
'''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 				UpperCAmelCase_			:
 UpperCAmelCase_						      =  42

 # setable values
 UpperCAmelCase_						      =  42
 UpperCAmelCase_						      =  42
 UpperCAmelCase_						      =  42
 UpperCAmelCase_						      =  None

 # running values
 UpperCAmelCase_						      =  None





 @classmethod
 def     snake_case__						(      cls	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_):
     return cls(common=lowercase_	,    init_noise_sigma=lowercase_	,    timesteps=lowercase_	,    sigmas=lowercase_)






@dataclass
class 				UpperCAmelCase_			(		snake_case__   ):
 UpperCAmelCase_						      =  42






class 				UpperCAmelCase_			(		snake_case__      ,  snake_case__   ):
 UpperCAmelCase_						      =  [e.name for e in FlaxKarrasDiffusionSchedulers]

 UpperCAmelCase_						      =  42





 @property
 def     snake_case__						(      self):
     return True





 @register_to_config
 def __init__(      self	,    lowercase_ = 10_00	,    lowercase_ = 0.0_001	,    lowercase_ = 0.02	,    lowercase_ = "linear"	,    lowercase_ = None	,    lowercase_ = "epsilon"	,    lowercase_ = jnp.floataa	,    ):
     snake_case_	:      Tuple            =  dtype





 def     snake_case__						(      self	,    lowercase_ = None):
     if common is None:
         snake_case_	:      Tuple            =  CommonSchedulerState.create(self)

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

     # standard deviation of the initial noise distribution
     snake_case_	:      int            =  sigmas.max()

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





 def     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_):
     ((snake_case_)     ,	)	:      List[Any]            =  jnp.where(state.timesteps == timestep	,    size=1)
     snake_case_	:      Optional[Any]            =  step_index[0]

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





 def     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_):

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

     snake_case_	:      Optional[Any]            =  integrate.quad(lowercase_	,    state.sigmas[t]	,    state.sigmas[t + 1]	,    epsrel=1E-4)[0]

     return integrated_coeff





 def     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_ = ()):
     snake_case_	:      Tuple            =  jnp.linspace(self.config.num_train_timesteps - 1	,    0	,    lowercase_	,    dtype=self.dtype)

     snake_case_	:      Optional[Any]            =  jnp.floor(lowercase_).astype(jnp.intaa)
     snake_case_	:      Dict            =  jnp.ceil(lowercase_).astype(jnp.intaa)

     snake_case_	:      Optional[Any]            =  jnp.mod(lowercase_	,    1.0)

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

     snake_case_	:      Union[str, Any]            =  timesteps.astype(jnp.intaa)

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

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





 def     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_ = 4	,    lowercase_ = 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")

     snake_case_	:      int            =  state.sigmas[timestep]

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

     # 3. Compute linear multistep coefficients
     snake_case_	:      Any            =  min(timestep + 1	,    lowercase_)
     snake_case_	:      Tuple            =  [self.get_lms_coefficient(lowercase_	,    lowercase_	,    lowercase_	,    lowercase_) for curr_order in range(lowercase_)]

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

     if not return_dict:
         return (prev_sample, state)

     return FlaxLMSSchedulerOutput(prev_sample=lowercase_	,    state=lowercase_)





 def     snake_case__						(      self	,    lowercase_	,    lowercase_	,    lowercase_	,    lowercase_	,    ):
     snake_case_	:      Optional[Any]            =  state.sigmas[timesteps].flatten()
     snake_case_	:      Optional[Any]            =  broadcast_to_shape_from_left(lowercase_	,    noise.shape)

     snake_case_	:      Dict            =  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_tokenizers_available, is_torch_available


a_     =  {
    "configuration_splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig"],
    "tokenization_splinter": ["SplinterTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_     =  ["SplinterTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_     =  [
      "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SplinterForQuestionAnswering",
      "SplinterForPreTraining",
      "SplinterLayer",
      "SplinterModel",
      "SplinterPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
  from .tokenization_splinter import SplinterTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_splinter_fast import SplinterTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_splinter import (
        SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SplinterForPreTraining,
        SplinterForQuestionAnswering,
        SplinterLayer,
        SplinterModel,
        SplinterPreTrainedModel,
    )


else:
  import sys

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




