


'''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_						:       Optional[Any]      			=				"""scheduler_config.json"""





class   snake_case					(					_snake_case    ):





  """simple docstring"""




  _lowerCamelCase						=						1
  _lowerCamelCase						=						2
  _lowerCamelCase						=						3
  _lowerCamelCase						=						4
  _lowerCamelCase						=						5





@dataclass
class   snake_case					(					_snake_case    ):





  """simple docstring"""




  _lowerCamelCase						=						42





class   snake_case					:





  """simple docstring"""




  _lowerCamelCase						=						SCHEDULER_CONFIG_NAME
  _lowerCamelCase						=						["dtype"]
  _lowerCamelCase						=						[]
  _lowerCamelCase						=						True






  @classmethod
  def 						snake_case	(					cls    ,						UpperCamelCase = None    ,						UpperCamelCase = None    ,						UpperCamelCase=False    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   lowerCamelCase_				,lowerCamelCase_  				=						cls.load_config(
       pretrained_model_name_or_path=lowerCAmelCase__    ,						subfolder=lowerCAmelCase__    ,						return_unused_kwargs=lowerCAmelCase__    ,						**lowerCAmelCase__    ,						)
   lowerCamelCase_				,lowerCamelCase_  				=						cls.from_config(lowerCAmelCase__    ,						return_unused_kwargs=lowerCAmelCase__    ,						**lowerCAmelCase__  )

   if hasattr(lowerCAmelCase__    ,						"create_state"  ) and getattr(lowerCAmelCase__    ,						"has_state"    ,						lowerCAmelCase__  ):
    lowerCamelCase_  				=						scheduler.create_state()

   if return_unused_kwargs:
    return scheduler, state, unused_kwargs

   return scheduler, state






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = False    ,						**UpperCamelCase  ):







   """simple docstring"""
   self.save_config(save_directory=lowerCAmelCase__    ,						push_to_hub=lowerCAmelCase__    ,						**lowerCAmelCase__  )






  @property
  def 						snake_case	(					self  ):







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







  @classmethod
  def 						snake_case	(					cls  ):







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





def __snake_case      (   UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Tuple	):
 assert len(__A	) >= x.ndim
 return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(__A	) - x.ndim)	)      ,       __A	)





def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		str=0.999      ,       UpperCAmelCase_		:		Tuple=jnp.floataa	):

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

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





@flax.struct.dataclass
class   snake_case					:





  """simple docstring"""




  _lowerCamelCase						=						42
  _lowerCamelCase						=						42
  _lowerCamelCase						=						42






  @classmethod
  def 						snake_case	(					cls    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						scheduler.config

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

   lowerCamelCase_  				=						1.0 - betas

   lowerCamelCase_  				=						jnp.cumprod(lowerCAmelCase__    ,						axis=0  )

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





def __snake_case      (   UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Any	):
 lowerCamelCase_  				=						state.alphas_cumprod

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

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

 return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





def __snake_case      (   UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		Tuple	):
 lowerCamelCase_				,lowerCamelCase_  				=						get_sqrt_alpha_prod(__A      ,       __A      ,       __A      ,       __A	)
 lowerCamelCase_  				=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
 return noisy_samples







def __snake_case      (   UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		Optional[Any]	):
 lowerCamelCase_				,lowerCamelCase_  				=						get_sqrt_alpha_prod(__A      ,       __A      ,       __A      ,       __A	)
 lowerCamelCase_  				=						sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
 return velocity







'''simple docstring'''

def __snake_case      (   UpperCAmelCase_		:		int = 100	):
 lowerCamelCase_  				=						n * (n + 1) * (2 * n + 1) / 6
 lowerCamelCase_  				=						(n * (n + 1) / 2) ** 2
 return int(square_of_sum - sum_of_squares	)


if __name__ == "__main__":
     print(f'''{solution() = }''')



