
from __future__ import annotations

import time

import numpy as np

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




class     _SCREAMING_SNAKE_CASE     :



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  )				->       None:
					lowerCamelCase_						=			claim_vector
					lowerCamelCase_						=			allocated_resources_table
					lowerCamelCase_						=			maximum_claim_table



	def     SCREAMING_SNAKE_CASE_(    self )				->       list[int]:
					return [
					    sum(p_item[i] for p_item in self.__allocated_resources_table )
					    for i in range(len(self.__allocated_resources_table[0] ) )
					]



	def     SCREAMING_SNAKE_CASE_(    self )				->       list[int]:
					return np.array(self.__claim_vector ) - np.array(
					    self.__processes_resource_summation() )



	def     SCREAMING_SNAKE_CASE_(    self )				->       list[list[int]]:
					return [
					    list(np.array(self.__maximum_claim_table[i] ) - np.array(lowercase ) )
					    for i, allocated_resource in enumerate(self.__allocated_resources_table )
					]



	def     SCREAMING_SNAKE_CASE_(    self )				->       dict[int, list[int]]:
					return {self.__need().index(lowercase ): i for i in self.__need()}



	def     SCREAMING_SNAKE_CASE_(    self						,  **lowercase )				->       None:
					lowerCamelCase_						=			self.__need()
					lowerCamelCase_						=			self.__allocated_resources_table
					lowerCamelCase_						=			self.__available_resources()
					lowerCamelCase_						=			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:
									lowerCamelCase_						=			False
									for each_need in need_list:
													lowerCamelCase_						=			True
													for index, need in enumerate(lowercase ):
																	if need > available_resources[index]:
																					lowerCamelCase_						=			False
																					break
													if execution:
																	lowerCamelCase_						=			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:
																									lowerCamelCase_						=			original_need_index
																	print(f'Process {process_number + 1} is executing.' )
																	# remove the process run from stack
																	need_list.remove(lowercase )
																	# update available/freed resources stack
																	lowerCamelCase_						=			np.array(lowercase ) + np.array(
																	    alloc_resources_table[process_number] )
																	print(
																	    "Updated available resource stack for processes: "
																	    + " ".join([str(lowercase ) 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     SCREAMING_SNAKE_CASE_(    self )				->       int:
					print(" " * 9 + "Allocated Resource Table" )
					for item in self.__allocated_resources_table:
									print(
									    f'P{self.__allocated_resources_table.index(lowercase ) + 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(lowercase ) + 1}'
									    + " ".join(f'{it:>8}' for it in item )
									    + "\n" )
					print(
					    "Current Usage by Active Processes: "
					    + " ".join(str(lowercase ) for x in self.__claim_vector ) )
					print(
					    "Initial Available Resources:       "
					    + " ".join(str(lowercase ) for x in self.__available_resources() ) )
					time.sleep(1 )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin







@dataclass
class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  None







class   __lowercase      (_UpperCAmelCase		,   _UpperCAmelCase ):
 _UpperCamelCase			  =  2
 @register_to_config
 def __init__(							self      ,							A_ = 0.02      ,							A_ = 100      ,							A_ = 1.007      ,							A_ = 80      ,							A_ = 0.05      ,							A_ = 50      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					sigma_max

       # setable values
       __lowerCAmelCase					:						int					=					None
       __lowerCAmelCase					:						np.IntTensor					=					None
       __lowerCAmelCase					:						torch.FloatTensor					=					None  # sigma(t_i)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->torch.FloatTensor:



       '''simple docstring'''





       return sample
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					num_inference_steps
       __lowerCAmelCase					:						Dict					=					np.arange(0      ,							self.num_inference_steps					)[::-1].copy()
       __lowerCAmelCase					:						Optional[Any]					=					torch.from_numpy(A_					).to(A_					)
       __lowerCAmelCase					:						Tuple					=					[
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       __lowerCAmelCase					:						Optional[int]					=					torch.tensor(A_      ,							dtype=torch.floataa      ,							device=A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_ = None					)					->Tuple[torch.FloatTensor, float]:



       '''simple docstring'''





       if self.config.s_min <= sigma <= self.config.s_max:
             __lowerCAmelCase					:						List[str]					=					min(self.config.s_churn / self.num_inference_steps      ,							2**0.5 - 1					)
       else:
             __lowerCAmelCase					:						List[str]					=					0

       # sample eps ~ N(0, S_noise^2 * I)
       __lowerCAmelCase					:						int					=					self.config.s_noise * randn_tensor(sample.shape      ,							generator=A_					).to(sample.device					)
       __lowerCAmelCase					:						str					=					sigma + gamma * sigma
       __lowerCAmelCase					:						Any					=					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_ = True      ,							)					->Union[KarrasVeOutput, Tuple]:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					sample_hat + sigma_hat * model_output
       __lowerCAmelCase					:						int					=					(sample_hat - pred_original_sample) / sigma_hat
       __lowerCAmelCase					:						Tuple					=					sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
             return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=A_      ,							derivative=A_      ,							pred_original_sample=A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_ = True      ,							)					->Union[KarrasVeOutput, Tuple]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					sample_prev + sigma_prev * model_output
       __lowerCAmelCase					:						List[Any]					=					(sample_prev - pred_original_sample) / sigma_prev
       __lowerCAmelCase					:						Any					=					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
             return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=A_      ,							derivative=A_      ,							pred_original_sample=A_					)





 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_					)					->Any:



       '''simple docstring'''





       raise NotImplementedError()


