


'''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


__snake_case    						="""scheduler_config.json"""

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Optional[int]		=     1
     lowerCamelCase				:	List[str]		=     2
     lowerCamelCase				:	str		=     3
     lowerCamelCase				:	Any		=     4
     lowerCamelCase				:	Dict		=     5




@dataclass
class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	jnp.ndarray




class 		UpperCAmelCase_      :
     lowerCamelCase				:	Any		=     SCHEDULER_CONFIG_NAME
     lowerCamelCase				:	str		=     ['''dtype''']
     lowerCamelCase				:	int		=     []
     lowerCamelCase				:	Dict		=     True





     @classmethod
     def 							__UpperCAmelCase      ( cls       :      Union[str, Any]    ,      UpperCAmelCase__       :      Dict[str, Any] = None    ,      UpperCAmelCase__       :      Optional[str] = None    ,      UpperCAmelCase__       :      int=False    ,      **UpperCAmelCase__       :      str    ,      )				->  List[Any]:
        lowerCAmelCase     ,  lowerCAmelCase     =			cls.load_config(
            pretrained_model_name_or_path=UpperCAmelCase__    ,      subfolder=UpperCAmelCase__    ,      return_unused_kwargs=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )
        lowerCAmelCase     ,  lowerCAmelCase     =			cls.from_config(UpperCAmelCase__    ,      return_unused_kwargs=UpperCAmelCase__    ,      **UpperCAmelCase__    )

        if hasattr(UpperCAmelCase__    ,      'create_state'    ) and getattr(UpperCAmelCase__    ,      'has_state'    ,      UpperCAmelCase__    ):
           lowerCAmelCase     =			scheduler.create_state()

        if return_unused_kwargs:
           return scheduler, state, unused_kwargs

        return scheduler, state





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





     @property
     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  Dict:
        return self._get_compatibles()





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





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





def 	a_					( lowerCamelCase	:     int	, lowerCamelCase	:     Dict=0.999	, lowerCamelCase	:     Optional[Any]=jnp.floataa				):

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

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

@flax.struct.dataclass
class 		UpperCAmelCase_      :
     lowerCamelCase				:	jnp.ndarray
     lowerCamelCase				:	jnp.ndarray
     lowerCamelCase				:	jnp.ndarray





     @classmethod
     def 							__UpperCAmelCase      ( cls       :      Optional[Any]    ,      UpperCAmelCase__       :      Tuple    )				->  List[str]:
        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(UpperCAmelCase__    ,      axis=0    )

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





def 	a_					( lowerCamelCase	:     CommonSchedulerState	, lowerCamelCase	:     jnp.ndarray	, lowerCamelCase	:     jnp.ndarray	, lowerCamelCase	:     jnp.ndarray				):
   lowerCAmelCase     =			state.alphas_cumprod

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

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

   return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





def 	a_					( lowerCamelCase	:     CommonSchedulerState	, lowerCamelCase	:     jnp.ndarray	, lowerCamelCase	:     jnp.ndarray	, lowerCamelCase	:     jnp.ndarray				):
   lowerCAmelCase     ,  lowerCAmelCase     =			get_sqrt_alpha_prod(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)
   lowerCAmelCase     =			sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples





def 	a_					( lowerCamelCase	:     CommonSchedulerState	, lowerCamelCase	:     jnp.ndarray	, lowerCamelCase	:     jnp.ndarray	, lowerCamelCase	:     jnp.ndarray				):
   lowerCAmelCase     ,  lowerCAmelCase     =			get_sqrt_alpha_prod(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)
   lowerCAmelCase     =			sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
   return velocity










"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version

def       _snake_case						(						*UpperCamelCase						:		str			,		UpperCamelCase						:		Optional[Union[Dict, Any]] = None			,		UpperCamelCase						:		Tuple=True			,		UpperCamelCase						:		Optional[int]=2						):
 from .. import __version__

 UpperCAmelCase	:					Tuple	=	take_from
 UpperCAmelCase	:					Optional[Any]	=	()
 if not isinstance(args[0]			,		UpperCamelCase						):
  UpperCAmelCase	:					List[str]	=	(args,)

 for attribute, version_name, message in args:
  if version.parse(version.parse(UpperCamelCase						).base_version						) >= version.parse(UpperCamelCase						):
   raise ValueError(
       F"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
       F" version {__version__} is >= {version_name}"						)

  UpperCAmelCase	:					Optional[int]	=	None
  if isinstance(UpperCamelCase			,		UpperCamelCase						) and attribute in deprecated_kwargs:
   values += (deprecated_kwargs.pop(UpperCamelCase						),)
   UpperCAmelCase	:					List[str]	=	F"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
  elif hasattr(UpperCamelCase			,		UpperCamelCase						):
   values += (getattr(UpperCamelCase			,		UpperCamelCase						),)
   UpperCAmelCase	:					List[Any]	=	F"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
  elif deprecated_kwargs is None:
   UpperCAmelCase	:					int	=	F"`{attribute}` is deprecated and will be removed in version {version_name}."

  if warning is not None:
   UpperCAmelCase	:					Optional[Any]	=	warning + """ """ if standard_warn else """"""
   warnings.warn(warning + message			,		UpperCamelCase			,		stacklevel=UpperCamelCase						)

 if isinstance(UpperCamelCase			,		UpperCamelCase						) and len(UpperCamelCase						) > 0:
  UpperCAmelCase	:					Optional[int]	=	inspect.getouterframes(inspect.currentframe()						)[1]
  UpperCAmelCase	:					Union[str, Any]	=	call_frame.filename
  UpperCAmelCase	:					List[Any]	=	call_frame.lineno
  UpperCAmelCase	:					List[str]	=	call_frame.function
  UpperCAmelCase	,		UpperCAmelCase	:					Optional[int]	=	next(iter(deprecated_kwargs.items()						)						)
  raise TypeError(F"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"						)

 if len(UpperCamelCase						) == 0:
  return
 elif len(UpperCamelCase						) == 1:
  return values[0]
 return values
