


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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin




@flax.struct.dataclass
class 					snake_case__							:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Optional[int]											=      None
				SCREAMING_SNAKE_CASE_ :							Any											=      None
				SCREAMING_SNAKE_CASE_ :							Optional[Any]											=      None  # sigma(t_i)





				@classmethod
				def 							__UpperCAmelCase							(   cls       :  Tuple							)      ->  Tuple:
							return cls()




@dataclass
class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							str											=      42
				SCREAMING_SNAKE_CASE_ :							Union[str, Any]											=      42
				SCREAMING_SNAKE_CASE_ :							int											=      42




class 					snake_case__							(_UpperCamelCase					, _UpperCamelCase     ):
				"""simple docstring"""



				@property
				def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Tuple:
							return True





				@register_to_config
				def __init__(   self       :  Optional[int]      ,   __lowerCamelCase       :  Tuple = 0.02      ,   __lowerCamelCase       :  Dict = 1_00      ,   __lowerCamelCase       :  Optional[int] = 1.007      ,   __lowerCamelCase       :  int = 80      ,   __lowerCamelCase       :  Any = 0.05      ,   __lowerCamelCase       :  int = 50      ,   )      ->  Union[str, Any]:
							pass





				def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Dict:
							return KarrasVeSchedulerState.create()





				def 							__UpperCAmelCase							(   self       :  Tuple      ,   __lowerCamelCase       :  Optional[int]      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  List[str] = ()							)      ->  Optional[Any]:
							a					=    jnp.arange(0      ,   _lowerCAmelCase							)[::-1].copy()
							a					=    [
							    (
							        self.config.sigma_max**2
							        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
							    )
							    for i in timesteps
							]

							return state.replace(
							    num_inference_steps=_lowerCAmelCase      ,   schedule=jnp.array(_lowerCAmelCase      ,   dtype=jnp.floataa							)      ,   timesteps=_lowerCAmelCase      ,   )





				def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  Union[str, Any]      ,   )      ->  Optional[Any]:
							if self.config.s_min <= sigma <= self.config.s_max:
										a					=    min(self.config.s_churn / state.num_inference_steps      ,   2**0.5 - 1							)
							else:
										a					=    0

							# sample eps ~ N(0, S_noise^2 * I)
							a					=    random.split(_lowerCAmelCase      ,   num=1							)
							a					=    self.config.s_noise * random.normal(key=_lowerCAmelCase      ,   shape=sample.shape							)
							a					=    sigma + gamma * sigma
							a					=    sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

							return sample_hat, sigma_hat





				def 							__UpperCAmelCase							(   self       :  Optional[int]      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Union[str, Any] = True      ,   )      ->  Any:
							a					=    sample_hat + sigma_hat * model_output
							a					=    (sample_hat - pred_original_sample) / sigma_hat
							a					=    sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
										return (sample_prev, derivative, state)

							return FlaxKarrasVeOutput(prev_sample=_lowerCAmelCase      ,   derivative=_lowerCAmelCase      ,   state=_lowerCAmelCase							)





				def 							__UpperCAmelCase							(   self       :  Optional[int]      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  Optional[Any] = True      ,   )      ->  Dict:
							a					=    sample_prev + sigma_prev * model_output
							a					=    (sample_prev - pred_original_sample) / sigma_prev
							a					=    sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

							if not return_dict:
										return (sample_prev, derivative, state)

							return FlaxKarrasVeOutput(prev_sample=_lowerCAmelCase      ,   derivative=_lowerCAmelCase      ,   state=_lowerCAmelCase							)





				def 							__UpperCAmelCase							(   self       :  List[Any]      ,   __lowerCamelCase       :  Tuple      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Optional[int]      ,   __lowerCamelCase       :  Tuple							)      ->  Optional[int]:
							raise NotImplementedError()












from __future__ import annotations

import time

import numpy as np

__lowerCAmelCase	:       List[str]     =	[8, 5, 9, 7]
__lowerCAmelCase	:       str     =	[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__lowerCAmelCase	:       Optional[Any]     =	[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]




class 					snake_case__							:
				"""simple docstring"""



				def __init__(   self       :  Any      ,   __lowerCamelCase       :  list[int]      ,   __lowerCamelCase       :  list[list[int]]      ,   __lowerCamelCase       :  list[list[int]]      ,   )      ->  None:
							a					=    claim_vector
							a					=    allocated_resources_table
							a					=    maximum_claim_table





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





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





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





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


if __name__ == "__main__":
		import doctest

		doctest.testmod()






