



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     __a			(  SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							1
	SCREAMING_SNAKE_CASE  	=							2
	SCREAMING_SNAKE_CASE  	=							3
	SCREAMING_SNAKE_CASE  	=							4
	SCREAMING_SNAKE_CASE  	=							5






@dataclass
class     __a			(  SCREAMING_SNAKE_CASE					):
	SCREAMING_SNAKE_CASE  	=							42






class     __a			:
	SCREAMING_SNAKE_CASE  	=							SCHEDULER_CONFIG_NAME
	SCREAMING_SNAKE_CASE  	=							["dtype"]
	SCREAMING_SNAKE_CASE  	=							[]
	SCREAMING_SNAKE_CASE  	=							True



	@classmethod
	def 		UpperCamelCase				(    cls				:     Dict					,						snake_case_				:     Dict[str, Any] = None					,						snake_case_				:     Optional[str] = None					,						snake_case_				:     Optional[int]=False					,						**snake_case_				:     Any					,						)->				str:
						__lowerCAmelCase ,						__lowerCAmelCase						 =cls.load_config(
						    pretrained_model_name_or_path=snake_case_					,						subfolder=snake_case_					,						return_unused_kwargs=snake_case_					,						**snake_case_					,						)
						__lowerCAmelCase ,						__lowerCAmelCase						 =cls.from_config(snake_case_					,						return_unused_kwargs=snake_case_					,						**snake_case_)

						if hasattr(snake_case_					,						"""create_state""") and getattr(snake_case_					,						"""has_state"""					,						snake_case_):
											__lowerCAmelCase						 =scheduler.create_state()

						if return_unused_kwargs:
											return scheduler, state, unused_kwargs

						return scheduler, state



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     Union[str, os.PathLike]					,						snake_case_				:     bool = False					,						**snake_case_				:     List[Any])->				Optional[int]:
						self.save_config(save_directory=snake_case_					,						push_to_hub=snake_case_					,						**snake_case_)



	@property
	def 		UpperCamelCase				(    self				:     List[Any])->				List[str]:
						return self._get_compatibles()



	@classmethod
	def 		UpperCamelCase				(    cls				:     Any)->				Optional[int]:
						__lowerCAmelCase						 =list(set([cls.__name__] + cls._compatibles))
						__lowerCAmelCase						 =importlib.import_module(__name__.split(""".""")[0])
						__lowerCAmelCase						 =[
						    getattr(snake_case_					,						snake_case_) for c in compatible_classes_str if hasattr(snake_case_					,						snake_case_)
						]
						return compatible_classes






def        __lowerCAmelCase		(	__lowerCamelCase						:     jnp.ndarray					, __lowerCamelCase						:     Tuple[int]						)	->    jnp.ndarray:
					assert len(__lowerCamelCase						) >= x.ndim
					return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(__lowerCamelCase						) - x.ndim)						)					, __lowerCamelCase						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     int					, __lowerCamelCase						:     int=0.999					, __lowerCamelCase						:     str=jnp.floataa						)	->    jnp.ndarray:

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

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






@flax.struct.dataclass
class     __a			:
	SCREAMING_SNAKE_CASE  	=							42
	SCREAMING_SNAKE_CASE  	=							42
	SCREAMING_SNAKE_CASE  	=							42



	@classmethod
	def 		UpperCamelCase				(    cls				:     str					,						snake_case_				:     Optional[int])->				Union[str, Any]:
						__lowerCAmelCase						 =scheduler.config

						if config.trained_betas is not None:
											__lowerCAmelCase						 =jnp.asarray(config.trained_betas					,						dtype=scheduler.dtype)
						elif config.beta_schedule == "linear":
											__lowerCAmelCase						 =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.
											__lowerCAmelCase						 =(
											    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
											__lowerCAmelCase						 =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__}""")

						__lowerCAmelCase						 =1.0 - betas

						__lowerCAmelCase						 =jnp.cumprod(snake_case_					,						axis=0)

						return cls(
						    alphas=snake_case_					,						betas=snake_case_					,						alphas_cumprod=snake_case_					,						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     CommonSchedulerState					, __lowerCamelCase						:     jnp.ndarray					, __lowerCamelCase						:     jnp.ndarray					, __lowerCamelCase						:     jnp.ndarray						)	->    Any:
					__lowerCAmelCase						 =state.alphas_cumprod

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

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

					return sqrt_alpha_prod, sqrt_one_minus_alpha_prod






def        __lowerCAmelCase		(	__lowerCamelCase						:     CommonSchedulerState					, __lowerCamelCase						:     jnp.ndarray					, __lowerCamelCase						:     jnp.ndarray					, __lowerCamelCase						:     jnp.ndarray						)	->    List[str]:
					__lowerCAmelCase ,						__lowerCAmelCase						 =get_sqrt_alpha_prod(__lowerCamelCase					, __lowerCamelCase					, __lowerCamelCase					, __lowerCamelCase						)
					__lowerCAmelCase						 =sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
					return noisy_samples






def        __lowerCAmelCase		(	__lowerCamelCase						:     CommonSchedulerState					, __lowerCamelCase						:     jnp.ndarray					, __lowerCamelCase						:     jnp.ndarray					, __lowerCamelCase						:     jnp.ndarray						)	->    int:
					__lowerCAmelCase ,						__lowerCAmelCase						 =get_sqrt_alpha_prod(__lowerCamelCase					, __lowerCamelCase					, __lowerCamelCase					, __lowerCamelCase						)
					__lowerCAmelCase						 =sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
					return velocity









from __future__ import annotations

import time

import numpy as np

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






class     __a			:



	def __init__(    self				:     Tuple					,						snake_case_				:     list[int]					,						snake_case_				:     list[list[int]]					,						snake_case_				:     list[list[int]]					,						)->				None:
						__lowerCAmelCase						 =claim_vector
						__lowerCAmelCase						 =allocated_resources_table
						__lowerCAmelCase						 =maximum_claim_table



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



	def 		UpperCamelCase				(    self				:     Optional[Any])->				list[list[int]]:
						return [
						    list(np.array(self.__maximum_claim_table[i]) - np.array(snake_case_))
						    for i, allocated_resource in enumerate(self.__allocated_resources_table)
						]



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



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						**snake_case_				:     Optional[Any])->				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(snake_case_):
																					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(snake_case_)
																					# update available/freed resources stack
																					__lowerCAmelCase						 =np.array(snake_case_) + np.array(
																					    alloc_resources_table[process_number])
																					print(
																					    """Updated available resource stack for processes: """
																					    + """ """.join([str(snake_case_) 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				:     List[str])->				Optional[Any]:
						print(""" """ * 9 + """Allocated Resource Table""")
						for item in self.__allocated_resources_table:
											print(
											    F"""P{self.__allocated_resources_table.index(snake_case_) + 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(snake_case_) + 1}"""
											    + """ """.join(F"""{it:>8}""" for it in item)
											    + """\n""")
						print(
						    """Current Usage by Active Processes: """
						    + """ """.join(str(snake_case_) for x in self.__claim_vector))
						print(
						    """Initial Available Resources:       """
						    + """ """.join(str(snake_case_) for x in self.__available_resources()))
						time.sleep(1)


if __name__ == "__main__":
				import doctest

				doctest.testmod()




