





'''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


A             =			'scheduler_config.json'







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    1
					_SCREAMING_SNAKE_CASE							   =    2
					_SCREAMING_SNAKE_CASE							   =    3
					_SCREAMING_SNAKE_CASE							   =    4
					_SCREAMING_SNAKE_CASE							   =    5







@dataclass
class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    42







class 			lowerCamelCase					:
					_SCREAMING_SNAKE_CASE							   =    SCHEDULER_CONFIG_NAME
					_SCREAMING_SNAKE_CASE							   =    ["dtype"]
					_SCREAMING_SNAKE_CASE							   =    []
					_SCREAMING_SNAKE_CASE							   =    True


					@classmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  Dict       ,     __snake_case	:  Dict[str, Any] = None       ,     __snake_case	:  Optional[str] = None       ,     __snake_case	:  List[str]=False       ,     **__snake_case	:  str       ,     ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		cls.load_config(
											    pretrained_model_name_or_path=__snake_case       ,     subfolder=__snake_case       ,     return_unused_kwargs=__snake_case       ,     **__snake_case       ,     )
											_snake_case:   Optional[int]  					=		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       ):
																	_snake_case:   List[str]  					=		scheduler.create_state()

											if return_unused_kwargs:
																	return scheduler, state, unused_kwargs

											return scheduler, state


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  Union[str, os.PathLike]       ,     __snake_case	:  bool = False       ,     **__snake_case	:  Tuple       ):





											'''simple docstring'''





											self.save_config(save_directory=__snake_case       ,     push_to_hub=__snake_case       ,     **__snake_case       )


					@property
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											return self._get_compatibles()




					@classmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Tuple  					=		list(set([cls.__name__] + cls._compatibles       )       )
											_snake_case:   str  					=		importlib.import_module(__name__.split('.'       )[0]       )
											_snake_case:   str  					=		[
											    getattr(__snake_case       ,     __snake_case       ) for c in compatible_classes_str if hasattr(__snake_case       ,     __snake_case       )
											]
											return compatible_classes





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





def       lowercase_      (		lowercase__				,  lowercase__=0.9_9_9				,  lowercase__=jnp.floataa						)   ->jnp.ndarray:

						def alpha_bar(lowercase__						):
												return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2						) ** 2

						_snake_case:   Any  					=		[]
						for i in range(lowercase__						):
												_snake_case:   List[str]  					=		i / num_diffusion_timesteps
												_snake_case:   Optional[Any]  					=		(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar(lowercase__						) / alpha_bar(lowercase__						)				,  lowercase__						)						)
						return jnp.array(lowercase__				,  dtype=lowercase__						)







@flax.struct.dataclass
class 			lowerCamelCase					:
					_SCREAMING_SNAKE_CASE							   =    42
					_SCREAMING_SNAKE_CASE							   =    42
					_SCREAMING_SNAKE_CASE							   =    42


					@classmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  List[str]       ,     __snake_case	:  str       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		scheduler.config

											if config.trained_betas is not None:
																	_snake_case:   Optional[int]  					=		jnp.asarray(config.trained_betas       ,     dtype=scheduler.dtype       )
											elif config.beta_schedule == "linear":
																	_snake_case:   int  					=		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.
																	_snake_case:   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
																	_snake_case:   Tuple  					=		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__}'''       )

											_snake_case:   str  					=		1.0 - betas

											_snake_case:   List[Any]  					=		jnp.cumprod(__snake_case       ,     axis=0       )

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





def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__				,  lowercase__						)   ->Tuple:
						_snake_case:   List[Any]  					=		state.alphas_cumprod

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

						_snake_case:   str  					=		(1 - alphas_cumprod[timesteps]) ** 0.5
						_snake_case:   List[Any]  					=		sqrt_one_minus_alpha_prod.flatten()
						_snake_case:   List[str]  					=		broadcast_to_shape_from_left(lowercase__				,  original_samples.shape						)

						return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__				,  lowercase__						)   ->Tuple:
						_snake_case:   Dict  					=		get_sqrt_alpha_prod(lowercase__				,  lowercase__				,  lowercase__				,  lowercase__						)
						_snake_case:   Optional[Any]  					=		sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
						return noisy_samples





def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__				,  lowercase__						)   ->Optional[int]:
						_snake_case:   str  					=		get_sqrt_alpha_prod(lowercase__				,  lowercase__				,  lowercase__				,  lowercase__						)
						_snake_case:   Optional[int]  					=		sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
						return velocity












'''simple docstring'''






from __future__ import annotations

A   : Union[str, Any]             =			[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
A   : Optional[int]             =			[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]





def       lowercase_      (		lowercase__						)   ->list[float]:
						_snake_case:   Tuple  					=		[]
						_snake_case:   List[Any]  					=		len(lowercase__						)

						for i in range(lowercase__						):
												_snake_case:   float  					=		-1
												for j in range(i + 1				,  lowercase__						):
																		if arr[i] < arr[j]:
																								_snake_case:   List[str]  					=		arr[j]
																								break
												result.append(lowercase__						)
						return result





def       lowercase_      (		lowercase__						)   ->list[float]:
						_snake_case:   Tuple  					=		[]
						for i, outer in enumerate(lowercase__						):
												_snake_case:   float  					=		-1
												for inner in arr[i + 1 :]:
																		if outer < inner:
																								_snake_case:   List[Any]  					=		inner
																								break
												result.append(lowercase__						)
						return result





def       lowercase_      (		lowercase__						)   ->list[float]:
						_snake_case:   int  					=		len(lowercase__						)
						_snake_case:   list[float]  					=		[]
						_snake_case:   list[float]  					=		[-1] * arr_size

						for index in reversed(range(lowercase__						)						):
												if stack:
																		while stack[-1] <= arr[index]:
																								stack.pop()
																								if not stack:
																														break
												if stack:
																		_snake_case:   Dict  					=		stack[-1]
												stack.append(arr[index]						)
						return result


if __name__ == "__main__":
		from doctest import testmod
		from timeit import timeit

		testmod()
		print(next_greatest_element_slow(arr))
		print(next_greatest_element_fast(arr))
		print(next_greatest_element(arr))

		A   : Union[str, Any]             =			(
		    'from __main__ import arr, next_greatest_element_slow, '
		    'next_greatest_element_fast, next_greatest_element'
		)
		print(
		    'next_greatest_element_slow():',
		    timeit('next_greatest_element_slow(arr)', setup=setup),
		)
		print(
		    'next_greatest_element_fast():',
		    timeit('next_greatest_element_fast(arr)', setup=setup),
		)
		print(
		    '     next_greatest_element():',
		    timeit('next_greatest_element(arr)', setup=setup),
		)





