
'''simple docstring'''







from __future__ import annotations

import time

import numpy as np

__lowerCAmelCase         =    [8, 5, 9, 7]
__lowerCAmelCase         =    [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__lowerCAmelCase         =    [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]







class UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     Tuple							,_a      :     list[int]							,_a      :     list[list[int]]							,_a      :     list[list[int]]							,):


     '''simple docstring'''






     _a				:	List[str]			=   claim_vector
     _a				:	Dict			=   allocated_resources_table
     _a				:	int			=   maximum_claim_table


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     return [
         sum(p_item[i] for p_item in self.__allocated_resources_table       )
         for i in range(len(self.__allocated_resources_table[0]       )       )
     ]


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     return np.array(self.__claim_vector       ) - np.array(
         self.__processes_resource_summation()       )


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     return [
         list(np.array(self.__maximum_claim_table[i]       ) - np.array(_a       )       )
         for i, allocated_resource in enumerate(self.__allocated_resources_table       )
     ]


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     return {self.__need().index(_a       ): i for i in self.__need()}


  def   __lowercase		(		self      :     Optional[Any]							,**_a      :     Any       ):


     '''simple docstring'''






     _a				:	Tuple			=   self.__need()
     _a				:	Union[str, Any]			=   self.__allocated_resources_table
     _a				:	Tuple			=   self.__available_resources()
     _a				:	List[Any]			=   self.__need_index_manager()
     for kw, val in kwargs.items():
        if kw and val is True:
           self.__pretty_data()
     print('_' * 50 + '\n'       )
     while need_list:
        _a				:	List[Any]			=   False
        for each_need in need_list:
           _a				:	Any			=   True
           for index, need in enumerate(_a       ):
              if need > available_resources[index]:
                 _a				:	Optional[int]			=   False
                 break
           if execution:
              _a				:	List[Any]			=   True
              # get the original index of the process from ind_ctrl db
              for original_need_index, need_clone in need_index_manager.items():
                 if each_need == need_clone:
                    _a				:	Union[str, Any]			=   original_need_index
              print(F"""Process {process_number + 1} is executing."""       )
              # remove the process run from stack
              need_list.remove(_a       )
              # update available/freed resources stack
              _a				:	Union[str, Any]			=   np.array(_a       ) + np.array(
                  alloc_resources_table[process_number]       )
              print(
                  'Updated available resource stack for processes: '
                  + ' '.join([str(_a       ) for x in available_resources]       )       )
              break
        if safe:
           print('The process is in a safe state.\n'       )
        else:
           print('System in unsafe state. Aborting...\n'       )
           break


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     print(' ' * 9 + 'Allocated Resource Table'       )
     for item in self.__allocated_resources_table:
        print(
            F"""P{self.__allocated_resources_table.index(_a       ) + 1}"""
            + ' '.join(F"""{it:>8}""" for it in item       )
            + '\n'       )
     print(' ' * 9 + 'System Resource Table'       )
     for item in self.__maximum_claim_table:
        print(
            F"""P{self.__maximum_claim_table.index(_a       ) + 1}"""
            + ' '.join(F"""{it:>8}""" for it in item       )
            + '\n'       )
     print(
         'Current Usage by Active Processes: '
         + ' '.join(str(_a       ) for x in self.__claim_vector       )       )
     print(
         'Initial Available Resources:       '
         + ' '.join(str(_a       ) for x in self.__available_resources()       )       )
     time.sleep(1       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()





'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				UNetaDModel
  __UpperCAmelCase				:				KarrasVeScheduler


  def __init__(		self      :     Union[str, Any]							,_a      :     UNetaDModel							,_a      :     KarrasVeScheduler       ):


     '''simple docstring'''






     super().__init__()
     self.register_modules(unet=_a							,scheduler=_a       )


  @torch.no_grad()
  def __call__(		self      :     List[Any]							,_a      :     int = 1							,_a      :     int = 50							,_a      :     Optional[Union[torch.Generator, List[torch.Generator]]] = None							,_a      :     Optional[str] = "pil"							,_a      :     bool = True							,**_a      :     List[Any]							,):


     '''simple docstring'''






     _a				:	Any			=   self.unet.config.sample_size
     _a				:	Optional[int]			=   (batch_size, 3, img_size, img_size)

     _a				:	Dict			=   self.unet

     # sample x_0 ~ N(0, sigma_0^2 * I)
     _a				:	Dict			=   randn_tensor(_a							,generator=_a							,device=self.device       ) * self.scheduler.init_noise_sigma

     self.scheduler.set_timesteps(_a       )

     for t in self.progress_bar(self.scheduler.timesteps       ):
        # here sigma_t == t_i from the paper
        _a				:	Optional[int]			=   self.scheduler.schedule[t]
        _a				:	List[str]			=   self.scheduler.schedule[t - 1] if t > 0 else 0

        # 1. Select temporarily increased noise level sigma_hat
        # 2. Add new noise to move from sample_i to sample_hat
        _a,  _a				:	List[Any]			=   self.scheduler.add_noise_to_input(_a							,_a							,generator=_a       )

        # 3. Predict the noise residual given the noise magnitude `sigma_hat`
        # The model inputs and output are adjusted by following eq. (213) in [1].
        _a				:	Optional[int]			=   (sigma_hat / 2) * model((sample_hat + 1) / 2							,sigma_hat / 2       ).sample

        # 4. Evaluate dx/dt at sigma_hat
        # 5. Take Euler step from sigma to sigma_prev
        _a				:	Tuple			=   self.scheduler.step(_a							,_a							,_a							,_a       )

        if sigma_prev != 0:
           # 6. Apply 2nd order correction
           # The model inputs and output are adjusted by following eq. (213) in [1].
           _a				:	Optional[int]			=   (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							,sigma_prev / 2       ).sample
           _a				:	Optional[Any]			=   self.scheduler.step_correct(
               _a							,_a							,_a							,_a							,step_output.prev_sample							,step_output['derivative']							,)
        _a				:	Dict			=   step_output.prev_sample

     _a				:	Tuple			=   (sample / 2 + 0.5).clamp(0							,1       )
     _a				:	Optional[Any]			=   sample.cpu().permute(0							,2							,3							,1       ).numpy()
     if output_type == "pil":
        _a				:	List[str]			=   self.numpy_to_pil(_a       )

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_a       )



