




'''simple docstring'''



# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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 .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left





@flax.struct.dataclass
class       lowerCAmelCase				:
       # setable values
       __lowercase					: Optional[jnp.ndarray]	    =     None
       __lowercase					: Optional[jnp.ndarray]	    =     None
       __lowercase					: Optional[jnp.ndarray]	    =     None


       @classmethod
       def  lowerCAmelCase (				cls					) ->					Tuple:



            '''simple docstring'''




            return cls()





@dataclass
class       lowerCAmelCase				(	__lowerCAmelCase):
       __lowercase					: ScoreSdeVeSchedulerState
       __lowercase					: jnp.ndarray
       __lowercase					: Optional[jnp.ndarray]	    =     None





class       lowerCAmelCase				(	__lowerCAmelCase			,				__lowerCAmelCase):


       @property
       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




            return True


       @register_to_config
       def __init__(				self      ,					__SCREAMING_SNAKE_CASE = 2000      ,					__SCREAMING_SNAKE_CASE = 0.15      ,					__SCREAMING_SNAKE_CASE = 0.01      ,					__SCREAMING_SNAKE_CASE = 1_348.0      ,					__SCREAMING_SNAKE_CASE = 1E-5      ,					__SCREAMING_SNAKE_CASE = 1      ,					) ->					Union[str, Any]:



            '''simple docstring'''




            pass


       def  lowerCAmelCase (				self					) ->					Optional[Any]:



            '''simple docstring'''




            __snake_case         =					ScoreSdeVeSchedulerState.create()
            return self.set_sigmas(
                __SCREAMING_SNAKE_CASE      ,					self.config.num_train_timesteps      ,					self.config.sigma_min      ,					self.config.sigma_max      ,					self.config.sampling_eps      ,					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = ()      ,					__SCREAMING_SNAKE_CASE = None					) ->					ScoreSdeVeSchedulerState:



            '''simple docstring'''




            __snake_case         =					sampling_eps if sampling_eps is not None else self.config.sampling_eps

            __snake_case         =					jnp.linspace(1      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)
            return state.replace(timesteps=__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = None      ,					__SCREAMING_SNAKE_CASE = None      ,					) ->					ScoreSdeVeSchedulerState:



            '''simple docstring'''




            __snake_case         =					sigma_min if sigma_min is not None else self.config.sigma_min
            __snake_case         =					sigma_max if sigma_max is not None else self.config.sigma_max
            __snake_case         =					sampling_eps if sampling_eps is not None else self.config.sampling_eps
            if state.timesteps is None:
                 __snake_case         =					self.set_timesteps(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)

            __snake_case         =					jnp.exp(jnp.linspace(jnp.log(__SCREAMING_SNAKE_CASE					)      ,					jnp.log(__SCREAMING_SNAKE_CASE					)      ,					__SCREAMING_SNAKE_CASE					)					)
            __snake_case         =					jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]					)

            return state.replace(discrete_sigmas=__SCREAMING_SNAKE_CASE      ,					sigmas=__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Union[str, Any]:



            '''simple docstring'''




            return jnp.where(timesteps == 0      ,					jnp.zeros_like(__SCREAMING_SNAKE_CASE					)      ,					state.discrete_sigmas[timesteps - 1]					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = True      ,					) ->					Union[FlaxSdeVeOutput, Tuple]:



            '''simple docstring'''




            if state.timesteps is None:
                 raise ValueError(
                     '''`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''					)

            __snake_case         =					timestep * jnp.ones(
                sample.shape[0]      ,					)
            __snake_case         =					(timestep * (len(state.timesteps					) - 1)).long()

            __snake_case         =					state.discrete_sigmas[timesteps]
            __snake_case         =					self.get_adjacent_sigma(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					)
            __snake_case         =					jnp.zeros_like(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					(sigma**2 - adjacent_sigma**2) ** 0.5

            # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
            # also equation 47 shows the analog from SDE models to ancestral sampling methods
            __snake_case         =					diffusion.flatten()
            __snake_case         =					broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE      ,					sample.shape					)
            __snake_case         =					drift - diffusion**2 * model_output

            #  equation 6: sample noise for the diffusion term of
            __snake_case         =					random.split(__SCREAMING_SNAKE_CASE      ,					num=1					)
            __snake_case         =					random.normal(key=__SCREAMING_SNAKE_CASE      ,					shape=sample.shape					)
            __snake_case         =					sample - drift  # subtract because `dt` is a small negative timestep
            # TODO is the variable diffusion the correct scaling term for the noise?
            __snake_case         =					prev_sample_mean + diffusion * noise  # add impact of diffusion field g

            if not return_dict:
                 return (prev_sample, prev_sample_mean, state)

            return FlaxSdeVeOutput(prev_sample=__SCREAMING_SNAKE_CASE      ,					prev_sample_mean=__SCREAMING_SNAKE_CASE      ,					state=__SCREAMING_SNAKE_CASE					)


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE = True      ,					) ->					Union[FlaxSdeVeOutput, Tuple]:



            '''simple docstring'''




            if state.timesteps is None:
                 raise ValueError(
                     '''`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''					)

            # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
            # sample noise for correction
            __snake_case         =					random.split(__SCREAMING_SNAKE_CASE      ,					num=1					)
            __snake_case         =					random.normal(key=__SCREAMING_SNAKE_CASE      ,					shape=sample.shape					)

            # compute step size from the model_output, the noise, and the snr
            __snake_case         =					jnp.linalg.norm(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					jnp.linalg.norm(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					(self.config.snr * noise_norm / grad_norm) ** 2 * 2
            __snake_case         =					step_size * jnp.ones(sample.shape[0]					)

            # compute corrected sample: model_output term and noise term
            __snake_case         =					step_size.flatten()
            __snake_case         =					broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE      ,					sample.shape					)
            __snake_case         =					sample + step_size * model_output
            __snake_case         =					prev_sample_mean + ((step_size * 2) ** 0.5) * noise

            if not return_dict:
                 return (prev_sample, state)

            return FlaxSdeVeOutput(prev_sample=__SCREAMING_SNAKE_CASE      ,					state=__SCREAMING_SNAKE_CASE					)




       def __len__(				self					) ->					Optional[int]:



            '''simple docstring'''




            return self.config.num_train_timesteps












'''simple docstring'''



from typing import TYPE_CHECKING

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


UpperCAmelCase_    :					Any					      =						{
    '''configuration_retribert''': ['''RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RetriBertConfig'''],
    '''tokenization_retribert''': ['''RetriBertTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_    :					str					      =						['''RetriBertTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_    :					Optional[int]					      =						[
         '''RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''RetriBertModel''',
         '''RetriBertPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
     from .tokenization_retribert import RetriBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_retribert_fast import RetriBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_retribert import (
              RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              RetriBertModel,
              RetriBertPreTrainedModel,
          )

else:
     import sys

     UpperCAmelCase_    :					Any					      =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






