






'''simple docstring'''







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 					a__    :
     '''simple docstring'''







     _snake_case             =  None
     _snake_case             =  None
     _snake_case             =  None






     @classmethod
     def     _UpperCamelCase   (cls    :	Optional[int]					)  ->       int:
           return cls()







@dataclass
class 					a__    (			UpperCAmelCase_						):
     '''simple docstring'''







     _snake_case             =  42
     _snake_case             =  42
     _snake_case             =  None







class 					a__    (			UpperCAmelCase_ ,						UpperCAmelCase_						):
     '''simple docstring'''







     @property
     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       Dict:
           return True






     @register_to_config
     def __init__(self    :	Optional[int]				,				lowercase__    :	Optional[Any] = 2000				,				lowercase__    :	int = 0.15				,				lowercase__    :	List[str] = 0.01				,				lowercase__    :	str = 1348.0				,				lowercase__    :	Tuple = 1e-5				,				lowercase__    :	Optional[Any] = 1				,				)  ->       Union[str, Any]:
           pass






     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       Optional[int]:
           __A		   =      ScoreSdeVeSchedulerState.create()
           return self.set_sigmas(
               __UpperCamelCase				,				self.config.num_train_timesteps				,				self.config.sigma_min				,				self.config.sigma_max				,				self.config.sampling_eps				,				)






     def     _UpperCamelCase   (self    :	List[Any]				,				lowercase__    :	Dict				,				lowercase__    :	List[Any]				,				lowercase__    :	str = ()				,				lowercase__    :	Union[str, Any] = None					)  ->       int:
           __A		   =      sampling_eps if sampling_eps is not None else self.config.sampling_eps

           __A		   =      jnp.linspace(1				,				__UpperCamelCase				,				__UpperCamelCase					)
           return state.replace(timesteps=__UpperCamelCase					)






     def     _UpperCamelCase   (self    :	Optional[Any]				,				lowercase__    :	List[Any]				,				lowercase__    :	List[Any]				,				lowercase__    :	Optional[Any] = None				,				lowercase__    :	List[str] = None				,				lowercase__    :	Optional[int] = None				,				)  ->       Dict:
           __A		   =      sigma_min if sigma_min is not None else self.config.sigma_min
           __A		   =      sigma_max if sigma_max is not None else self.config.sigma_max
           __A		   =      sampling_eps if sampling_eps is not None else self.config.sampling_eps
           if state.timesteps is None:
                 __A		   =      self.set_timesteps(__UpperCamelCase				,				__UpperCamelCase				,				__UpperCamelCase					)

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

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






     def     _UpperCamelCase   (self    :	Union[str, Any]				,				lowercase__    :	Optional[Any]				,				lowercase__    :	List[Any]				,				lowercase__    :	Dict					)  ->       str:
           return jnp.where(timesteps == 0				,				jnp.zeros_like(__UpperCamelCase					)				,				state.discrete_sigmas[timesteps - 1]					)






     def     _UpperCamelCase   (self    :	List[Any]				,				lowercase__    :	Tuple				,				lowercase__    :	List[str]				,				lowercase__    :	Union[str, Any]				,				lowercase__    :	Dict				,				lowercase__    :	Tuple				,				lowercase__    :	Optional[Any] = True				,				)  ->       List[Any]:
           if state.timesteps is None:
                 raise ValueError(
                     "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"					)

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

           __A		   =      state.discrete_sigmas[timesteps]
           __A		   =      self.get_adjacent_sigma(__UpperCamelCase				,				__UpperCamelCase				,				__UpperCamelCase					)
           __A		   =      jnp.zeros_like(__UpperCamelCase					)
           __A		   =      (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
           __A		   =      diffusion.flatten()
           __A		   =      broadcast_to_shape_from_left(__UpperCamelCase				,				sample.shape					)
           __A		   =      drift - diffusion**2 * model_output

           #  equation 6: sample noise for the diffusion term of
           __A		   =      random.split(__UpperCamelCase				,				num=1					)
           __A		   =      random.normal(key=__UpperCamelCase				,				shape=sample.shape					)
           __A		   =      sample - drift  # subtract because `dt` is a small negative timestep
           # TODO is the variable diffusion the correct scaling term for the noise?
           __A		   =      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=__UpperCamelCase				,				prev_sample_mean=__UpperCamelCase				,				state=__UpperCamelCase					)






     def     _UpperCamelCase   (self    :	Optional[Any]				,				lowercase__    :	List[Any]				,				lowercase__    :	List[Any]				,				lowercase__    :	Dict				,				lowercase__    :	Dict				,				lowercase__    :	List[str] = True				,				)  ->       Dict:
           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
           __A		   =      random.split(__UpperCamelCase				,				num=1					)
           __A		   =      random.normal(key=__UpperCamelCase				,				shape=sample.shape					)

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

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

           if not return_dict:
                 return (prev_sample, state)

           return FlaxSdeVeOutput(prev_sample=__UpperCamelCase				,				state=__UpperCamelCase					)






     def __len__(self    :	Dict					)  ->       Any:
           return self.config.num_train_timesteps










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A      :       str      =	{'configuration_umt5': ['UMT5Config', 'UMT5OnnxConfig']}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A      :       str      =	[
           'UMT5EncoderModel',
           'UMT5ForConditionalGeneration',
           'UMT5ForQuestionAnswering',
           'UMT5Model',
           'UMT5PreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_umta import UMTaConfig, UMTaOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_umta import (
                  UMTaEncoderModel,
                  UMTaForConditionalGeneration,
                  UMTaForQuestionAnswering,
                  UMTaModel,
                  UMTaPreTrainedModel,
              )
else:
       import sys

       __A      :       Optional[int]      =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


