

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


lowerCAmelCase__       = "scheduler_config.json"






class 						__magic_name__    (       _snake_case    ):
 UpperCAmelCase							    =						1
 UpperCAmelCase							    =						2
 UpperCAmelCase							    =						3
 UpperCAmelCase							    =						4
 UpperCAmelCase							    =						5






@dataclass
class 						__magic_name__    (       _snake_case    ):
 UpperCAmelCase							    =						42






class 						__magic_name__    :
 UpperCAmelCase							    =						SCHEDULER_CONFIG_NAME
 UpperCAmelCase							    =						["""dtype"""]
 UpperCAmelCase							    =						[]
 UpperCAmelCase							    =						True


 @classmethod
 def _UpperCamelCase	(						cls		: Union[str, Any]			,						lowerCAmelCase__		: Dict[str, Any] = None			,						lowerCAmelCase__		: Optional[str] = None			,						lowerCAmelCase__		: str=False			,						**lowerCAmelCase__		: Optional[int]			,						)    ->      Tuple:
   UpperCAmelCase      ,	UpperCAmelCase					      =  cls.load_config(
       pretrained_model_name_or_path=lowerCAmelCase__			,						subfolder=lowerCAmelCase__			,						return_unused_kwargs=lowerCAmelCase__			,						**lowerCAmelCase__			,						)
   UpperCAmelCase      ,	UpperCAmelCase					      =  cls.from_config(lowerCAmelCase__			,						return_unused_kwargs=lowerCAmelCase__			,						**lowerCAmelCase__    )

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

   if return_unused_kwargs:
     return scheduler, state, unused_kwargs

   return scheduler, state


 def _UpperCamelCase	(						self		: List[Any]			,						lowerCAmelCase__		: Union[str, os.PathLike]			,						lowerCAmelCase__		: bool = False			,						**lowerCAmelCase__		: int    )    ->      Union[str, Any]:
   self.save_config(save_directory=lowerCAmelCase__			,						push_to_hub=lowerCAmelCase__			,						**lowerCAmelCase__    )


 @property
 def _UpperCamelCase	(						self		: Any    )    ->      List[Any]:
   return self._get_compatibles()


 @classmethod
 def _UpperCamelCase	(						cls		: str    )    ->      Optional[Any]:
   UpperCAmelCase					      =  list(set([cls.__name__] + cls._compatibles    )    )
   UpperCAmelCase					      =  importlib.import_module(__name__.split("."    )[0]    )
   UpperCAmelCase					      =  [
       getattr(lowerCAmelCase__			,						lowerCAmelCase__    ) for c in compatible_classes_str if hasattr(lowerCAmelCase__			,						lowerCAmelCase__    )
   ]
   return compatible_classes

def      _lowerCAmelCase(	__A   ,       __A			):
  assert len(__A			) >= x.ndim
  return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(__A			) - x.ndim)			)   ,       __A			)
def      _lowerCAmelCase(	__A   ,       __A=0.999   ,       __A=jnp.floataa			):

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

  UpperCAmelCase					      =  []
  for i in range(__A			):
    UpperCAmelCase					      =  i / num_diffusion_timesteps
    UpperCAmelCase					      =  (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 						__magic_name__    :
 UpperCAmelCase							    =						42
 UpperCAmelCase							    =						42
 UpperCAmelCase							    =						42


 @classmethod
 def _UpperCamelCase	(						cls		: int			,						lowerCAmelCase__		: int    )    ->      Any:
   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(lowerCAmelCase__			,						axis=0    )

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

def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A			):
  UpperCAmelCase					      =  state.alphas_cumprod

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

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

  return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A			):
  UpperCAmelCase      ,	UpperCAmelCase					      =  get_sqrt_alpha_prod(__A   ,       __A   ,       __A   ,       __A			)
  UpperCAmelCase					      =  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
  return noisy_samples





def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A			):
  UpperCAmelCase      ,	UpperCAmelCase					      =  get_sqrt_alpha_prod(__A   ,       __A   ,       __A   ,       __A			)
  UpperCAmelCase					      =  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
  return velocity






def      _lowerCAmelCase(	__A			):

  if not isinstance(__A   ,       __A			):
    raise TypeError("only integers accepted as input"			)
  else:
    UpperCAmelCase					      =  str(abs(__A			)			)
    UpperCAmelCase					      =  [list(__A			) for char in range(len(__A			)			)]
    for index in range(len(__A			)			):
      num_transpositions[index].pop(__A			)
    return max(
        int("".join(list(__A			)			)			) for transposition in num_transpositions			)


if __name__ == "__main__":
      __import__("doctest").testmod()



