code: 
  
  
  
  
  
  '''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'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    None
  		__lowerCAmelCase 				=    None
  		__lowerCAmelCase 				=    None
  
  
  
  
  
  
  
  		@classmethod
  		def        A     (cls						:      int      ):
  			return cls()
  
  
  
  
  
  
  @dataclass
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    42
  		__lowerCAmelCase 				=    42
  		__lowerCAmelCase 				=    None
  
  
  
  
  
  
  class 							__UpperCAmelCase  (       A__		,					A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      Tuple      ):
  			return True
  
  
  
  
  
  
  
  		@register_to_config
  		def __init__(self						:      Union[str, Any]      ,      _lowerCAmelCase						:      int = 2000      ,      _lowerCAmelCase						:      float = 0.15      ,      _lowerCAmelCase						:      float = 0.01      ,      _lowerCAmelCase						:      float = 1_348.0      ,      _lowerCAmelCase						:      float = 1e-5      ,      _lowerCAmelCase						:      int = 1      ,      ):
  			pass
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[Any]      ):
  			A							  =      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						:      Optional[Any]      ,      _lowerCAmelCase						:      ScoreSdeVeSchedulerState      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      Tuple = ()      ,      _lowerCAmelCase						:      float = None      ):
  			A							  =      sampling_eps if sampling_eps is not None else self.config.sampling_eps
  
  			A							  =      jnp.linspace(1      ,      _lowerCAmelCase      ,      _lowerCAmelCase      )
  			return state.replace(timesteps=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      int      ,      _lowerCAmelCase						:      ScoreSdeVeSchedulerState      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      float = None      ,      _lowerCAmelCase						:      float = None      ,      _lowerCAmelCase						:      float = None      ,      ):
  			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(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      )
  
  			A							  =      jnp.exp(jnp.linspace(jnp.log(_lowerCAmelCase      )      ,      jnp.log(_lowerCAmelCase      )      ,      _lowerCAmelCase      )      )
  			A							  =      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						:      Optional[Any]      ,      _lowerCAmelCase						:      Dict      ,      _lowerCAmelCase						:      Dict      ,      _lowerCAmelCase						:      Union[str, Any]      ):
  			return jnp.where(timesteps == 0      ,      jnp.zeros_like(_lowerCAmelCase      )      ,      state.discrete_sigmas[timesteps - 1]      )
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[Any]      ,      _lowerCAmelCase						:      ScoreSdeVeSchedulerState      ,      _lowerCAmelCase						:      jnp.ndarray      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      jnp.ndarray      ,      _lowerCAmelCase						:      random.KeyArray      ,      _lowerCAmelCase						:      bool = True      ,      ):
  			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(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      )
  			A							  =      jnp.zeros_like(_lowerCAmelCase      )
  			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(_lowerCAmelCase      ,      sample.shape      )
  			A							  =      drift - diffusion**2 * model_output
  
  			#  equation 6: sample noise for the diffusion term of
  			A							  =      random.split(_lowerCAmelCase      ,      num=1      )
  			A							  =      random.normal(key=_lowerCAmelCase      ,      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=_lowerCAmelCase      ,      prev_sample_mean=_lowerCAmelCase      ,      state=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Any      ,      _lowerCAmelCase						:      ScoreSdeVeSchedulerState      ,      _lowerCAmelCase						:      jnp.ndarray      ,      _lowerCAmelCase						:      jnp.ndarray      ,      _lowerCAmelCase						:      random.KeyArray      ,      _lowerCAmelCase						:      bool = True      ,      ):
  			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(_lowerCAmelCase      ,      num=1      )
  			A							  =      random.normal(key=_lowerCAmelCase      ,      shape=sample.shape      )
  
  			# compute step size from the model_output, the noise, and the snr
  			A							  =      jnp.linalg.norm(_lowerCAmelCase      )
  			A							  =      jnp.linalg.norm(_lowerCAmelCase      )
  			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(_lowerCAmelCase      ,      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=_lowerCAmelCase      ,      state=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def __len__(self						:      List[Any]      ):
  			return self.config.num_train_timesteps
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      List[str]      ):
  			# numbermap keys are the number and its values are lists of indexes sorted
  			# in ascending order
  			A							  =      {}
  			# indexmap keys are an index and it's values are the number at that index
  			A							  =      {}
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ,      _lowerCAmelCase						:      list | str | range      ,      _lowerCAmelCase						:      int      ):
  			if isinstance(_lowerCAmelCase      ,      (range, str)      ):
  				A							  =      list(_lowerCAmelCase      )
  			elif not isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ):
  				raise TypeError(
  				    """binary_search_delete() only accepts either a list, range or str"""      )
  
  			A							  =      0
  			A							  =      len(_lowerCAmelCase      ) - 1
  
  			while low <= high:
  				A							  =      (low + high) // 2
  				if array[mid] == item:
  					array.pop(_lowerCAmelCase      )
  					return array
  				elif array[mid] < item:
  					A							  =      mid + 1
  				else:
  					A							  =      mid - 1
  			raise ValueError(
  			    """Either the item is not in the array or the array was unsorted"""      )
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      list | str | range      ,      _lowerCAmelCase						:      int      ):
  			if isinstance(_lowerCAmelCase      ,      (range, str)      ):
  				A							  =      list(_lowerCAmelCase      )
  			elif not isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ):
  				raise TypeError(
  				    """binary_search_insert() only accepts either a list, range or str"""      )
  
  			A							  =      0
  			A							  =      len(_lowerCAmelCase      ) - 1
  
  			while low <= high:
  				A							  =      (low + high) // 2
  				if array[mid] == index:
  					# If the item already exists in the array,
  					# insert it after the existing item
  					array.insert(mid + 1      ,      _lowerCAmelCase      )
  					return array
  				elif array[mid] < index:
  					A							  =      mid + 1
  				else:
  					A							  =      mid - 1
  
          # If the item doesn't exist in the array, insert it at the appropriate position
  			array.insert(_lowerCAmelCase      ,      _lowerCAmelCase      )
  			return array
  
  
  
  
  
  
  
  		def        A     (self						:      List[str]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      int      ):
  			# Remove previous index
  			if index in self.indexmap:
  				A							  =      self.indexmap[index]
  				if len(self.numbermap[n]      ) == 1:
  					del self.numbermap[n]
  				else:
  					A							  =      self.binary_search_delete(self.numbermap[n]      ,      _lowerCAmelCase      )
  
          # Set new index
  			A							  =      number
  
  			# Number not seen before or empty so insert number value
  			if number not in self.numbermap:
  				A							  =      [index]
  
  			# Here we need to perform a binary search insertion in order to insert
  			# The item in the correct place
  			else:
  				A							  =      self.binary_search_insert(
  				    self.numbermap[number]      ,      _lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      int      ,      _lowerCAmelCase						:      int      ):
  			return self.numbermap.get(_lowerCAmelCase      ,      [-1]      )[0]
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  