


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    __magic_name__						:
     A:		int								=   4_2

     # setable values
     A:		str								=   4_2
     A:		int								=   4_2
     A:		Any								=   4_2
     A:		Union[str, Any]								=   None

     # running values
     A:		Dict								=   None


     @classmethod
     def   UpperCAmelCase__						(   cls				:  List[str]				,						lowerCamelCase__				:  CommonSchedulerState				,						lowerCamelCase__				:  jnp.ndarray				,						lowerCamelCase__				:  jnp.ndarray				,						lowerCamelCase__				:  jnp.ndarray      )	->   Optional[Any]:





      '''simple docstring'''






      return cls(common=lowerCAmelCase_				,						init_noise_sigma=lowerCAmelCase_				,						timesteps=lowerCAmelCase_				,						sigmas=lowerCAmelCase_      )



@dataclass
class    __magic_name__						(  __lowerCAmelCase):
     A:		Union[str, Any]								=   4_2



class    __magic_name__						(  __lowerCAmelCase       ,					__lowerCAmelCase):
     A:		Optional[Any]								=   [e.name for e in FlaxKarrasDiffusionSchedulers]

     A:		Optional[int]								=   4_2


     @property
     def   UpperCAmelCase__						(   self				:  Optional[int]      )	->   Tuple:





      '''simple docstring'''






      return True


     @register_to_config
     def __init__(   self				:  Tuple				,						lowerCamelCase__				:  int = 1000				,						lowerCamelCase__				:  float = 0.0001				,						lowerCamelCase__				:  float = 0.02				,						lowerCamelCase__				:  str = "linear"				,						lowerCamelCase__				:  Optional[jnp.ndarray] = None				,						lowerCamelCase__				:  str = "epsilon"				,						lowerCamelCase__				:  jnp.dtype = jnp.floataa				,						)	->   Dict:





      '''simple docstring'''






      UpperCamelCase__       :     int 							= dtype


     def   UpperCAmelCase__						(   self				:  str				,						lowerCamelCase__				:  Optional[CommonSchedulerState] = None      )	->   LMSDiscreteSchedulerState:





      '''simple docstring'''






      if common is None:
       UpperCamelCase__       :     int 							= CommonSchedulerState.create(self      )

      UpperCamelCase__       :     Any 							= jnp.arange(0				,						self.config.num_train_timesteps      ).round()[::-1]
      UpperCamelCase__       :     List[Any] 							= ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

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

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


     def   UpperCAmelCase__						(   self				:  str				,						lowerCamelCase__				:  LMSDiscreteSchedulerState				,						lowerCamelCase__				:  jnp.ndarray				,						lowerCamelCase__				:  int      )	->   jnp.ndarray:





      '''simple docstring'''






      ((UpperCamelCase__						)							,											)       :     Any 							= jnp.where(state.timesteps == timestep				,						size=1      )
      UpperCamelCase__       :     Any 							= step_index[0]

      UpperCamelCase__       :     Union[str, Any] 							= state.sigmas[step_index]
      UpperCamelCase__       :     Any 							= sample / ((sigma**2 + 1) ** 0.5)
      return sample


     def   UpperCAmelCase__						(   self				:  Optional[int]				,						lowerCamelCase__				:  LMSDiscreteSchedulerState				,						lowerCamelCase__				:  Optional[Any]				,						lowerCamelCase__				:  str				,						lowerCamelCase__				:  Any      )	->   Optional[Any]:





      '''simple docstring'''






      def lms_derivative(lowerCamelCase__				:  Tuple      ):
       UpperCamelCase__       :     Union[str, Any] 							= 1.0
       for k in range(lowerCAmelCase_      ):
        if current_order == k:
         continue
        prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
       return prod

      UpperCamelCase__       :     List[Any] 							= integrate.quad(lowerCAmelCase_				,						state.sigmas[t]				,						state.sigmas[t + 1]				,						epsrel=1E-4      )[0]

      return integrated_coeff


     def   UpperCAmelCase__						(   self				:  str				,						lowerCamelCase__				:  LMSDiscreteSchedulerState				,						lowerCamelCase__				:  int				,						lowerCamelCase__				:  Tuple = ()      )	->   LMSDiscreteSchedulerState:





      '''simple docstring'''






      UpperCamelCase__       :     List[Any] 							= jnp.linspace(self.config.num_train_timesteps - 1				,						0				,						lowerCAmelCase_				,						dtype=self.dtype      )

      UpperCamelCase__       :     List[str] 							= jnp.floor(lowerCAmelCase_      ).astype(jnp.intaa      )
      UpperCamelCase__       :     str 							= jnp.ceil(lowerCAmelCase_      ).astype(jnp.intaa      )

      UpperCamelCase__       :     Optional[int] 							= jnp.mod(lowerCAmelCase_				,						1.0      )

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

      UpperCamelCase__       :     Tuple 							= timesteps.astype(jnp.intaa      )

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

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


     def   UpperCAmelCase__						(   self				:  Optional[int]				,						lowerCamelCase__				:  LMSDiscreteSchedulerState				,						lowerCamelCase__				:  jnp.ndarray				,						lowerCamelCase__				:  int				,						lowerCamelCase__				:  jnp.ndarray				,						lowerCamelCase__				:  int = 4				,						lowerCamelCase__				:  bool = True				,						)	->   Union[FlaxLMSSchedulerOutput, Tuple]:





      '''simple docstring'''






      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'''      )

      UpperCamelCase__       :     List[str] 							= state.sigmas[timestep]

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
       UpperCamelCase__       :     Optional[Any] 							= sample - sigma * model_output
      elif self.config.prediction_type == "v_prediction":
       # * c_out + input * c_skip
       UpperCamelCase__       :     Optional[int] 							= 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
      UpperCamelCase__       :     Dict 							= (sample - pred_original_sample) / sigma
      UpperCamelCase__       :     str 							= state.replace(derivatives=jnp.append(state.derivatives				,						lowerCAmelCase_      )      )
      if len(state.derivatives      ) > order:
       UpperCamelCase__       :     List[Any] 							= state.replace(derivatives=jnp.delete(state.derivatives				,						0      )      )

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

      # 4. Compute previous sample based on the derivatives path
      UpperCamelCase__       :     Dict 							= sample + sum(
          coeff * derivative for coeff, derivative in zip(lowerCAmelCase_				,						reversed(state.derivatives      )      )      )

      if not return_dict:
       return (prev_sample, state)

      return FlaxLMSSchedulerOutput(prev_sample=lowerCAmelCase_				,						state=lowerCAmelCase_      )


     def   UpperCAmelCase__						(   self				:  Any				,						lowerCamelCase__				:  LMSDiscreteSchedulerState				,						lowerCamelCase__				:  jnp.ndarray				,						lowerCamelCase__				:  jnp.ndarray				,						lowerCamelCase__				:  jnp.ndarray				,						)	->   jnp.ndarray:





      '''simple docstring'''






      UpperCamelCase__       :     str 							= state.sigmas[timesteps].flatten()
      UpperCamelCase__       :     Tuple 							= broadcast_to_shape_from_left(lowerCAmelCase_				,						noise.shape      )

      UpperCamelCase__       :     Tuple 							= original_samples + noise * sigma

      return noisy_samples


     def __len__(   self				:  Dict      )	->   Dict:





      '''simple docstring'''






      return self.config.num_train_timesteps




'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A							     = {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A							     = ["MarianTokenizer"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A							     = [
         "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "MarianForCausalLM",
         "MarianModel",
         "MarianMTModel",
         "MarianPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A							     = ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A							     = ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
     from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_marian import MarianTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_marian import (
              MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarianForCausalLM,
              MarianModel,
              MarianMTModel,
              MarianPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
     import sys

     A							     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

