code: 
  '''simple docstring'''
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import is_detectrona_available, logging
  
  
  __A          =    logging.get_logger(__name__)
  
  __A          =    {
      "layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json",
      "layoutlmv2-large-uncased": "https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json",
      # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
  }
  
  # soft dependency
  if is_detectrona_available():
  				import detectrona
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  			lowerCamelCase							:							List[Any]       		=  """layoutlmv2"""
  
  
  
  			def __init__(					self       ,     lowerCamelCase__=30_522       ,     lowerCamelCase__=768       ,     lowerCamelCase__=12       ,     lowerCamelCase__=12       ,     lowerCamelCase__=3_072       ,     lowerCamelCase__="gelu"       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=512       ,     lowerCamelCase__=2       ,     lowerCamelCase__=0.02       ,     lowerCamelCase__=1e-12       ,     lowerCamelCase__=0       ,     lowerCamelCase__=1_024       ,     lowerCamelCase__=128       ,     lowerCamelCase__=32       ,     lowerCamelCase__=True       ,     lowerCamelCase__=256       ,     lowerCamelCase__=64       ,     lowerCamelCase__=True       ,     lowerCamelCase__=[7, 7, 256]       ,     lowerCamelCase__=128       ,     lowerCamelCase__=128       ,     lowerCamelCase__=True       ,     lowerCamelCase__=True       ,     lowerCamelCase__=False       ,     lowerCamelCase__=None       ,     **lowerCamelCase__       ,     )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									super().__init__(
  									    vocab_size=lowerCamelCase__       ,     hidden_size=lowerCamelCase__       ,     num_hidden_layers=lowerCamelCase__       ,     num_attention_heads=lowerCamelCase__       ,     intermediate_size=lowerCamelCase__       ,     hidden_act=lowerCamelCase__       ,     hidden_dropout_prob=lowerCamelCase__       ,     attention_probs_dropout_prob=lowerCamelCase__       ,     max_position_embeddings=lowerCamelCase__       ,     type_vocab_size=lowerCamelCase__       ,     initializer_range=lowerCamelCase__       ,     layer_norm_eps=lowerCamelCase__       ,     pad_token_id=lowerCamelCase__       ,     **lowerCamelCase__       ,     )
  									lowercase__							 =       max_ad_position_embeddings
  									lowercase__							 =       max_rel_pos
  									lowercase__							 =       rel_pos_bins
  									lowercase__							 =       fast_qkv
  									lowercase__							 =       max_rel_ad_pos
  									lowercase__							 =       rel_ad_pos_bins
  									lowercase__							 =       convert_sync_batchnorm
  									lowercase__							 =       image_feature_pool_shape
  									lowercase__							 =       coordinate_size
  									lowercase__							 =       shape_size
  									lowercase__							 =       has_relative_attention_bias
  									lowercase__							 =       has_spatial_attention_bias
  									lowercase__							 =       has_visual_segment_embedding
  									lowercase__							 =       (
  									    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
  									)
  
  
  
  			@classmethod
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return {
  									    "MODEL.MASK_ON": True,
  									    "MODEL.PIXEL_STD": [57.3_75, 57.1_20, 58.3_95],
  									    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
  									    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
  									    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
  									    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
  									    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2_000,
  									    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_000,
  									    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_000,
  									    "MODEL.POST_NMS_TOPK_TEST": 1_000,
  									    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
  									    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
  									    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
  									    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
  									    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
  									    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
  									    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
  									    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
  									    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
  									    "MODEL.RESNETS.DEPTH": 101,
  									    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
  									    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
  									    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
  									    "MODEL.RESNETS.NUM_GROUPS": 32,
  									    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
  									    "MODEL.RESNETS.STRIDE_IN_1X1": False,
  									}
  
  
  
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       detectrona.config.get_cfg()
  									for k, v in self.detectrona_config_args.items():
  															lowercase__							 =       k.split("""."""       )
  															lowercase__							 =       detectrona_config
  															for attribute in attributes[:-1]:
  																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
  															setattr(lowerCamelCase__       ,     attributes[-1]       ,     lowerCamelCase__       )
  
  									return detectrona_config
  
  
  
  
  
  
  
style_context: 
  '''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 			A							:
  			# setable values
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  
  
  
  			@classmethod
  			def A__ (					cls       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return cls()
  
  
  
  
  
  @dataclass
  class 			A							(       __UpperCAmelCase				):
  			lowerCamelCase							:							ScoreSdeVeSchedulerState
  			lowerCamelCase							:							jnp.ndarray
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  
  
  
  
  
  class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase				):
  
  
  
  			@property
  			def A__ (					self       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return True
  
  
  
  			@register_to_config
  			def __init__(					self       ,     lowerCamelCase__ = 2_000       ,     lowerCamelCase__ = 0.15       ,     lowerCamelCase__ = 0.01       ,     lowerCamelCase__ = 13_48.0       ,     lowerCamelCase__ = 1e-5       ,     lowerCamelCase__ = 1       ,     )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       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       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = ()       ,     lowerCamelCase__ = None       )			->		ScoreSdeVeSchedulerState:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
  
  									lowercase__							 =       jnp.linspace(1       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return state.replace(timesteps=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     )			->		ScoreSdeVeSchedulerState:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       sigma_min if sigma_min is not None else self.config.sigma_min
  									lowercase__							 =       sigma_max if sigma_max is not None else self.config.sigma_max
  									lowercase__							 =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
  									if state.timesteps is None:
  															lowercase__							 =       self.set_timesteps(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  
  									lowercase__							 =       jnp.exp(jnp.linspace(jnp.log(lowerCamelCase__       )       ,     jnp.log(lowerCamelCase__       )       ,     lowerCamelCase__       )       )
  									lowercase__							 =       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       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return jnp.where(timesteps == 0       ,     jnp.zeros_like(lowerCamelCase__       )       ,     state.discrete_sigmas[timesteps - 1]       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = True       ,     )			->		Union[FlaxSdeVeOutput, Tuple]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if state.timesteps is None:
  															raise ValueError(
  															    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""       )
  
  									lowercase__							 =       timestep * jnp.ones(
  									    sample.shape[0]       ,     )
  									lowercase__							 =       (timestep * (len(state.timesteps       ) - 1)).long()
  
  									lowercase__							 =       state.discrete_sigmas[timesteps]
  									lowercase__							 =       self.get_adjacent_sigma(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									lowercase__							 =       jnp.zeros_like(lowerCamelCase__       )
  									lowercase__							 =       (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
  									lowercase__							 =       diffusion.flatten()
  									lowercase__							 =       broadcast_to_shape_from_left(lowerCamelCase__       ,     sample.shape       )
  									lowercase__							 =       drift - diffusion**2 * model_output
  
  									#  equation 6: sample noise for the diffusion term of
  									lowercase__							 =       random.split(lowerCamelCase__       ,     num=1       )
  									lowercase__							 =       random.normal(key=lowerCamelCase__       ,     shape=sample.shape       )
  									lowercase__							 =       sample - drift  # subtract because `dt` is a small negative timestep
  									# TODO is the variable diffusion the correct scaling term for the noise?
  									lowercase__							 =       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       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = True       ,     )			->		Union[FlaxSdeVeOutput, Tuple]:
  
  
  
  
  									'''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
  									lowercase__							 =       random.split(lowerCamelCase__       ,     num=1       )
  									lowercase__							 =       random.normal(key=lowerCamelCase__       ,     shape=sample.shape       )
  
  									# compute step size from the model_output, the noise, and the snr
  									lowercase__							 =       jnp.linalg.norm(lowerCamelCase__       )
  									lowercase__							 =       jnp.linalg.norm(lowerCamelCase__       )
  									lowercase__							 =       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
  									lowercase__							 =       step_size * jnp.ones(sample.shape[0]       )
  
  									# compute corrected sample: model_output term and noise term
  									lowercase__							 =       step_size.flatten()
  									lowercase__							 =       broadcast_to_shape_from_left(lowerCamelCase__       ,     sample.shape       )
  									lowercase__							 =       sample + step_size * model_output
  									lowercase__							 =       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       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self.config.num_train_timesteps
  
  
  
  
  
  
  