code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  from scipy import integrate
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import (
      CommonSchedulerState,
      FlaxKarrasDiffusionSchedulers,
      FlaxSchedulerMixin,
      FlaxSchedulerOutput,
      broadcast_to_shape_from_left,
  )
  
  
  
  
  
  @flax.struct.dataclass
  class   lowerCamelCase_							:
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				4_2
  
  					# setable values
  					lowercase__					=				4_2
  					lowercase__					=				4_2
  					lowercase__					=				4_2
  					lowercase__					=				None
  
  					# running values
  					lowercase__					=				None
  
  
  
  
  
  
  					@classmethod
  					def        __SCREAMING_SNAKE_CASE			(					cls,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase				):
  							return cls(common=__UpperCamelCase,						init_noise_sigma=__UpperCamelCase,						timesteps=__UpperCamelCase,						sigmas=__UpperCamelCase				)
  
  
  
  
  
  @dataclass
  class   lowerCamelCase_							( UpperCAmelCase_	):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				4_2
  
  
  
  
  
  class   lowerCamelCase_							( UpperCAmelCase_, UpperCAmelCase_	):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				[e.name for e in FlaxKarrasDiffusionSchedulers]
  
  					lowercase__					=				4_2
  
  
  
  
  
  
  					@property
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							return True
  
  
  
  
  
  
  					@register_to_config
  					def __init__(					self,						__UpperCamelCase = 1_000,						__UpperCamelCase = 0.00_01,						__UpperCamelCase = 0.02,						__UpperCamelCase = "linear",						__UpperCamelCase = None,						__UpperCamelCase = "epsilon",						__UpperCamelCase = jnp.floataa,						):
  							_snake_case			 = dtype
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase = None				):
  							if common is None:
  									_snake_case			 = CommonSchedulerState.create(self				)
  
  							_snake_case			 = jnp.arange(0,						self.config.num_train_timesteps				).round()[::-1]
  							_snake_case			 = ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5
  
  							# standard deviation of the initial noise distribution
  							_snake_case			 = sigmas.max()
  
  							return LMSDiscreteSchedulerState.create(
  							    common=__UpperCamelCase,						init_noise_sigma=__UpperCamelCase,						timesteps=__UpperCamelCase,						sigmas=__UpperCamelCase,						)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase				):
  							((_snake_case					) ,										)			 = jnp.where(state.timesteps == timestep,						size=1				)
  							_snake_case			 = step_index[0]
  
  							_snake_case			 = state.sigmas[step_index]
  							_snake_case			 = sample / ((sigma**2 + 1) ** 0.5)
  							return sample
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase				):
  
  							def lms_derivative(__UpperCamelCase				):
  									_snake_case			 = 1.0
  									for k in range(__UpperCamelCase				):
  											if current_order == k:
  													continue
  											prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
  									return prod
  
  							_snake_case			 = integrate.quad(__UpperCamelCase,						state.sigmas[t],						state.sigmas[t + 1],						epsrel=1E-4				)[0]
  
  							return integrated_coeff
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase = ()				):
  							_snake_case			 = jnp.linspace(self.config.num_train_timesteps - 1,						0,						__UpperCamelCase,						dtype=self.dtype				)
  
  							_snake_case			 = jnp.floor(__UpperCamelCase				).astype(jnp.intaa				)
  							_snake_case			 = jnp.ceil(__UpperCamelCase				).astype(jnp.intaa				)
  
  							_snake_case			 = jnp.mod(__UpperCamelCase,						1.0				)
  
  							_snake_case			 = ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
  							_snake_case			 = (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
  							_snake_case			 = jnp.concatenate([sigmas, jnp.array([0.0],						dtype=self.dtype				)]				)
  
  							_snake_case			 = timesteps.astype(jnp.intaa				)
  
  							# initial running values
  							_snake_case			 = jnp.zeros((0,) + shape,						dtype=self.dtype				)
  
  							return state.replace(
  							    timesteps=__UpperCamelCase,						sigmas=__UpperCamelCase,						num_inference_steps=__UpperCamelCase,						derivatives=__UpperCamelCase,						)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase = 4,						__UpperCamelCase = True,						):
  							if state.num_inference_steps is None:
  									raise ValueError(
  									    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"				)
  
  							_snake_case			 = state.sigmas[timestep]
  
  							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
  							if self.config.prediction_type == "epsilon":
  									_snake_case			 = sample - sigma * model_output
  							elif self.config.prediction_type == "v_prediction":
  									# * c_out + input * c_skip
  									_snake_case			 = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
  							else:
  									raise ValueError(
  									    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''				)
  
  							# 2. Convert to an ODE derivative
  							_snake_case			 = (sample - pred_original_sample) / sigma
  							_snake_case			 = state.replace(derivatives=jnp.append(state.derivatives,						__UpperCamelCase				)				)
  							if len(state.derivatives				) > order:
  									_snake_case			 = state.replace(derivatives=jnp.delete(state.derivatives,						0				)				)
  
  							# 3. Compute linear multistep coefficients
  							_snake_case			 = min(timestep + 1,						__UpperCamelCase				)
  							_snake_case			 = [self.get_lms_coefficient(__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase				) for curr_order in range(__UpperCamelCase				)]
  
  							# 4. Compute previous sample based on the derivatives path
  							_snake_case			 = sample + sum(
  							    coeff * derivative for coeff, derivative in zip(__UpperCamelCase,						reversed(state.derivatives				)				)				)
  
  							if not return_dict:
  									return (prev_sample, state)
  
  							return FlaxLMSSchedulerOutput(prev_sample=__UpperCamelCase,						state=__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						):
  							_snake_case			 = state.sigmas[timesteps].flatten()
  							_snake_case			 = broadcast_to_shape_from_left(__UpperCamelCase,						noise.shape				)
  
  							_snake_case			 = original_samples + noise * sigma
  
  							return noisy_samples
  
  
  
  
  
  
  					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				)
  
  
  
  
  
  