



"""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       UpperCamelCase__   (	SCREAMING_SNAKE_CASE_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     42
      _SCREAMING_SNAKE_CASE  				=     42
      _SCREAMING_SNAKE_CASE  				=     None







class       UpperCamelCase__   (	SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     2



      @register_to_config
      def __init__(      self				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      float = 0.02    ,			SCREAMING_SNAKE_CASE_				:      float = 1_0_0    ,			SCREAMING_SNAKE_CASE_				:      float = 1.0_07    ,			SCREAMING_SNAKE_CASE_				:      float = 8_0    ,			SCREAMING_SNAKE_CASE_				:      float = 0.05    ,			SCREAMING_SNAKE_CASE_				:      float = 5_0    ,			):
        # standard deviation of the initial noise distribution
        lowerCAmelCase_   :				Tuple								= sigma_max

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



      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      torch.FloatTensor    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None       ):
        return sample



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      Union[str, torch.device] = None       ):
        lowerCAmelCase_   :				Optional[Any]								= num_inference_steps
        lowerCAmelCase_   :				Union[str, Any]								= np.arange(0    ,			self.num_inference_steps       )[::-1].copy()
        lowerCAmelCase_   :				Any								= torch.from_numpy(_a       ).to(_a       )
        lowerCAmelCase_   :				List[Any]								= [
            (
                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
        ]
        lowerCAmelCase_   :				Union[str, Any]								= torch.tensor(_a    ,			dtype=torch.floataa    ,			device=_a       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      torch.FloatTensor    ,			SCREAMING_SNAKE_CASE_				:      float    ,			SCREAMING_SNAKE_CASE_				:      Optional[torch.Generator] = None       ):
        if self.config.s_min <= sigma <= self.config.s_max:
          lowerCAmelCase_   :				Tuple								= min(self.config.s_churn / self.num_inference_steps    ,			2**0.5 - 1       )
        else:
          lowerCAmelCase_   :				int								= 0

        # sample eps ~ N(0, S_noise^2 * I)
        lowerCAmelCase_   :				Optional[Any]								= self.config.s_noise * randn_tensor(sample.shape    ,			generator=_a       ).to(sample.device       )
        lowerCAmelCase_   :				int								= sigma + gamma * sigma
        lowerCAmelCase_   :				int								= sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

        return sample_hat, sigma_hat



      def      SCREAMING_SNAKE_CASE__ (      self				:      str    ,			SCREAMING_SNAKE_CASE_				:      torch.FloatTensor    ,			SCREAMING_SNAKE_CASE_				:      float    ,			SCREAMING_SNAKE_CASE_				:      float    ,			SCREAMING_SNAKE_CASE_				:      torch.FloatTensor    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			):
        lowerCAmelCase_   :				List[Any]								= sample_hat + sigma_hat * model_output
        lowerCAmelCase_   :				Any								= (sample_hat - pred_original_sample) / sigma_hat
        lowerCAmelCase_   :				Union[str, Any]								= sample_hat + (sigma_prev - sigma_hat) * derivative

        if not return_dict:
          return (sample_prev, derivative)

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



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      torch.FloatTensor    ,			SCREAMING_SNAKE_CASE_				:      float    ,			SCREAMING_SNAKE_CASE_				:      float    ,			SCREAMING_SNAKE_CASE_				:      torch.FloatTensor    ,			SCREAMING_SNAKE_CASE_				:      torch.FloatTensor    ,			SCREAMING_SNAKE_CASE_				:      torch.FloatTensor    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			):
        lowerCAmelCase_   :				Any								= sample_prev + sigma_prev * model_output
        lowerCAmelCase_   :				Tuple								= (sample_prev - pred_original_sample) / sigma_prev
        lowerCAmelCase_   :				Dict								= 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=_a    ,			derivative=_a    ,			pred_original_sample=_a       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      str       ):
        raise NotImplementedError()










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_UpperCamelCase											=						{'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						['SpeechEncoderDecoderModel']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
   from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
   import sys

   _UpperCamelCase											=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






