
'''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        UpperCAmelCase_       :

 '''simple docstring'''
 # setable values
 lowercase_				:						Optional[jnp.ndarray]				=       None
 lowercase_				:						Optional[jnp.ndarray]				=       None
 lowercase_				:						Optional[jnp.ndarray]				=       None







 @classmethod
 def        UpperCamelCase     (				cls		:     List[str]   ):



   '''simple docstring'''




   return cls()






@dataclass
class        UpperCAmelCase_       (						A							):

 '''simple docstring'''
 lowercase_				:						ScoreSdeVeSchedulerState
 lowercase_				:						jnp.ndarray
 lowercase_				:						Optional[jnp.ndarray]				=       None






class        UpperCAmelCase_       (						A				,							A							):

 '''simple docstring'''







 @property
 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




   return True







 @register_to_config
 def __init__(				self		:     Optional[Any] ,							snake_case__		:     int = 20_00 ,							snake_case__		:     float = 0.15 ,							snake_case__		:     float = 0.01 ,							snake_case__		:     float = 1348.0 ,							snake_case__		:     float = 1e-5 ,							snake_case__		:     int = 1 ,							):



   '''simple docstring'''




   pass







 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Union[str, Any]												=       ScoreSdeVeSchedulerState.create()
   return self.set_sigmas(
       snake_case__ ,							self.config.num_train_timesteps ,							self.config.sigma_min ,							self.config.sigma_max ,							self.config.sampling_eps ,							)







 def        UpperCamelCase     (				self		:     List[Any] ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     int ,							snake_case__		:     Tuple = () ,							snake_case__		:     float = None   ):



   '''simple docstring'''




   UpperCAmelCase__			:				str												=       sampling_eps if sampling_eps is not None else self.config.sampling_eps

   UpperCAmelCase__			:				Optional[int]												=       jnp.linspace(1 ,							snake_case__ ,							snake_case__   )
   return state.replace(timesteps=snake_case__   )







 def        UpperCamelCase     (				self		:     str ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     int ,							snake_case__		:     float = None ,							snake_case__		:     float = None ,							snake_case__		:     float = None ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       sigma_min if sigma_min is not None else self.config.sigma_min
   UpperCAmelCase__			:				Dict												=       sigma_max if sigma_max is not None else self.config.sigma_max
   UpperCAmelCase__			:				Any												=       sampling_eps if sampling_eps is not None else self.config.sampling_eps
   if state.timesteps is None:
     UpperCAmelCase__			:				Optional[int]												=       self.set_timesteps(snake_case__ ,							snake_case__ ,							snake_case__   )

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

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







 def        UpperCamelCase     (				self		:     List[str] ,							snake_case__		:     str ,							snake_case__		:     Tuple ,							snake_case__		:     Dict   ):



   '''simple docstring'''




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







 def        UpperCamelCase     (				self		:     Optional[int] ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     int ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     random.KeyArray ,							snake_case__		:     bool = True ,							):



   '''simple docstring'''




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

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

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

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







 def        UpperCamelCase     (				self		:     Any ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     random.KeyArray ,							snake_case__		:     bool = True ,							):



   '''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
   UpperCAmelCase__			:				str												=       random.split(snake_case__ ,							num=1   )
   UpperCAmelCase__			:				Tuple												=       random.normal(key=snake_case__ ,							shape=sample.shape   )

   # compute step size from the model_output, the noise, and the snr
   UpperCAmelCase__			:				Union[str, Any]												=       jnp.linalg.norm(snake_case__   )
   UpperCAmelCase__			:				List[str]												=       jnp.linalg.norm(snake_case__   )
   UpperCAmelCase__			:				Optional[Any]												=       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
   UpperCAmelCase__			:				str												=       step_size * jnp.ones(sample.shape[0]   )

   # compute corrected sample: model_output term and noise term
   UpperCAmelCase__			:				Union[str, Any]												=       step_size.flatten()
   UpperCAmelCase__			:				Optional[int]												=       broadcast_to_shape_from_left(snake_case__ ,							sample.shape   )
   UpperCAmelCase__			:				Tuple												=       sample + step_size * model_output
   UpperCAmelCase__			:				Any												=       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

   if not return_dict:
     return (prev_sample, state)

   return FlaxSdeVeOutput(prev_sample=snake_case__ ,							state=snake_case__   )







 def __len__(				self		:     List[str]   ):



   '''simple docstring'''




   return self.config.num_train_timesteps





'''simple docstring'''





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE    =					{
    """configuration_donut_swin""": ["""DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DonutSwinConfig"""],
    """processing_donut""": ["""DonutProcessor"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =					[
          """DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """DonutSwinModel""",
          """DonutSwinPreTrainedModel""",
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =					["""DonutFeatureExtractor"""]
      SCREAMING_SNAKE_CASE    =					["""DonutImageProcessor"""]


if TYPE_CHECKING:
      from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
      from .processing_donut import DonutProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_donut_swin import (
                DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                DonutSwinModel,
                DonutSwinPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_donut import DonutFeatureExtractor
            from .image_processing_donut import DonutImageProcessor

else:
      import sys

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



