


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    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[Any]              =  1
    UpperCAmelCase__						:		Union[str, Any]              =  2
    UpperCAmelCase__						:		List[Any]              =  3
    UpperCAmelCase__						:		Optional[int]              =  4
    UpperCAmelCase__						:		Dict              =  5






@dataclass
class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		jnp.ndarray






class    UpperCAmelCase__	:



    """simple docstring"""






    UpperCAmelCase__						:		str              =  SCHEDULER_CONFIG_NAME
    UpperCAmelCase__						:		str              =  ["dtype"]
    UpperCAmelCase__						:		int              =  []
    UpperCAmelCase__						:		List[Any]              =  True






    @classmethod
    def _a (      cls ,       A_ = None ,       A_ = None ,       A_=False ,       **A_ ,       )  ->	List[Any]:
         __UpperCamelCase							,				__UpperCamelCase   							=cls.load_config(
             pretrained_model_name_or_path=A_ ,       subfolder=A_ ,       return_unused_kwargs=A_ ,       **A_ ,       )
         __UpperCamelCase							,				__UpperCamelCase   							=cls.from_config(A_ ,       return_unused_kwargs=A_ ,       **A_   )

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

         if return_unused_kwargs:
              return scheduler, state, unused_kwargs

         return scheduler, state






    def _a (      self ,       A_ ,       A_ = False ,       **A_   )  ->	Dict:
         self.save_config(save_directory=A_ ,       push_to_hub=A_ ,       **A_   )






    @property
    def _a (      self   )  ->	Dict:
         return self._get_compatibles()






    @classmethod
    def _a (      cls   )  ->	List[Any]:
         __UpperCamelCase   							=list(set([cls.__name__] + cls._compatibles   )   )
         __UpperCamelCase   							=importlib.import_module(__name__.split('.'   )[0]   )
         __UpperCamelCase   							=[
             getattr(A_ ,       A_   ) for c in compatible_classes_str if hasattr(A_ ,       A_   )
         ]
         return compatible_classes

def 						_UpperCAmelCase    (      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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]=0.999      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]=jnp.floataa    ):

     def alpha_bar(SCREAMING_SNAKE_CASE__      : List[str]    ):
          return math.cos((time_step + 0.008) / 1.008 * math.pi / 2    ) ** 2

     __UpperCamelCase   							=[]
     for i in range(SCREAMING_SNAKE_CASE__    ):
          __UpperCamelCase   							=i / num_diffusion_timesteps
          __UpperCamelCase   							=(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__	:



    """simple docstring"""






    UpperCAmelCase__						:		jnp.ndarray
    UpperCAmelCase__						:		jnp.ndarray
    UpperCAmelCase__						:		jnp.ndarray






    @classmethod
    def _a (      cls ,       A_   )  ->	List[str]:
         __UpperCamelCase   							=scheduler.config

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

         __UpperCamelCase   							=1.0 - betas

         __UpperCamelCase   							=jnp.cumprod(A_ ,       axis=0   )

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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : CommonSchedulerState      ,					SCREAMING_SNAKE_CASE__      : jnp.ndarray      ,					SCREAMING_SNAKE_CASE__      : jnp.ndarray      ,					SCREAMING_SNAKE_CASE__      : jnp.ndarray    ):
     __UpperCamelCase   							=state.alphas_cumprod

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

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

     return sqrt_alpha_prod, sqrt_one_minus_alpha_prod

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : CommonSchedulerState      ,					SCREAMING_SNAKE_CASE__      : jnp.ndarray      ,					SCREAMING_SNAKE_CASE__      : jnp.ndarray      ,					SCREAMING_SNAKE_CASE__      : jnp.ndarray    ):
     __UpperCamelCase							,				__UpperCamelCase   							=get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples





def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : CommonSchedulerState      ,					SCREAMING_SNAKE_CASE__      : jnp.ndarray      ,					SCREAMING_SNAKE_CASE__      : jnp.ndarray      ,					SCREAMING_SNAKE_CASE__      : jnp.ndarray    ):
     __UpperCamelCase							,				__UpperCamelCase   							=get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
     return velocity









from ..utils import DummyObject, requires_backends






class    UpperCAmelCase__	( metaclass=A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Any              =  ["speech"]






    def __init__(      self ,       *A_ ,       **A_   )  ->	Any:
         requires_backends(self ,       ['speech']   )






class    UpperCAmelCase__	( metaclass=A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Union[str, Any]              =  ["speech"]






    def __init__(      self ,       *A_ ,       **A_   )  ->	Union[str, Any]:
         requires_backends(self ,       ['speech']   )





