



'''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__    :   int           =					'scheduler_config.json'



class  snake_case__						(    snake_case_				):
 A__				= 1
 A__				= 2
 A__				= 3
 A__				= 4
 A__				= 5



@dataclass
class  snake_case__						(    snake_case_				):
 A__				= 42



class  snake_case__						:
 A__				= SCHEDULER_CONFIG_NAME
 A__				= ['''dtype''']
 A__				= []
 A__				= True
 @classmethod
 def A_   (   cls						:							Optional[int]      ,     __a						:							Optional[Any] = None      ,     __a						:							Tuple = None      ,     __a						:							Optional[int]=False      ,     **__a						:							List[Any]      ,     )      ->   Tuple:







       '''simple docstring'''
       __snake_case							,     __snake_case :   int													=    cls.load_config(
           pretrained_model_name_or_path=A_      ,     subfolder=A_      ,     return_unused_kwargs=A_      ,     **A_      ,     )
       __snake_case							,     __snake_case :   int													=    cls.from_config(A_      ,     return_unused_kwargs=A_      ,     **A_  )

       if hasattr(A_      ,     'create_state'  ) and getattr(A_      ,     'has_state'      ,     A_  ):
             __snake_case :   List[str]													=    scheduler.create_state()

       if return_unused_kwargs:
             return scheduler, state, unused_kwargs

       return scheduler, state
 def A_   (   self						:							Optional[Any]      ,     __a						:							int      ,     __a						:							Any = False      ,     **__a						:							Optional[int]  )      ->   Any:







       '''simple docstring'''
       self.save_config(save_directory=A_      ,     push_to_hub=A_      ,     **A_  )
 @property
 def A_   (   self						:							Optional[Any]  )      ->   List[str]:







       '''simple docstring'''
       return self._get_compatibles()





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







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





def   a_   (		_UpperCAmelCase    :      jnp.ndarray ,_UpperCAmelCase    :      Tuple[int]				)    ->  int:
      assert len(_UpperCAmelCase				) >= x.ndim
      return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_UpperCAmelCase				) - x.ndim)				) ,_UpperCAmelCase				)





def   a_   (		_UpperCAmelCase    :      int ,_UpperCAmelCase    :      int=0.9_9_9 ,_UpperCAmelCase    :      List[Any]=jnp.floataa				)    ->  Tuple:

      def alpha_bar(_UpperCAmelCase    :      Union[str, Any]				):
            return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

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



@flax.struct.dataclass
class  snake_case__						:
 A__				= 42
 A__				= 42
 A__				= 42
 @classmethod
 def A_   (   cls						:							List[Any]      ,     __a						:							List[Any]  )      ->   str:







       '''simple docstring'''
       __snake_case :   Dict													=    scheduler.config

       if config.trained_betas is not None:
             __snake_case :   Any													=    jnp.asarray(config.trained_betas      ,     dtype=scheduler.dtype  )
       elif config.beta_schedule == "linear":
             __snake_case :   Union[str, Any]													=    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 :   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__}'''  )

       __snake_case :   List[str]													=    1.0 - betas

       __snake_case :   Optional[Any]													=    jnp.cumprod(A_      ,     axis=0  )

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





def   a_   (		_UpperCAmelCase    :      CommonSchedulerState ,_UpperCAmelCase    :      jnp.ndarray ,_UpperCAmelCase    :      jnp.ndarray ,_UpperCAmelCase    :      jnp.ndarray				)    ->  Optional[int]:
      __snake_case :   Optional[int]													=    state.alphas_cumprod

      __snake_case :   Any													=    alphas_cumprod[timesteps] ** 0.5
      __snake_case :   Tuple													=    sqrt_alpha_prod.flatten()
      __snake_case :   Tuple													=    broadcast_to_shape_from_left(_UpperCAmelCase ,original_samples.shape				)

      __snake_case :   Tuple													=    (1 - alphas_cumprod[timesteps]) ** 0.5
      __snake_case :   Optional[Any]													=    sqrt_one_minus_alpha_prod.flatten()
      __snake_case :   Optional[Any]													=    broadcast_to_shape_from_left(_UpperCAmelCase ,original_samples.shape				)

      return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





def   a_   (		_UpperCAmelCase    :      CommonSchedulerState ,_UpperCAmelCase    :      jnp.ndarray ,_UpperCAmelCase    :      jnp.ndarray ,_UpperCAmelCase    :      jnp.ndarray				)    ->  Any:
      __snake_case							,     __snake_case :   int													=    get_sqrt_alpha_prod(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase				)
      __snake_case :   Dict													=    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples





def   a_   (		_UpperCAmelCase    :      CommonSchedulerState ,_UpperCAmelCase    :      jnp.ndarray ,_UpperCAmelCase    :      jnp.ndarray ,_UpperCAmelCase    :      jnp.ndarray				)    ->  int:
      __snake_case							,     __snake_case :   int													=    get_sqrt_alpha_prod(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase				)
      __snake_case :   List[Any]													=    sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
      return velocity





'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase







def 		A_(     A						:		str):
							if not sentence:
														return ""
							UpperCamelCase   			=     dict(zip(A		,     A))
							return lower_to_upper.get(sentence[0]		,     sentence[0]) + sentence[1:]


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

						testmod()




