
'''simple docstring'''

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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)


@flax.struct.dataclass
class 		_a     :







   """simple docstring"""




   A		= 42

   # setable values
   A		= 42
   A		= 42
   A		= 42
   A		= None

   # running values
   A		= None






   @classmethod
   def 					__a			(       cls    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    return cls(common=__SCREAMING_SNAKE_CASE    ,init_noise_sigma=__SCREAMING_SNAKE_CASE    ,timesteps=__SCREAMING_SNAKE_CASE    ,sigmas=__SCREAMING_SNAKE_CASE      )



@dataclass
class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   A		= 42



class 		_a     (							SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   A		= [e.name for e in FlaxKarrasDiffusionSchedulers]

   A		= 42






   @property
   def 					__a			(       self      ):
    return True






   @register_to_config
   def __init__(       self    ,__SCREAMING_SNAKE_CASE = 1000    ,__SCREAMING_SNAKE_CASE = 0.0001    ,__SCREAMING_SNAKE_CASE = 0.02    ,__SCREAMING_SNAKE_CASE = "linear"    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = "epsilon"    ,__SCREAMING_SNAKE_CASE = jnp.floataa    ,):
    SCREAMING_SNAKE_CASE  :  List[str]								=		dtype






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE = None      ):
    if common is None:
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		CommonSchedulerState.create(self      )

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		jnp.arange(0    ,self.config.num_train_timesteps      ).round()[::-1]
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

    # standard deviation of the initial noise distribution
    SCREAMING_SNAKE_CASE  :  List[Any]								=		sigmas.max()

    return LMSDiscreteSchedulerState.create(
        common=__SCREAMING_SNAKE_CASE    ,init_noise_sigma=__SCREAMING_SNAKE_CASE    ,timesteps=__SCREAMING_SNAKE_CASE    ,sigmas=__SCREAMING_SNAKE_CASE    ,)






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    ((SCREAMING_SNAKE_CASE)				, )  :  List[str]								=		jnp.where(state.timesteps == timestep    ,size=1      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		step_index[0]

    SCREAMING_SNAKE_CASE  :  int								=		state.sigmas[step_index]
    SCREAMING_SNAKE_CASE  :  str								=		sample / ((sigma**2 + 1) ** 0.5)
    return sample






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):

    def lms_derivative(__SCREAMING_SNAKE_CASE      ):
     SCREAMING_SNAKE_CASE  :  str								=		1.0
     for k in range(__SCREAMING_SNAKE_CASE      ):
      if current_order == k:
       continue
      prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
     return prod

    SCREAMING_SNAKE_CASE  :  Tuple								=		integrate.quad(__SCREAMING_SNAKE_CASE    ,state.sigmas[t]    ,state.sigmas[t + 1]    ,epsrel=1e-4      )[0]

    return integrated_coeff






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = ()      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		jnp.linspace(self.config.num_train_timesteps - 1    ,0    ,__SCREAMING_SNAKE_CASE    ,dtype=self.dtype      )

    SCREAMING_SNAKE_CASE  :  Any								=		jnp.floor(__SCREAMING_SNAKE_CASE      ).astype(jnp.intaa      )
    SCREAMING_SNAKE_CASE  :  int								=		jnp.ceil(__SCREAMING_SNAKE_CASE      ).astype(jnp.intaa      )

    SCREAMING_SNAKE_CASE  :  int								=		jnp.mod(__SCREAMING_SNAKE_CASE    ,1.0      )

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
    SCREAMING_SNAKE_CASE  :  int								=		(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		jnp.concatenate([sigmas, jnp.array([0.0]    ,dtype=self.dtype      )]      )

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		timesteps.astype(jnp.intaa      )

    # initial running values
    SCREAMING_SNAKE_CASE  :  Tuple								=		jnp.zeros((0,) + shape    ,dtype=self.dtype      )

    return state.replace(
        timesteps=__SCREAMING_SNAKE_CASE    ,sigmas=__SCREAMING_SNAKE_CASE    ,num_inference_steps=__SCREAMING_SNAKE_CASE    ,derivatives=__SCREAMING_SNAKE_CASE    ,)






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = 4    ,__SCREAMING_SNAKE_CASE = True    ,):
    if state.num_inference_steps is None:
     raise ValueError(
         'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		state.sigmas[timestep]

    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
    if self.config.prediction_type == "epsilon":
     SCREAMING_SNAKE_CASE  :  str								=		sample - sigma * model_output
    elif self.config.prediction_type == "v_prediction":
     # * c_out + input * c_skip
     SCREAMING_SNAKE_CASE  :  Dict								=		model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
    else:
     raise ValueError(
         f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""      )

    # 2. Convert to an ODE derivative
    SCREAMING_SNAKE_CASE  :  Dict								=		(sample - pred_original_sample) / sigma
    SCREAMING_SNAKE_CASE  :  str								=		state.replace(derivatives=jnp.append(state.derivatives    ,__SCREAMING_SNAKE_CASE      )      )
    if len(state.derivatives      ) > order:
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		state.replace(derivatives=jnp.delete(state.derivatives    ,0      )      )

    # 3. Compute linear multistep coefficients
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		min(timestep + 1    ,__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		[self.get_lms_coefficient(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ) for curr_order in range(__SCREAMING_SNAKE_CASE      )]

    # 4. Compute previous sample based on the derivatives path
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		sample + sum(
        coeff * derivative for coeff, derivative in zip(__SCREAMING_SNAKE_CASE    ,reversed(state.derivatives      )      )      )

    if not return_dict:
     return (prev_sample, state)

    return FlaxLMSSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE    ,state=__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,):
    SCREAMING_SNAKE_CASE  :  Any								=		state.sigmas[timesteps].flatten()
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE    ,noise.shape      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		original_samples + noise * sigma

    return noisy_samples






   def __len__(       self      ):
    return self.config.num_train_timesteps




'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase												=   {'configuration_sew_d': ['SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWDConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase												=   [
       'SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST',
       'SEWDForCTC',
       'SEWDForSequenceClassification',
       'SEWDModel',
       'SEWDPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_sew_d import (
          SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
          SEWDForCTC,
          SEWDForSequenceClassification,
          SEWDModel,
          SEWDPreTrainedModel,
      )


else:
   import sys

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


