




# 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      UpperCAmelCase						:
   __SCREAMING_SNAKE_CASE		:							Optional[jnp.ndarray]							=					None
   __SCREAMING_SNAKE_CASE		:							Optional[jnp.ndarray]							=					None
   __SCREAMING_SNAKE_CASE		:							Optional[jnp.ndarray]							=					None






   @classmethod
   def  _a						(	cls   :       Dict     )->			Optional[Any]:
     return cls()

@dataclass
class      UpperCAmelCase						(			lowerCAmelCase__   ):
   __SCREAMING_SNAKE_CASE		:							ScoreSdeVeSchedulerState
   __SCREAMING_SNAKE_CASE		:							jnp.ndarray
   __SCREAMING_SNAKE_CASE		:							Optional[jnp.ndarray]							=					None

class      UpperCAmelCase						(			lowerCAmelCase__   ,					lowerCAmelCase__   ):






   @property
   def  _a						(	self   :       List[str]     )->			Dict:
     return True






   @register_to_config
   def __init__(	self   :       str						,					UpperCamelCase_   :       int = 20_00						,					UpperCamelCase_   :       float = 0.15						,					UpperCamelCase_   :       float = 0.01						,					UpperCamelCase_   :       float = 13_48.0						,					UpperCamelCase_   :       float = 1E-5						,					UpperCamelCase_   :       int = 1						,					)->			Optional[Any]:
     pass






   def  _a						(	self   :       Dict     )->			Optional[int]:
     _lowercase: Optional[Any]						     =    ScoreSdeVeSchedulerState.create()
     return self.set_sigmas(
         lowerCAmelCase_						,					self.config.num_train_timesteps						,					self.config.sigma_min						,					self.config.sigma_max						,					self.config.sampling_eps						,					)






   def  _a						(	self   :       str						,					UpperCamelCase_   :       ScoreSdeVeSchedulerState						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       Tuple = ()						,					UpperCamelCase_   :       float = None     )->			ScoreSdeVeSchedulerState:
     _lowercase: Dict						     =    sampling_eps if sampling_eps is not None else self.config.sampling_eps

     _lowercase: Union[str, Any]						     =    jnp.linspace(1						,					lowerCAmelCase_						,					lowerCAmelCase_     )
     return state.replace(timesteps=lowerCAmelCase_     )






   def  _a						(	self   :       Any						,					UpperCamelCase_   :       ScoreSdeVeSchedulerState						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       float = None						,					UpperCamelCase_   :       float = None						,					UpperCamelCase_   :       float = None						,					)->			ScoreSdeVeSchedulerState:
     _lowercase: Optional[int]						     =    sigma_min if sigma_min is not None else self.config.sigma_min
     _lowercase: List[Any]						     =    sigma_max if sigma_max is not None else self.config.sigma_max
     _lowercase: Dict						     =    sampling_eps if sampling_eps is not None else self.config.sampling_eps
     if state.timesteps is None:
       _lowercase: str						     =    self.set_timesteps(lowerCAmelCase_						,					lowerCAmelCase_						,					lowerCAmelCase_     )

     _lowercase: int						     =    jnp.exp(jnp.linspace(jnp.log(lowerCAmelCase_     )						,					jnp.log(lowerCAmelCase_     )						,					lowerCAmelCase_     )     )
     _lowercase: int						     =    jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]     )

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






   def  _a						(	self   :       List[str]						,					UpperCamelCase_   :       Optional[int]						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       Dict     )->			List[Any]:
     return jnp.where(timesteps == 0						,					jnp.zeros_like(lowerCAmelCase_     )						,					state.discrete_sigmas[timesteps - 1]     )






   def  _a						(	self   :       Tuple						,					UpperCamelCase_   :       ScoreSdeVeSchedulerState						,					UpperCamelCase_   :       jnp.ndarray						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       jnp.ndarray						,					UpperCamelCase_   :       random.KeyArray						,					UpperCamelCase_   :       bool = True						,					)->			Union[FlaxSdeVeOutput, Tuple]:






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

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

     _lowercase: int						     =    state.discrete_sigmas[timesteps]
     _lowercase: Optional[int]						     =    self.get_adjacent_sigma(lowerCAmelCase_						,					lowerCAmelCase_						,					lowerCAmelCase_     )
     _lowercase: List[str]						     =    jnp.zeros_like(lowerCAmelCase_     )
     _lowercase: List[Any]						     =    (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
     _lowercase: Tuple						     =    diffusion.flatten()
     _lowercase: Any						     =    broadcast_to_shape_from_left(lowerCAmelCase_						,					sample.shape     )
     _lowercase: int						     =    drift - diffusion**2 * model_output

     #  equation 6: sample noise for the diffusion term of
     _lowercase: List[str]						     =    random.split(lowerCAmelCase_						,					num=1     )
     _lowercase: Any						     =    random.normal(key=lowerCAmelCase_						,					shape=sample.shape     )
     _lowercase: Tuple						     =    sample - drift  # subtract because `dt` is a small negative timestep
     # TODO is the variable diffusion the correct scaling term for the noise?
     _lowercase: Optional[Any]						     =    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=lowerCAmelCase_						,					prev_sample_mean=lowerCAmelCase_						,					state=lowerCAmelCase_     )






   def  _a						(	self   :       List[Any]						,					UpperCamelCase_   :       ScoreSdeVeSchedulerState						,					UpperCamelCase_   :       jnp.ndarray						,					UpperCamelCase_   :       jnp.ndarray						,					UpperCamelCase_   :       random.KeyArray						,					UpperCamelCase_   :       bool = True						,					)->			Union[FlaxSdeVeOutput, Tuple]:






     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
     _lowercase: str						     =    random.split(lowerCAmelCase_						,					num=1     )
     _lowercase: Dict						     =    random.normal(key=lowerCAmelCase_						,					shape=sample.shape     )

     # compute step size from the model_output, the noise, and the snr
     _lowercase: Tuple						     =    jnp.linalg.norm(lowerCAmelCase_     )
     _lowercase: Dict						     =    jnp.linalg.norm(lowerCAmelCase_     )
     _lowercase: int						     =    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
     _lowercase: Any						     =    step_size * jnp.ones(sample.shape[0]     )

     # compute corrected sample: model_output term and noise term
     _lowercase: str						     =    step_size.flatten()
     _lowercase: Any						     =    broadcast_to_shape_from_left(lowerCAmelCase_						,					sample.shape     )
     _lowercase: Dict						     =    sample + step_size * model_output
     _lowercase: Dict						     =    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

     if not return_dict:
       return (prev_sample, state)

     return FlaxSdeVeOutput(prev_sample=lowerCAmelCase_						,					state=lowerCAmelCase_     )






   def __len__(	self   :       Union[str, Any]     )->			Dict:
     return self.config.num_train_timesteps






from typing import TYPE_CHECKING

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


UpperCAmelCase__						:       Union[str, Any]							=    {'configuration_bert_generation': ['BertGenerationConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:       str							=    ['BertGenerationTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__						:       Tuple							=    [
       'BertGenerationDecoder',
       'BertGenerationEncoder',
       'BertGenerationPreTrainedModel',
       'load_tf_weights_in_bert_generation',
   ]


if TYPE_CHECKING:
   from .configuration_bert_generation import BertGenerationConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bert_generation import BertGenerationTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bert_generation import (
          BertGenerationDecoder,
          BertGenerationEncoder,
          BertGenerationPreTrainedModel,
          load_tf_weights_in_bert_generation,
      )

else:
   import sys

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