_time + 1

   # Calculate waiting time
   lowerCamelCase_  				=						finish_time - arrival_time[short]
   lowerCamelCase_  				=						finar - burst_time[short]

   if waiting_time[short] < 0:
    lowerCamelCase_  				=						0

        # Increment time
  increment_time += 1
 return waiting_time





def __snake_case      (   UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		list[int]	):
 lowerCamelCase_  				=						[0] * no_of_processes
 for i in range(UpperCAmelCase_	):
  lowerCamelCase_  				=						burst_time[i] + waiting_time[i]
 return turn_around_time





def __snake_case      (   UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		int	):
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						0
 for i in range(UpperCAmelCase_	):
  lowerCamelCase_  				=						total_waiting_time + waiting_time[i]
  lowerCamelCase_  				=						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""")
     a_						:       Dict      			=				int(input())
     a_						:       Any      			=				[0] * no_of_processes
     a_						:       Optional[int]      			=				[0] * no_of_processes
     a_						:       Tuple      			=				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))
          a_			,     a_						:       str      			=				map(int, input().split())

     a_						:       List[Any]      			=				calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

     calculate_average_times(waiting_time, turn_around_time, no_of_processes)

     a_						:       Optional[int]      			=				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)



