"""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


__SCREAMING_SNAKE_CASE								="scheduler_config.json"







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						1
   lowercase							=						2
   lowercase							=						3
   lowercase							=						4
   lowercase							=						5







@dataclass
class  UpperCamelCase	(    lowercase_						):
   lowercase							=						42







class  UpperCamelCase	:
   lowercase							=						SCHEDULER_CONFIG_NAME
   lowercase							=						['dtype']
   lowercase							=						[]
   lowercase							=						True
   @classmethod
   def     _UpperCAmelCase     (    cls  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase=False  ,**__UpperCamelCase  ,)			->   Optional[Any]:


        '''simple docstring'''


        lowercase_							,						lowercase_			:       Tuple									=			cls.load_config(
            pretrained_model_name_or_path=__UpperCamelCase  ,subfolder=__UpperCamelCase  ,return_unused_kwargs=__UpperCamelCase  ,**__UpperCamelCase  ,)
        lowercase_							,						lowercase_			:       Union[str, Any]									=			cls.from_config(__UpperCamelCase  ,return_unused_kwargs=__UpperCamelCase  ,**__UpperCamelCase    )

        if hasattr(__UpperCamelCase  ,'create_state'    ) and getattr(__UpperCamelCase  ,'has_state'  ,__UpperCamelCase    ):
             lowercase_			:       Dict									=			scheduler.create_state()

        if return_unused_kwargs:
             return scheduler, state, unused_kwargs

        return scheduler, state
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = False  ,**__UpperCamelCase    )			->   Tuple:


        '''simple docstring'''


        self.save_config(save_directory=__UpperCamelCase  ,push_to_hub=__UpperCamelCase  ,**__UpperCamelCase    )
   @property
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        return self._get_compatibles()







   @classmethod
   def     _UpperCAmelCase     (    cls    )			->   int:


        '''simple docstring'''


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






def     lowercase__( __SCREAMING_SNAKE_CASE							:  jnp.ndarray  ,    __SCREAMING_SNAKE_CASE							:  Tuple[int]   ):
     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							:  int  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]=0.999  ,    __SCREAMING_SNAKE_CASE							:  Tuple=jnp.floataa   ):

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

     lowercase_			:       int									=			[]
     for i in range(__SCREAMING_SNAKE_CASE   ):
          lowercase_			:       int									=			i / num_diffusion_timesteps
          lowercase_			:       str									=			(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  UpperCamelCase	:
   lowercase							=						42
   lowercase							=						42
   lowercase							=						42
   @classmethod
   def     _UpperCAmelCase     (    cls  ,__UpperCamelCase    )			->   str:


        '''simple docstring'''


        lowercase_			:       Dict									=			scheduler.config

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

        lowercase_			:       Dict									=			1.0 - betas

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

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






def     lowercase__( __SCREAMING_SNAKE_CASE							:  CommonSchedulerState  ,    __SCREAMING_SNAKE_CASE							:  jnp.ndarray  ,    __SCREAMING_SNAKE_CASE							:  jnp.ndarray  ,    __SCREAMING_SNAKE_CASE							:  jnp.ndarray   ):
     lowercase_			:       Any									=			state.alphas_cumprod

     lowercase_			:       Dict									=			alphas_cumprod[timesteps] ** 0.5
     lowercase_			:       List[str]									=			sqrt_alpha_prod.flatten()
     lowercase_			:       Union[str, Any]									=			broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE  ,    original_samples.shape   )

     lowercase_			:       Any									=			(1 - alphas_cumprod[timesteps]) ** 0.5
     lowercase_			:       List[Any]									=			sqrt_one_minus_alpha_prod.flatten()
     lowercase_			:       int									=			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							:  CommonSchedulerState  ,    __SCREAMING_SNAKE_CASE							:  jnp.ndarray  ,    __SCREAMING_SNAKE_CASE							:  jnp.ndarray  ,    __SCREAMING_SNAKE_CASE							:  jnp.ndarray   ):
     lowercase_							,						lowercase_			:       Any									=			get_sqrt_alpha_prod(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     lowercase_			:       Any									=			sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples







def     lowercase__( __SCREAMING_SNAKE_CASE							:  CommonSchedulerState  ,    __SCREAMING_SNAKE_CASE							:  jnp.ndarray  ,    __SCREAMING_SNAKE_CASE							:  jnp.ndarray  ,    __SCREAMING_SNAKE_CASE							:  jnp.ndarray   ):
     lowercase_							,						lowercase_			:       Optional[Any]									=			get_sqrt_alpha_prod(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     lowercase_			:       Any									=			sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
     return velocity


"""simple docstring"""







from diffusers.utils.testing_utils import require_onnxruntime







@require_onnxruntime
class  UpperCamelCase	:
   pass

