






"""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


_lowerCAmelCase         =							"scheduler_config.json"



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				List[str]    =				1
	_A       :				List[str]    =				2
	_A       :				List[str]    =				3
	_A       :				List[str]    =				4
	_A       :				Optional[int]    =				5






@dataclass
class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				jnp.ndarray






class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	_A       :				Tuple    =				SCHEDULER_CONFIG_NAME
	_A       :				Optional[int]    =				["""dtype"""]
	_A       :				List[Any]    =				[]
	_A       :				Union[str, Any]    =				True





	@classmethod
	def    lowerCamelCase(cls     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_=False     ,     **lowerCAmelCase_     ,     ):
		A_    ,     A_		:						List[str]	    =   cls.load_config(
		    pretrained_model_name_or_path=lowerCAmelCase_     ,     subfolder=lowerCAmelCase_     ,     return_unused_kwargs=lowerCAmelCase_     ,     **lowerCAmelCase_     ,     )
		A_    ,     A_		:						Optional[Any]	    =   cls.from_config(lowerCAmelCase_     ,     return_unused_kwargs=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if hasattr(lowerCAmelCase_     ,     """create_state"""							) and getattr(lowerCAmelCase_     ,     """has_state"""     ,     lowerCAmelCase_							):
			A_		:						int	    =   scheduler.create_state()

		if return_unused_kwargs:
			return scheduler, state, unused_kwargs

		return scheduler, state





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = False     ,     **lowerCAmelCase_							):
		self.save_config(save_directory=lowerCAmelCase_     ,     push_to_hub=lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		return self._get_compatibles()





	@classmethod
	def    lowerCamelCase(cls							):
		A_		:						List[str]	    =   list(set([cls.__name__] + cls._compatibles							)							)
		A_		:						Tuple	    =   importlib.import_module(__name__.split("""."""							)[0]							)
		A_		:						List[Any]	    =   [
		    getattr(lowerCAmelCase_     ,     lowerCAmelCase_							) for c in compatible_classes_str if hasattr(lowerCAmelCase_     ,     lowerCAmelCase_							)
		]
		return compatible_classes






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






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__=0.999   ,   snake_case__=jnp.floataa					):

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

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



@flax.struct.dataclass
class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	_A       :				jnp.ndarray
	_A       :				jnp.ndarray
	_A       :				jnp.ndarray





	@classmethod
	def    lowerCamelCase(cls     ,     lowerCAmelCase_							):
		A_		:						Dict	    =   scheduler.config

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

		A_		:						Dict	    =   1.0 - betas

		A_		:						Any	    =   jnp.cumprod(lowerCAmelCase_     ,     axis=0							)

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






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						List[str]	    =   state.alphas_cumprod

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

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

	return sqrt_alpha_prod, sqrt_one_minus_alpha_prod






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):
	A_    ,     A_		:						List[Any]	    =   get_sqrt_alpha_prod(snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					)
	A_		:						int	    =   sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
	return noisy_samples






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):
	A_    ,     A_		:						Any	    =   get_sqrt_alpha_prod(snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					)
	A_		:						Tuple	    =   sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
	return velocity









"""simple docstring"""
import math






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	if (
	    not isinstance(snake_case__   ,   (int, float)					)
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("""power_factor must be a valid float value between -1 and 1."""					)
	return apparent_power * power_factor






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	if (
	    not isinstance(snake_case__   ,   (int, float)					)
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("""power_factor must be a valid float value between -1 and 1."""					)
	return apparent_power * math.sqrt(1 - power_factor**2					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

