
class 			__A:




				def __init__(     self		,     _snake_case		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    name
								__a										=    val




				def __str__(     self				)    ->      List[Any]:


								'''simple docstring'''




								return F"""{self.__class__.__name__}({self.name}, {self.val})"""




				def __lt__(     self		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								return self.val < other.val







class 			__A:




				def __init__(     self		,     _snake_case				)    ->      List[str]:


								'''simple docstring'''




								__a										=    {}
								__a										=    {}
								__a										=    self.build_heap(_snake_case				)




				def __getitem__(     self		,     _snake_case				)    ->      Any:


								'''simple docstring'''




								return self.get_value(_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Tuple:


								'''simple docstring'''




								return (idx - 1) // 2




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      List[str]:


								'''simple docstring'''




								return idx * 2 + 1




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Tuple:


								'''simple docstring'''




								return idx * 2 + 2




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      str:


								'''simple docstring'''




								return self.heap_dict[key]




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Dict:


								'''simple docstring'''




								__a										=    len(_snake_case				) - 1
								__a										=    self.get_parent_idx(_snake_case				)

								for idx, i in enumerate(_snake_case				):
												__a										=    idx
												__a										=    i.val

								for i in range(_snake_case		,     -1		,     -1				):
												self.sift_down(_snake_case		,     _snake_case				)
								return array




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      List[Any]:


								'''simple docstring'''




								while True:
												__a										=    self.get_left_child_idx(_snake_case				)  # noqa: E741
												__a										=    self.get_right_child_idx(_snake_case				)

												__a										=    idx
												if l < len(_snake_case				) and array[l] < array[idx]:
																__a										=    l
												if r < len(_snake_case				) and array[r] < array[smallest]:
																__a										=    r

												if smallest != idx:
																__a							,     __a										=    array[smallest], array[idx]
																(
																    (
																    __a
																)							,     (
																    __a
																)							,     
																)										=    (
																    self.idx_of_element[array[smallest]],
																    self.idx_of_element[array[idx]],
																)
																__a										=    smallest
												else:
																break




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    self.get_parent_idx(_snake_case				)
								while p >= 0 and self.heap[p] > self.heap[idx]:
												__a							,     __a										=    self.heap[idx], self.heap[p]
												__a							,     __a										=    (
												    self.idx_of_element[self.heap[idx]],
												    self.idx_of_element[self.heap[p]],
												)
												__a										=    p
												__a										=    self.get_parent_idx(_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								return self.heap[0]




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								__a							,     __a										=    self.heap[-1], self.heap[0]
								__a							,     __a										=    (
								    self.idx_of_element[self.heap[-1]],
								    self.idx_of_element[self.heap[0]],
								)

								__a										=    self.heap.pop()
								del self.idx_of_element[x]
								self.sift_down(0		,     self.heap				)
								return x




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      str:


								'''simple docstring'''




								self.heap.append(_snake_case				)
								__a										=    len(self.heap				) - 1
								__a										=    node.val
								self.sift_up(len(self.heap				) - 1				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								return len(self.heap				) == 0




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								assert (
								    self.heap[self.idx_of_element[node]].val > new_value
								), "newValue must be less that current value"
								__a										=    new_value
								__a										=    new_value
								self.sift_up(self.idx_of_element[node]				)



A     :	Union[str, Any]    = Node('R', -1)
A     :	Union[str, Any]    = Node('B', 6)
A     :	List[Any]    = Node('A', 3)
A     :	List[Any]    = Node('X', 1)
A     :	Union[str, Any]    = Node('E', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
A     :	Union[str, Any]    = MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
					print(i)

print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -1_7)

# After
for i in my_min_heap.heap:
					print(i)

if __name__ == "__main__":
					import doctest

					doctest.testmod()


"""simple docstring"""





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


__UpperCamelCase  :      Optional[Any]							      =		'''scheduler_config.json'''







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       1
					lowercase__				 =       2
					lowercase__				 =       3
					lowercase__				 =       4
					lowercase__				 =       5







@dataclass
class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       42







class 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



					lowercase__				 =       SCHEDULER_CONFIG_NAME
					lowercase__				 =       ["dtype"]
					lowercase__				 =       []
					lowercase__				 =       True







					@classmethod
					def        __lowerCAmelCase (       cls			:				List[Any]   ,lowercase_			:				Dict[str, Any] = None   ,lowercase_			:				Optional[str] = None   ,lowercase_			:				Optional[int]=False   ,**lowercase_			:				Any   ,):
						lowerCAmelCase__				,lowerCAmelCase__	:						Dict  				=	cls.load_config(
						    pretrained_model_name_or_path=lowercase_   ,subfolder=lowercase_   ,return_unused_kwargs=lowercase_   ,**lowercase_   ,)
						lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, Any]  				=	cls.from_config(lowercase_   ,return_unused_kwargs=lowercase_   ,**lowercase_		)

						if hasattr(lowercase_   ,'''create_state'''		) and getattr(lowercase_   ,'''has_state'''   ,lowercase_		):
							lowerCAmelCase__	:						List[Any]  				=	scheduler.create_state()

						if return_unused_kwargs:
							return scheduler, state, unused_kwargs

						return scheduler, state







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				Union[str, os.PathLike]   ,lowercase_			:				bool = False   ,**lowercase_			:				str		):
						self.save_config(save_directory=lowercase_   ,push_to_hub=lowercase_   ,**lowercase_		)







					@property
					def        __lowerCAmelCase (       self			:				List[str]		):
						return self._get_compatibles()







					@classmethod
					def        __lowerCAmelCase (       cls			:				List[Any]		):
						lowerCAmelCase__	:						Tuple  				=	list(set([cls.__name__] + cls._compatibles		)		)
						lowerCAmelCase__	:						Tuple  				=	importlib.import_module(__name__.split('''.'''		)[0]		)
						lowerCAmelCase__	:						Union[str, Any]  				=	[
						    getattr(lowercase_   ,lowercase_		) for c in compatible_classes_str if hasattr(lowercase_   ,lowercase_		)
						]
						return compatible_classes

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	assert len(A_ ) >= x.ndim
	return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(A_ ) - x.ndim) )						,     A_ )

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=0.999						,     A_=jnp.floataa ):

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

	lowerCAmelCase__	:						Optional[Any]  				=	[]
	for i in range(A_ ):
		lowerCAmelCase__	:						str  				=	i / num_diffusion_timesteps
		lowerCAmelCase__	:						Union[str, Any]  				=	(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 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



					lowercase__				 =       42
					lowercase__				 =       42
					lowercase__				 =       42







					@classmethod
					def        __lowerCAmelCase (       cls			:				Union[str, Any]   ,lowercase_			:				List[Any]		):
						lowerCAmelCase__	:						Optional[int]  				=	scheduler.config

						if config.trained_betas is not None:
							lowerCAmelCase__	:						Any  				=	jnp.asarray(config.trained_betas   ,dtype=scheduler.dtype		)
						elif config.beta_schedule == "linear":
							lowerCAmelCase__	:						Union[str, Any]  				=	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__	:						int  				=	(
							    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__	:						List[Any]  				=	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__	:						str  				=	1.0 - betas

						lowerCAmelCase__	:						Union[str, Any]  				=	jnp.cumprod(lowercase_   ,axis=0		)

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

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Any  				=	state.alphas_cumprod

	lowerCAmelCase__	:						Optional[Any]  				=	alphas_cumprod[timesteps] ** 0.5
	lowerCAmelCase__	:						Tuple  				=	sqrt_alpha_prod.flatten()
	lowerCAmelCase__	:						str  				=	broadcast_to_shape_from_left(A_						,     original_samples.shape )

	lowerCAmelCase__	:						Optional[Any]  				=	(1 - alphas_cumprod[timesteps]) ** 0.5
	lowerCAmelCase__	:						Optional[Any]  				=	sqrt_one_minus_alpha_prod.flatten()
	lowerCAmelCase__	:						Optional[int]  				=	broadcast_to_shape_from_left(A_						,     original_samples.shape )

	return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	get_sqrt_alpha_prod(A_						,     A_						,     A_						,     A_ )
	lowerCAmelCase__	:						str  				=	sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
	return noisy_samples





def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	get_sqrt_alpha_prod(A_						,     A_						,     A_						,     A_ )
	lowerCAmelCase__	:						Union[str, Any]  				=	sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
	return velocity




