"""simple docstring"""


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin


@flax.struct.dataclass
class 							a					:
    # setable values
    _snake_case  :	Optional[int]  				=				None
    _snake_case  :	Optional[jnp.ndarray]  				=				None
    _snake_case  :	Optional[jnp.ndarray]  				=				None  # sigma(t_i)


    @classmethod
    def   lowerCAmelCase_	(       cls						:	Tuple       ):
      return cls()



@dataclass
class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	jnp.ndarray
    _snake_case  :	jnp.ndarray
    _snake_case  :	KarrasVeSchedulerState



class 							a					(		lowerCAmelCase_						,				lowerCAmelCase_ ):


    @property
    def   lowerCAmelCase_	(       self						:	str       ):
      return True


    @register_to_config
    def __init__(       self						:	int ,					__lowerCAmelCase						:	float = 0.02 ,					__lowerCAmelCase						:	float = 100 ,					__lowerCAmelCase						:	float = 1.007 ,					__lowerCAmelCase						:	float = 80 ,					__lowerCAmelCase						:	float = 0.05 ,					__lowerCAmelCase						:	float = 50 ,					):
      pass


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      return KarrasVeSchedulerState.create()


    def   lowerCAmelCase_	(       self						:	Tuple ,					__lowerCAmelCase						:	KarrasVeSchedulerState ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Tuple = ()       ):
      _UpperCAmelCase    			=   jnp.arange(0 ,					__lowerCAmelCase       )[::-1].copy()
      _UpperCAmelCase    			=   [
          (
              self.config.sigma_max**2
              * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
          )
          for i in timesteps
      ]

      return state.replace(
          num_inference_steps=__lowerCAmelCase ,					schedule=jnp.array(__lowerCAmelCase ,					dtype=jnp.floataa       ) ,					timesteps=__lowerCAmelCase ,					)


    def   lowerCAmelCase_	(       self						:	Optional[int] ,					__lowerCAmelCase						:	KarrasVeSchedulerState ,					__lowerCAmelCase						:	jnp.ndarray ,					__lowerCAmelCase						:	float ,					__lowerCAmelCase						:	random.KeyArray ,					):
      if self.config.s_min <= sigma <= self.config.s_max:
        _UpperCAmelCase    			=   min(self.config.s_churn / state.num_inference_steps ,					2**0.5 - 1       )
      else:
        _UpperCAmelCase    			=   0

      # sample eps ~ N(0, S_noise^2 * I)
      _UpperCAmelCase    			=   random.split(__lowerCAmelCase ,					num=1       )
      _UpperCAmelCase    			=   self.config.s_noise * random.normal(key=__lowerCAmelCase ,					shape=sample.shape       )
      _UpperCAmelCase    			=   sigma + gamma * sigma
      _UpperCAmelCase    			=   sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

      return sample_hat, sigma_hat


    def   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	KarrasVeSchedulerState ,					__lowerCAmelCase						:	jnp.ndarray ,					__lowerCAmelCase						:	float ,					__lowerCAmelCase						:	float ,					__lowerCAmelCase						:	jnp.ndarray ,					__lowerCAmelCase						:	bool = True ,					):
      _UpperCAmelCase    			=   sample_hat + sigma_hat * model_output
      _UpperCAmelCase    			=   (sample_hat - pred_original_sample) / sigma_hat
      _UpperCAmelCase    			=   sample_hat + (sigma_prev - sigma_hat) * derivative

      if not return_dict:
        return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=__lowerCAmelCase ,					derivative=__lowerCAmelCase ,					state=__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	str ,					__lowerCAmelCase						:	KarrasVeSchedulerState ,					__lowerCAmelCase						:	jnp.ndarray ,					__lowerCAmelCase						:	float ,					__lowerCAmelCase						:	float ,					__lowerCAmelCase						:	jnp.ndarray ,					__lowerCAmelCase						:	jnp.ndarray ,					__lowerCAmelCase						:	jnp.ndarray ,					__lowerCAmelCase						:	bool = True ,					):
      _UpperCAmelCase    			=   sample_prev + sigma_prev * model_output
      _UpperCAmelCase    			=   (sample_prev - pred_original_sample) / sigma_prev
      _UpperCAmelCase    			=   sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

      if not return_dict:
        return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=__lowerCAmelCase ,					derivative=__lowerCAmelCase ,					state=__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Optional[int] ,					__lowerCAmelCase						:	KarrasVeSchedulerState ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	Tuple       ):
      raise NotImplementedError()

"""simple docstring"""


from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	Dict  				=				CustomTokenizer
    pass
