





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


SCREAMING_SNAKE_CASE_		     = """scheduler_config.json"""

class 	__SCREAMING_SNAKE_CASE				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			str				=    1
     __snake_case					:			Union[str, Any]				=    2
     __snake_case					:			Union[str, Any]				=    3
     __snake_case					:			str				=    4
     __snake_case					:			Optional[int]				=    5

@dataclass
class 	__SCREAMING_SNAKE_CASE				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			jnp.ndarray

class 	__SCREAMING_SNAKE_CASE				:



     '''simple docstring'''

     __snake_case					:			Optional[int]				=    SCHEDULER_CONFIG_NAME
     __snake_case					:			Dict				=    ["dtype"]
     __snake_case					:			Optional[Any]				=    []
     __snake_case					:			Dict				=    True






     @classmethod
     def        SCREAMING_SNAKE_CASE__						(							cls		:					str		,lowerCamelCase__		:					Dict[str, Any] = None		,lowerCamelCase__		:					Optional[str] = None		,lowerCamelCase__		:					List[Any]=False		,**lowerCamelCase__		:					str		,)  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		cls.load_config(
               pretrained_model_name_or_path=lowerCamelCase__		,subfolder=lowerCamelCase__		,return_unused_kwargs=lowerCamelCase__		,**lowerCamelCase__		,)
           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		cls.from_config(lowerCamelCase__		,return_unused_kwargs=lowerCamelCase__		,**lowerCamelCase__ )

           if hasattr(lowerCamelCase__		,"""create_state""" ) and getattr(lowerCamelCase__		,"""has_state"""		,lowerCamelCase__ ):
                 SCREAMING_SNAKE_CASE	=		scheduler.create_state()

           if return_unused_kwargs:
                 return scheduler, state, unused_kwargs

           return scheduler, state






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any]		,lowerCamelCase__		:					Union[str, os.PathLike]		,lowerCamelCase__		:					bool = False		,**lowerCamelCase__		:					Optional[int] )  ->	Union[str, Any]:




           '''simple docstring'''


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






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Tuple:




           '''simple docstring'''


           return self._get_compatibles()






     @classmethod
     def        SCREAMING_SNAKE_CASE__						(							cls		:					Any )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		list(set([cls.__name__] + cls._compatibles ) )
           SCREAMING_SNAKE_CASE	=		importlib.import_module(__name__.split(""".""" )[0] )
           SCREAMING_SNAKE_CASE	=		[
               getattr(lowerCamelCase__		,lowerCamelCase__ ) for c in compatible_classes_str if hasattr(lowerCamelCase__		,lowerCamelCase__ )
           ]
           return compatible_classes



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     jnp.ndarray:


      '''simple docstring'''


      assert len(_SCREAMING_SNAKE_CASE			) >= x.ndim
      return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_SCREAMING_SNAKE_CASE			) - x.ndim)			)				,		_SCREAMING_SNAKE_CASE			)



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE=0.999				,		_SCREAMING_SNAKE_CASE=jnp.floataa			)				->     jnp.ndarray:


      '''simple docstring'''


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

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

@flax.struct.dataclass
class 	__SCREAMING_SNAKE_CASE				:



     '''simple docstring'''

     __snake_case					:			jnp.ndarray
     __snake_case					:			jnp.ndarray
     __snake_case					:			jnp.ndarray






     @classmethod
     def        SCREAMING_SNAKE_CASE__						(							cls		:					Union[str, Any]		,lowerCamelCase__		:					List[str] )  ->	Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		scheduler.config

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

           SCREAMING_SNAKE_CASE	=		1.0 - betas

           SCREAMING_SNAKE_CASE	=		jnp.cumprod(lowerCamelCase__		,axis=0 )

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



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Optional[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		state.alphas_cumprod

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

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

      return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		get_sqrt_alpha_prod(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		get_sqrt_alpha_prod(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
      return velocity









def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      return price * (1 + tax_rate)


if __name__ == "__main__":
      print(F'''{price_plus_tax(1_0_0, 0.25) = }''')
      print(F'''{price_plus_tax(125.50, 0.05) = }''')


