

import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


lowercase_   							=  'scheduler_config.json'





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    1
  _a		     =    2
  _a		     =    3
  _a		     =    4
  _a		     =    5





@dataclass
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    42





class 					__lowerCAmelCase	:
  _a		     =    SCHEDULER_CONFIG_NAME
  _a		     =    ["""dtype"""]
  _a		     =    []
  _a		     =    True




  @classmethod
  def     A__	(							cls				,  lowerCAmelCase = None				,  lowerCAmelCase = None				,  lowerCAmelCase=False				,  **lowerCAmelCase				,  )    ->					Union[str, Any]:
        '''simple docstring'''






        _lowercase			,			_lowercase      			=cls.load_config(
            pretrained_model_name_or_path=lowerCAmelCase				,  subfolder=lowerCAmelCase				,  return_unused_kwargs=lowerCAmelCase				,  **lowerCAmelCase				,  )
        _lowercase			,			_lowercase      			=cls.from_config(lowerCAmelCase				,  return_unused_kwargs=lowerCAmelCase				,  **lowerCAmelCase   )

        if hasattr(lowerCAmelCase				,  'create_state'   ) and getattr(lowerCAmelCase				,  'has_state'				,  lowerCAmelCase   ):
              _lowercase      			=scheduler.create_state()

        if return_unused_kwargs:
              return scheduler, state, unused_kwargs

        return scheduler, state




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase = False				,  **lowerCAmelCase   )    ->					Optional[Any]:
        '''simple docstring'''






        self.save_config(save_directory=lowerCAmelCase				,  push_to_hub=lowerCAmelCase				,  **lowerCAmelCase   )




  @property
  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        return self._get_compatibles()




  @classmethod
  def     A__	(							cls   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=list(set([cls.__name__] + cls._compatibles   )   )
        _lowercase      			=importlib.import_module(__name__.split('.'   )[0]   )
        _lowercase      			=[
            getattr(lowerCAmelCase				,  lowerCAmelCase   ) for c in compatible_classes_str if hasattr(lowerCAmelCase				,  lowerCAmelCase   )
        ]
        return compatible_classes
def 	a (     A__      :  jnp.ndarray ,  A__      :  Tuple[int]	)       -> jnp.ndarray:





      """simple docstring"""



      assert len(A__	) >= x.ndim
      return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(A__	) - x.ndim)	) ,  A__	)
def 	a (     A__      :  int ,  A__      :  str=0.999 ,  A__      :  Optional[int]=jnp.floataa	)       -> jnp.ndarray:





      """simple docstring"""



      def alpha_bar(A__      :  Dict	):
            return math.cos((time_step + 0.008) / 1.008 * math.pi / 2	) ** 2

      _lowercase      			=[]
      for i in range(A__	):
            _lowercase      			=i / num_diffusion_timesteps
            _lowercase      			=(i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar(A__	) / alpha_bar(A__	) ,  A__	)	)
      return jnp.array(A__ ,  dtype=A__	)





@flax.struct.dataclass
class 					__lowerCAmelCase	:
  _a		     =    42
  _a		     =    42
  _a		     =    42




  @classmethod
  def     A__	(							cls				,  lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=scheduler.config

        if config.trained_betas is not None:
              _lowercase      			=jnp.asarray(config.trained_betas				,  dtype=scheduler.dtype   )
        elif config.beta_schedule == "linear":
              _lowercase      			=jnp.linspace(config.beta_start				,  config.beta_end				,  config.num_train_timesteps				,  dtype=scheduler.dtype   )
        elif config.beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              _lowercase      			=(
                  jnp.linspace(
                      config.beta_start**0.5				,  config.beta_end**0.5				,  config.num_train_timesteps				,  dtype=scheduler.dtype   )
                  ** 2
              )
        elif config.beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              _lowercase      			=betas_for_alpha_bar(config.num_train_timesteps				,  dtype=scheduler.dtype   )
        else:
              raise NotImplementedError(
                  F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'''   )

        _lowercase      			=1.0 - betas

        _lowercase      			=jnp.cumprod(lowerCAmelCase				,  axis=0   )

        return cls(
            alphas=lowerCAmelCase				,  betas=lowerCAmelCase				,  alphas_cumprod=lowerCAmelCase				,  )
def 	a (     A__      :  CommonSchedulerState ,  A__      :  jnp.ndarray ,  A__      :  jnp.ndarray ,  A__      :  jnp.ndarray	)       -> Tuple:





      """simple docstring"""



      _lowercase      			=state.alphas_cumprod

      _lowercase      			=alphas_cumprod[timesteps] ** 0.5
      _lowercase      			=sqrt_alpha_prod.flatten()
      _lowercase      			=broadcast_to_shape_from_left(A__ ,  original_samples.shape	)

      _lowercase      			=(1 - alphas_cumprod[timesteps]) ** 0.5
      _lowercase      			=sqrt_one_minus_alpha_prod.flatten()
      _lowercase      			=broadcast_to_shape_from_left(A__ ,  original_samples.shape	)

      return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def 	a (     A__      :  CommonSchedulerState ,  A__      :  jnp.ndarray ,  A__      :  jnp.ndarray ,  A__      :  jnp.ndarray	)       -> List[Any]:





      """simple docstring"""



      _lowercase			,			_lowercase      			=get_sqrt_alpha_prod(A__ ,  A__ ,  A__ ,  A__	)
      _lowercase      			=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples







def 	a (     A__      :  CommonSchedulerState ,  A__      :  jnp.ndarray ,  A__      :  jnp.ndarray ,  A__      :  jnp.ndarray	)       -> Dict:





      """simple docstring"""



      _lowercase			,			_lowercase      			=get_sqrt_alpha_prod(A__ ,  A__ ,  A__ ,  A__	)
      _lowercase      			=sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
      return velocity





from __future__ import annotations

from statistics import mean
def 	a (     A__      :  list[int] ,  A__      :  list[int] ,  A__      :  int	)       -> list[int]:





      """simple docstring"""



      _lowercase      			=[0] * no_of_processes
      _lowercase      			=[0] * no_of_processes

      # Initialize remaining_time to waiting_time.

      for i in range(A__	):
            _lowercase      			=burst_time[i]
      _lowercase      			=[]

      _lowercase      			=0
      _lowercase      			=0

      # When processes are not completed,
      # A process whose arrival time has passed \
      # and has remaining execution time is put into the ready_process.
      # The shortest process in the ready_process, target_process is executed.

      while completed != no_of_processes:
            _lowercase      			=[]
            _lowercase      			=-1

            for i in range(A__	):
                  if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
                        ready_process.append(A__	)

            if len(A__	) > 0:
                  _lowercase      			=ready_process[0]
                  for i in ready_process:
                        if remaining_time[i] < remaining_time[target_process]:
                              _lowercase      			=i
                  total_time += burst_time[target_process]
                  completed += 1
                  _lowercase      			=0
                  _lowercase      			=(
                      total_time - arrival_time[target_process] - burst_time[target_process]
                  )
            else:
                  total_time += 1

      return waiting_time
def 	a (     A__      :  list[int] ,  A__      :  int ,  A__      :  list[int]	)       -> list[int]:





      """simple docstring"""



      _lowercase      			=[0] * no_of_processes
      for i in range(A__	):
            _lowercase      			=burst_time[i] + waiting_time[i]
      return turn_around_time


if __name__ == "__main__":
  print('[TEST CASE 01]')

  lowercase_   							=  4
  lowercase_   							=  [2, 5, 3, 7]
  lowercase_   							=  [0, 0, 0, 0]
  lowercase_   							=  calculate_waitingtime(arrival_time, burst_time, no_of_processes)
  lowercase_   							=  calculate_turnaroundtime(
      burst_time, no_of_processes, waiting_time
  )

  # Printing the Result
  print('PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time')
  for i, process_id in enumerate(list(range(1, 5))):
    print(
        f"{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"
        f"{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"
    )
  print(f"\nAverage waiting time = {mean(waiting_time):.5f}")
  print(f"Average turnaround time = {mean(turn_around_time):.5f}")


