code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  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    __A       :
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						None
   __lowerCAmelCase				      =						None
   __lowerCAmelCase				      =						None
  
  
   @classmethod
   def    SCREAMING_SNAKE_CASE							(	cls		)      ->  str:
       return cls()
  
  
  
  
  
  
  @dataclass
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						42
   __lowerCAmelCase				      =						42
   __lowerCAmelCase				      =						None
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
       return True
  
  
   @register_to_config
   def __init__(	self				,					__A = 2000				,					__A = 0.15				,					__A = 0.01				,					__A = 1_348.0				,					__A = 1E-5				,					__A = 1				,					)      ->  Optional[int]:
       pass
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
       a					=ScoreSdeVeSchedulerState.create()
       return self.set_sigmas(
           a_				,					self.config.num_train_timesteps				,					self.config.sigma_min				,					self.config.sigma_max				,					self.config.sampling_eps				,					)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A = ()				,					__A = None		)      ->  ScoreSdeVeSchedulerState:
       a					=sampling_eps if sampling_eps is not None else self.config.sampling_eps
  
       a					=jnp.linspace(1				,					a_				,					a_		)
       return state.replace(timesteps=a_		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A = None				,					__A = None				,					__A = None				,					)      ->  ScoreSdeVeSchedulerState:
       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(a_				,					a_				,					a_		)
  
       a					=jnp.exp(jnp.linspace(jnp.log(a_		)				,					jnp.log(a_		)				,					a_		)		)
       a					=jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]		)
  
       return state.replace(discrete_sigmas=a_				,					sigmas=a_		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A		)      ->  Any:
       return jnp.where(timesteps == 0				,					jnp.zeros_like(a_		)				,					state.discrete_sigmas[timesteps - 1]		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A = True				,					)      ->  Union[FlaxSdeVeOutput, Tuple]:
       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(a_				,					a_				,					a_		)
       a					=jnp.zeros_like(a_		)
       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(a_				,					sample.shape		)
       a					=drift - diffusion**2 * model_output
  
       #  equation 6: sample noise for the diffusion term of
       a					=random.split(a_				,					num=1		)
       a					=random.normal(key=a_				,					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=a_				,					prev_sample_mean=a_				,					state=a_		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A				,					__A				,					__A = True				,					)      ->  Union[FlaxSdeVeOutput, Tuple]:
       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(a_				,					num=1		)
       a					=random.normal(key=a_				,					shape=sample.shape		)
  
       # compute step size from the model_output, the noise, and the snr
       a					=jnp.linalg.norm(a_		)
       a					=jnp.linalg.norm(a_		)
       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(a_				,					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=a_				,					state=a_		)
  
  
  
  
  
   def __len__(	self		)      ->  List[str]:
       return self.config.num_train_timesteps
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCamelCase_     :     int    		=						logging.get_logger(__name__)
  
  lowerCamelCase_     :     Optional[Any]    		=						{
      """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
  }
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"vilt"
  
  
   def __init__(	self				,					__A=3_0522				,					__A=2				,					__A=2				,					__A=40				,					__A=768				,					__A=12				,					__A=12				,					__A=3072				,					__A="gelu"				,					__A=0.0				,					__A=0.0				,					__A=0.02				,					__A=1E-1_2				,					__A=384				,					__A=32				,					__A=3				,					__A=True				,					__A=-1				,					__A=False				,					__A=-1				,					**__A				,					)      ->  Dict:
       super().__init__(tie_word_embeddings=__A				,					**__A		)
  
       a					=vocab_size
       a					=type_vocab_size
       a					=modality_type_vocab_size
       a					=max_position_embeddings
  
       a					=hidden_size
       a					=num_hidden_layers
       a					=num_attention_heads
       a					=intermediate_size
       a					=hidden_act
       a					=hidden_dropout_prob
       a					=attention_probs_dropout_prob
       a					=initializer_range
       a					=layer_norm_eps
  
       a					=image_size
       a					=patch_size
       a					=num_channels
       a					=qkv_bias
       a					=max_image_length
       a					=num_images