





'''simple docstring'''


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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class 				a_       (    __SCREAMING_SNAKE_CASE							):
     __A								=      42
     __A								=      42
     __A								=      None







class 				a_       (    __SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE							):
     __A								=      2





     @register_to_config
     def __init__(			self		: Dict     ,				lowercase		: float = 0.02     ,				lowercase		: float = 100     ,				lowercase		: float = 1.0_07     ,				lowercase		: float = 80     ,				lowercase		: float = 0.05     ,				lowercase		: float = 50     ,				):





        """simple docstring"""




        lowercase_      :Tuple         =			sigma_max

        # setable values
        lowercase_      :int         =			None
        lowercase_      :np.IntTensor         =			None
        lowercase_      :torch.FloatTensor         =			None  # sigma(t_i)





     def 							lowercase__				(			self		: Union[str, Any]     ,				lowercase		: torch.FloatTensor     ,				lowercase		: Optional[int] = None						):





        """simple docstring"""




        return sample





     def 							lowercase__				(			self		: Tuple     ,				lowercase		: int     ,				lowercase		: Union[str, torch.device] = None						):





        """simple docstring"""




        lowercase_      :int         =			num_inference_steps
        lowercase_      :Union[str, Any]         =			np.arange(0     ,				self.num_inference_steps						)[::-1].copy()
        lowercase_      :Dict         =			torch.from_numpy(_SCREAMING_SNAKE_CASE						).to(_SCREAMING_SNAKE_CASE						)
        lowercase_      :str         =			[
            (
                self.config.sigma_max**2
                * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
            )
            for i in self.timesteps
        ]
        lowercase_      :Optional[Any]         =			torch.tensor(_SCREAMING_SNAKE_CASE     ,				dtype=torch.floataa     ,				device=_SCREAMING_SNAKE_CASE						)





     def 							lowercase__				(			self		: List[str]     ,				lowercase		: torch.FloatTensor     ,				lowercase		: float     ,				lowercase		: Optional[torch.Generator] = None						):





        """simple docstring"""




        if self.config.s_min <= sigma <= self.config.s_max:
           lowercase_      :List[str]         =			min(self.config.s_churn / self.num_inference_steps     ,				2**0.5 - 1						)
        else:
           lowercase_      :int         =			0

        # sample eps ~ N(0, S_noise^2 * I)
        lowercase_      :Tuple         =			self.config.s_noise * randn_tensor(sample.shape     ,				generator=_SCREAMING_SNAKE_CASE						).to(sample.device						)
        lowercase_      :Any         =			sigma + gamma * sigma
        lowercase_      :str         =			sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

        return sample_hat, sigma_hat





     def 							lowercase__				(			self		: Union[str, Any]     ,				lowercase		: torch.FloatTensor     ,				lowercase		: float     ,				lowercase		: float     ,				lowercase		: torch.FloatTensor     ,				lowercase		: bool = True     ,				):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			sample_hat + sigma_hat * model_output
        lowercase_      :Tuple         =			(sample_hat - pred_original_sample) / sigma_hat
        lowercase_      :int         =			sample_hat + (sigma_prev - sigma_hat) * derivative

        if not return_dict:
           return (sample_prev, derivative)

        return KarrasVeOutput(
            prev_sample=_SCREAMING_SNAKE_CASE     ,				derivative=_SCREAMING_SNAKE_CASE     ,				pred_original_sample=_SCREAMING_SNAKE_CASE						)





     def 							lowercase__				(			self		: Any     ,				lowercase		: torch.FloatTensor     ,				lowercase		: float     ,				lowercase		: float     ,				lowercase		: torch.FloatTensor     ,				lowercase		: torch.FloatTensor     ,				lowercase		: torch.FloatTensor     ,				lowercase		: bool = True     ,				):





        """simple docstring"""




        lowercase_      :Any         =			sample_prev + sigma_prev * model_output
        lowercase_      :Dict         =			(sample_prev - pred_original_sample) / sigma_prev
        lowercase_      :str         =			sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

        if not return_dict:
           return (sample_prev, derivative)

        return KarrasVeOutput(
            prev_sample=_SCREAMING_SNAKE_CASE     ,				derivative=_SCREAMING_SNAKE_CASE     ,				pred_original_sample=_SCREAMING_SNAKE_CASE						)







     def 							lowercase__				(			self		: str     ,				lowercase		: Dict     ,				lowercase		: List[Any]     ,				lowercase		: List[str]						):





        """simple docstring"""




        raise NotImplementedError()









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase :					Optional[int] 							={'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase :					Optional[Any] 							=[
     '''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

 lowerCAmelCase :					Union[str, Any] 							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


