





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 					__snake_case	:


    '''simple docstring'''







    lowerCamelCase__    :		Optional[int]				=	None
    lowerCamelCase__    :		Optional[jnp.ndarray]				=	None
    lowerCamelCase__    :		Optional[jnp.ndarray]				=	None  # sigma(t_i)



    @classmethod
    def  lowercase_					(       cls     ):





        '''simple docstring'''

        return cls()





@dataclass
class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		jnp.ndarray
    lowerCamelCase__    :		jnp.ndarray
    lowerCamelCase__    :		KarrasVeSchedulerState





class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return True



    @register_to_config
    def __init__(       self   ,				A_ = 0.02   ,				A_ = 1_00   ,				A_ = 1.007   ,				A_ = 80   ,				A_ = 0.05   ,				A_ = 50   ,				):





        '''simple docstring'''

        pass



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return KarrasVeSchedulerState.create()



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_ = ()     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			jnp.arange(0   ,				A_     )[::-1].copy()
        SCREAMING_SNAKE_CASE__											=			[
            (
                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=A_   ,				schedule=jnp.array(A_   ,				dtype=jnp.floataa     )   ,				timesteps=A_   ,				)



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        if self.config.s_min <= sigma <= self.config.s_max:
            SCREAMING_SNAKE_CASE__											=			min(self.config.s_churn / state.num_inference_steps   ,				2**0.5 - 1     )
        else:
            SCREAMING_SNAKE_CASE__											=			0

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

        return sample_hat, sigma_hat



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_ = True   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			sample_hat + sigma_hat * model_output
        SCREAMING_SNAKE_CASE__											=			(sample_hat - pred_original_sample) / sigma_hat
        SCREAMING_SNAKE_CASE__											=			sample_hat + (sigma_prev - sigma_hat) * derivative

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

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



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_ = True   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			sample_prev + sigma_prev * model_output
        SCREAMING_SNAKE_CASE__											=			(sample_prev - pred_original_sample) / sigma_prev
        SCREAMING_SNAKE_CASE__											=			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=A_   ,				derivative=A_   ,				state=A_     )




    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        raise NotImplementedError()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A					:				List[str]				=			{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Dict				=			[
      """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """WavLMForAudioFrameClassification""",
      """WavLMForCTC""",
      """WavLMForSequenceClassification""",
      """WavLMForXVector""",
      """WavLMModel""",
      """WavLMPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  _A					:				List[Any]				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
