
'''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


UpperCAmelCase_       :   Optional[Any]   =       'scheduler_config.json'

class  UpperCAmelCase__						(       lowerCAmelCase__ ):
    lowerCAmelCase_				    =		1
    lowerCAmelCase_				    =		2
    lowerCAmelCase_				    =		3
    lowerCAmelCase_				    =		4
    lowerCAmelCase_				    =		5


@dataclass
class  UpperCAmelCase__						(       lowerCAmelCase__ ):
    lowerCAmelCase_				    =		42


class  UpperCAmelCase__						:
    lowerCAmelCase_				    =		SCHEDULER_CONFIG_NAME
    lowerCAmelCase_				    =		["dtype"]
    lowerCAmelCase_				    =		[]
    lowerCAmelCase_				    =		True



    @classmethod
    def   lowerCamelCase_  (	cls      :       int,__A      :       Optional[Any] = None,__A      :       Union[str, Any] = None,__A      :       int=False,**__A      :       Tuple,):
          _lowerCamelCase						,		_lowerCamelCase				:   Union[str, Any]								=   cls.load_config(
              pretrained_model_name_or_path=_lowerCamelCase,subfolder=_lowerCamelCase,return_unused_kwargs=_lowerCamelCase,**_lowerCamelCase,)
          _lowerCamelCase						,		_lowerCamelCase				:   str								=   cls.from_config(_lowerCamelCase,return_unused_kwargs=_lowerCamelCase,**_lowerCamelCase     )

          if hasattr(_lowerCamelCase,"create_state"     ) and getattr(_lowerCamelCase,"has_state",_lowerCamelCase     ):
                _lowerCamelCase				:   str								=   scheduler.create_state()

          if return_unused_kwargs:
                return scheduler, state, unused_kwargs

          return scheduler, state



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       List[Any],__A      :       Union[str, Any] = False,**__A      :       Tuple     ):
          self.save_config(save_directory=_lowerCamelCase,push_to_hub=_lowerCamelCase,**_lowerCamelCase     )



    @property
    def   lowerCamelCase_  (	self      :       Dict     ):
          return self._get_compatibles()



    @classmethod
    def   lowerCamelCase_  (	cls      :       List[Any]     ):
          _lowerCamelCase				:   int								=   list(set([cls.__name__] + cls._compatibles     )     )
          _lowerCamelCase				:   int								=   importlib.import_module(__name__.split("."     )[0]     )
          _lowerCamelCase				:   List[str]								=   [
              getattr(_lowerCamelCase,_lowerCamelCase     ) for c in compatible_classes_str if hasattr(_lowerCamelCase,_lowerCamelCase     )
          ]
          return compatible_classes







def 		A_       (	_lowerCAmelCase   :    jnp.ndarray				,	_lowerCAmelCase   :    Tuple[int]							):



      """simple docstring"""




      assert len(lowerCamelCase_							) >= x.ndim
      return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCamelCase_							) - x.ndim)							)				,	lowerCamelCase_							)







def 		A_       (	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    Union[str, Any]=0.9_9_9				,	_lowerCAmelCase   :    Union[str, Any]=jnp.floataa							):



      """simple docstring"""




      def alpha_bar(_lowerCAmelCase   :    Any							):
            return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2							) ** 2

      _lowerCamelCase				:   Tuple								=   []
      for i in range(lowerCamelCase_							):
            _lowerCamelCase				:   Dict								=   i / num_diffusion_timesteps
            _lowerCamelCase				:   Dict								=   (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_				    =		42
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		42



    @classmethod
    def   lowerCamelCase_  (	cls      :       Union[str, Any],__A      :       Dict     ):
          _lowerCamelCase				:   Optional[Any]								=   scheduler.config

          if config.trained_betas is not None:
                _lowerCamelCase				:   Dict								=   jnp.asarray(config.trained_betas,dtype=scheduler.dtype     )
          elif config.beta_schedule == "linear":
                _lowerCamelCase				:   int								=   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				:   Optional[Any]								=   (
                    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				:   List[str]								=   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				:   int								=   1.0 - betas

          _lowerCamelCase				:   List[Any]								=   jnp.cumprod(_lowerCamelCase,axis=0     )

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







def 		A_       (	_lowerCAmelCase   :    CommonSchedulerState				,	_lowerCAmelCase   :    jnp.ndarray				,	_lowerCAmelCase   :    jnp.ndarray				,	_lowerCAmelCase   :    jnp.ndarray							):



      """simple docstring"""




      _lowerCamelCase				:   List[str]								=   state.alphas_cumprod

      _lowerCamelCase				:   Optional[int]								=   alphas_cumprod[timesteps] ** 0.5
      _lowerCamelCase				:   List[str]								=   sqrt_alpha_prod.flatten()
      _lowerCamelCase				:   List[Any]								=   broadcast_to_shape_from_left(lowerCamelCase_				,	original_samples.shape							)

      _lowerCamelCase				:   Optional[Any]								=   (1 - alphas_cumprod[timesteps]) ** 0.5
      _lowerCamelCase				:   List[Any]								=   sqrt_one_minus_alpha_prod.flatten()
      _lowerCamelCase				:   int								=   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							):



      """simple docstring"""




      _lowerCamelCase						,		_lowerCamelCase				:   Dict								=   get_sqrt_alpha_prod(lowerCamelCase_				,	lowerCamelCase_				,	lowerCamelCase_				,	lowerCamelCase_							)
      _lowerCamelCase				:   Tuple								=   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							):



      """simple docstring"""




      _lowerCamelCase						,		_lowerCamelCase				:   Any								=   get_sqrt_alpha_prod(lowerCamelCase_				,	lowerCamelCase_				,	lowerCamelCase_				,	lowerCamelCase_							)
      _lowerCamelCase				:   Tuple								=   sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
      return velocity

'''simple docstring'''






import warnings

from ..trainer import Trainer
from ..utils import logging


UpperCAmelCase_       :   Union[str, Any]   =       logging.get_logger(__name__)

class  UpperCAmelCase__						(       A ):



    def __init__(	self      :       int,__A      :       Any=None,**__A      :       Optional[Any]     ):
          warnings.warn(
              "`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` "
              "instead.",__A,)
          super().__init__(args=__A,**__A     )