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   lowerCamelCase_							:
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				None
  					lowercase__					=				None
  					lowercase__					=				None
  
  
  
  
  
  
  					@classmethod
  					def        __SCREAMING_SNAKE_CASE			(					cls				):
  							return cls()
  
  
  
  
  
  @dataclass
  class   lowerCamelCase_							( UpperCAmelCase_							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				42
  					lowercase__					=				42
  					lowercase__					=				None
  
  
  
  
  
  class   lowerCamelCase_							( UpperCAmelCase_, UpperCAmelCase_							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					@property
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							return True
  
  
  
  
  
  
  					@register_to_config
  					def __init__(					self,						__UpperCamelCase = 2_000,						__UpperCamelCase = 0.15,						__UpperCamelCase = 0.01,						__UpperCamelCase = 13_48.0,						__UpperCamelCase = 1E-5,						__UpperCamelCase = 1,						):
  							pass
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = 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        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase = (),						__UpperCamelCase = None				):
  							_snake_case			 = sampling_eps if sampling_eps is not None else self.config.sampling_eps
  
  							_snake_case			 = jnp.linspace(1,						__UpperCamelCase,						__UpperCamelCase				)
  							return state.replace(timesteps=__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase = None,						__UpperCamelCase = None,						__UpperCamelCase = None,						):
  							_snake_case			 = sigma_min if sigma_min is not None else self.config.sigma_min
  							_snake_case			 = sigma_max if sigma_max is not None else self.config.sigma_max
  							_snake_case			 = sampling_eps if sampling_eps is not None else self.config.sampling_eps
  							if state.timesteps is None:
  									_snake_case			 = self.set_timesteps(__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase				)
  
  							_snake_case			 = jnp.exp(jnp.linspace(jnp.log(__UpperCamelCase				),						jnp.log(__UpperCamelCase				),						__UpperCamelCase				)				)
  							_snake_case			 = jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]				)
  
  							return state.replace(discrete_sigmas=__UpperCamelCase,						sigmas=__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase				):
  							return jnp.where(timesteps == 0,						jnp.zeros_like(__UpperCamelCase				),						state.discrete_sigmas[timesteps - 1]				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase = True,						):
  							if state.timesteps is None:
  									raise ValueError(
  									    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)
  
  							_snake_case			 = timestep * jnp.ones(
  							    sample.shape[0],						)
  							_snake_case			 = (timestep * (len(state.timesteps				) - 1)).long()
  
  							_snake_case			 = state.discrete_sigmas[timesteps]
  							_snake_case			 = self.get_adjacent_sigma(__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase				)
  							_snake_case			 = jnp.zeros_like(__UpperCamelCase				)
  							_snake_case			 = (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
  							_snake_case			 = diffusion.flatten()
  							_snake_case			 = broadcast_to_shape_from_left(__UpperCamelCase,						sample.shape				)
  							_snake_case			 = drift - diffusion**2 * model_output
  
  							#  equation 6: sample noise for the diffusion term of
  							_snake_case			 = random.split(__UpperCamelCase,						num=1				)
  							_snake_case			 = random.normal(key=__UpperCamelCase,						shape=sample.shape				)
  							_snake_case			 = sample - drift  # subtract because `dt` is a small negative timestep
  							# TODO is the variable diffusion the correct scaling term for the noise?
  							_snake_case			 = 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        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase = 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
  							_snake_case			 = random.split(__UpperCamelCase,						num=1				)
  							_snake_case			 = random.normal(key=__UpperCamelCase,						shape=sample.shape				)
  
  							# compute step size from the model_output, the noise, and the snr
  							_snake_case			 = jnp.linalg.norm(__UpperCamelCase				)
  							_snake_case			 = jnp.linalg.norm(__UpperCamelCase				)
  							_snake_case			 = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
  							_snake_case			 = step_size * jnp.ones(sample.shape[0]				)
  
  							# compute corrected sample: model_output term and noise term
  							_snake_case			 = step_size.flatten()
  							_snake_case			 = broadcast_to_shape_from_left(__UpperCamelCase,						sample.shape				)
  							_snake_case			 = sample + step_size * model_output
  							_snake_case			 = 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				):
  							return self.config.num_train_timesteps
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vilt import ViltImageProcessor
  
  
  _lowerCamelCase							:		Optional[Any]										=						logging.get_logger(__name__)
  
  
  
  
  
  class   lowerCamelCase_							( UpperCAmelCase_							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						*__UpperCamelCase,						**__UpperCamelCase				):
  							warnings.warn(
  							    "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
  							    " use ViltImageProcessor instead.",						__UpperCamelCase,						)
  							super().__init__(*__UpperCamelCase,						**__UpperCamelCase				)
  
  
  
  
  
  