


'''simple docstring'''


from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any







class        __magic_name__  :

     """simple docstring"""






     lowerCamelCase__						=							('_front', '_back', '_len')







     @dataclass
     class        __magic_name__  :

          """simple docstring"""






          lowerCamelCase__						=							None
          lowerCamelCase__						=							None
          lowerCamelCase__						=							None







     class        __magic_name__  :

          """simple docstring"""






          lowerCamelCase__						=							('_cur',)


          def __init__(      self ,    lowerCamelCase		):


               '''simple docstring'''





               __A  :					Tuple						     =       cur


          def __iter__(      self		):


               '''simple docstring'''





               return self


          def lowerCAmelCase__     (      self		):


               '''simple docstring'''





               if self._cur is None:
                    # finished iterating
                    raise StopIteration
               __A  :					Optional[int]						     =       self._cur.val
               __A  :					Union[str, Any]						     =       self._cur.next_node

               return val


     def __init__(      self ,    lowerCamelCase = None		):


          '''simple docstring'''





          __A  :					Any						     =       None
          __A  :					Any						     =       None
          __A  :					int						     =       0

          if iterable is not None:
               # append every value to the deque
               for val in iterable:
                    self.append(lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					str						     =       self._Node(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)
          if self.is_empty():
               # front = back
               __A  :					int						     =       node
               __A  :					Tuple						     =       1
          else:
               # connect nodes
               __A  :					str						     =       node
               __A  :					str						     =       self._back
               __A  :					List[str]						     =       node  # assign new back to the new node

               self._len += 1

               # make sure there were no errors
               assert not self.is_empty(), "Error on appending value."


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Tuple						     =       self._Node(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)
          if self.is_empty():
               # front = back
               __A  :					Optional[int]						     =       node
               __A  :					Optional[int]						     =       1
          else:
               # connect nodes
               __A  :					Any						     =       self._front
               __A  :					List[Any]						     =       node
               __A  :					Any						     =       node  # assign new front to the new node

               self._len += 1

               # make sure there were no errors
               assert not self.is_empty(), "Error on appending value."


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          for val in iterable:
               self.append(lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          for val in iterable:
               self.appendleft(lowerCamelCase		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          assert not self.is_empty(), "Deque is empty."

          __A  :					Dict						     =       self._back
          __A  :					Tuple						     =       self._back.prev_node  # set new back
          # drop the last node - python will deallocate memory automatically
          __A  :					List[Any]						     =       None

          self._len -= 1

          return topop.val


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          assert not self.is_empty(), "Deque is empty."

          __A  :					Optional[int]						     =       self._front
          __A  :					Any						     =       self._front.next_node  # set new front and drop the first node
          __A  :					Optional[Any]						     =       None

          self._len -= 1

          return topop.val


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return self._front is None


     def __len__(      self		):


          '''simple docstring'''





          return self._len


     def __eq__(      self ,    lowerCamelCase		):


          '''simple docstring'''





          if not isinstance(lowerCamelCase ,    lowerCamelCase		):
               return NotImplemented

          __A  :					Tuple						     =       self._front
          __A  :					int						     =       other._front

          # if the length of the dequeues are not the same, they are not equal
          if len(self		) != len(lowerCamelCase		):
               return False

          while me is not None and oth is not None:
               # compare every value
               if me.val != oth.val:
                    return False
               __A  :					Any						     =       me.next_node
               __A  :					Any						     =       oth.next_node

          return True


     def __iter__(      self		):


          '''simple docstring'''





          return Deque._Iterator(self._front		)


     def __repr__(      self		):


          '''simple docstring'''





          __A  :					Any						     =       []
          __A  :					List[str]						     =       self._front
          while aux is not None:
               # append the values in a list to display
               values_list.append(aux.val		)
               __A  :					List[str]						     =       aux.next_node

          return f"[{', '.join(repr(lowerCamelCase		) for val in values_list		)}]"


if __name__ == "__main__":
      import doctest

      doctest.testmod()





'''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        __magic_name__  :

     """simple docstring"""






     lowerCamelCase__						=							None
     lowerCamelCase__						=							None
     lowerCamelCase__						=							None


     @classmethod
     def lowerCAmelCase__     (      cls		):


          '''simple docstring'''





          return cls()







@dataclass
class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							42
     lowerCamelCase__						=							42
     lowerCamelCase__						=							None







class        __magic_name__  (					lowerCAmelCase						,		lowerCAmelCase   ):

     """simple docstring"""






     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return True


     @register_to_config
     def __init__(      self ,    lowerCamelCase = 2000 ,    lowerCamelCase = 0.15 ,    lowerCamelCase = 0.01 ,    lowerCamelCase = 1348.0 ,    lowerCamelCase = 1E-5 ,    lowerCamelCase = 1 ,    ):


          '''simple docstring'''





          pass


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					List[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 lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = () ,    lowerCamelCase = None		):


          '''simple docstring'''





          __A  :					Tuple						     =       sampling_eps if sampling_eps is not None else self.config.sampling_eps

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


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = None ,    lowerCamelCase = None ,    lowerCamelCase = None ,    ):


          '''simple docstring'''





          __A  :					str						     =       sigma_min if sigma_min is not None else self.config.sigma_min
          __A  :					Union[str, Any]						     =       sigma_max if sigma_max is not None else self.config.sigma_max
          __A  :					Dict						     =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
          if state.timesteps is None:
               __A  :					Union[str, Any]						     =       self.set_timesteps(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)

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

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


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = 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"		)

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

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

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


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = 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
          __A  :					int						     =       random.split(lowerCamelCase ,    num=1		)
          __A  :					int						     =       random.normal(key=lowerCamelCase ,    shape=sample.shape		)

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

          # compute corrected sample: model_output term and noise term
          __A  :					Optional[Any]						     =       step_size.flatten()
          __A  :					Optional[int]						     =       broadcast_to_shape_from_left(lowerCamelCase ,    sample.shape		)
          __A  :					Dict						     =       sample + step_size * model_output
          __A  :					List[Any]						     =       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		):


          '''simple docstring'''





          return self.config.num_train_timesteps

