


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
 __lowercase :							Optional[int]        = None
 __lowercase :							Optional[jnp.ndarray]        = None
 __lowercase :							Optional[jnp.ndarray]        = None  # sigma(t_i)
 @classmethod
 def 		SCREAMING_SNAKE_CASE__   (   cls )					->				Any:




        '''simple docstring'''
        return cls()





@dataclass
class   __a			(    __UpperCamelCase      ):
 __lowercase :							jnp.ndarray
 __lowercase :							jnp.ndarray
 __lowercase :							KarrasVeSchedulerState





class   __a			(    __UpperCamelCase       ,       __UpperCamelCase      ):
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Any:




        '''simple docstring'''
        return True
 @register_to_config
 def __init__(   self				, lowerCAmelCase__ = 0.0_2				, lowerCAmelCase__ = 100				, lowerCAmelCase__ = 1.0_0_7				, lowerCAmelCase__ = 80				, lowerCAmelCase__ = 0.0_5				, lowerCAmelCase__ = 50				, )					->				List[Any]:




        '''simple docstring'''
        pass
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        return KarrasVeSchedulerState.create()
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = () )					->				KarrasVeSchedulerState:




        '''simple docstring'''
        lowercase__:   Tuple  			=   jnp.arange(0				, lowerCAmelCase__ )[::-1].copy()
        lowercase__:   Optional[Any]  			=   [
            (
                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 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, )					->				Tuple[jnp.ndarray, float]:




        '''simple docstring'''
        if self.config.s_min <= sigma <= self.config.s_max:
               lowercase__:   List[Any]  			=   min(self.config.s_churn / state.num_inference_steps				, 2**0.5 - 1 )
        else:
               lowercase__:   Optional[Any]  			=   0

        # sample eps ~ N(0, S_noise^2 * I)
        lowercase__:   List[str]  			=   random.split(lowerCAmelCase__				, num=1 )
        lowercase__:   Dict  			=   self.config.s_noise * random.normal(key=lowerCAmelCase__				, shape=sample.shape )
        lowercase__:   Optional[Any]  			=   sigma + gamma * sigma
        lowercase__:   List[str]  			=   sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

        return sample_hat, sigma_hat
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = True				, )					->				Union[FlaxKarrasVeOutput, Tuple]:




        '''simple docstring'''
        lowercase__:   Dict  			=   sample_hat + sigma_hat * model_output
        lowercase__:   Tuple  			=   (sample_hat - pred_original_sample) / sigma_hat
        lowercase__:   Union[str, Any]  			=   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 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = True				, )					->				Union[FlaxKarrasVeOutput, Tuple]:




        '''simple docstring'''
        lowercase__:   List[Any]  			=   sample_prev + sigma_prev * model_output
        lowercase__:   Any  			=   (sample_prev - pred_original_sample) / sigma_prev
        lowercase__:   Optional[int]  			=   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 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        raise NotImplementedError()






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__lowerCAmelCase		       =							{
    '''configuration_encodec''': [
        '''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EncodecConfig''',
    ],
    '''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase		       =							[
     '''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''EncodecModel''',
     '''EncodecPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_encodec import (
     ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
     EncodecConfig,
 )
 from .feature_extraction_encodec import EncodecFeatureExtractor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_encodec import (
      ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
      EncodecModel,
      EncodecPreTrainedModel,
  )

else:
 import sys

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


