





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_									=logging.get_logger(__name__)

UpperCamelCase_									={
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}




class 			_a						(				__A    ):
				UpperCamelCase								=   'markuplm'






				def __init__(			self		:   List[str],			lowerCAmelCase__		:   List[Any]=3_0_5_2_2,			lowerCAmelCase__		:   Dict=7_6_8,			lowerCAmelCase__		:   Dict=1_2,			lowerCAmelCase__		:   Any=1_2,			lowerCAmelCase__		:   List[str]=3_0_7_2,			lowerCAmelCase__		:   Optional[Any]="gelu",			lowerCAmelCase__		:   List[str]=0.1,			lowerCAmelCase__		:   Any=0.1,			lowerCAmelCase__		:   int=5_1_2,			lowerCAmelCase__		:   Optional[Any]=2,			lowerCAmelCase__		:   Union[str, Any]=0.02,			lowerCAmelCase__		:   Union[str, Any]=1e-1_2,			lowerCAmelCase__		:   List[Any]=0,			lowerCAmelCase__		:   Optional[Any]=0,			lowerCAmelCase__		:   List[str]=2,			lowerCAmelCase__		:   Optional[int]=2_5_6,			lowerCAmelCase__		:   Optional[int]=1_0_2_4,			lowerCAmelCase__		:   List[Any]=2_1_6,			lowerCAmelCase__		:   Optional[Any]=1_0_0_1,			lowerCAmelCase__		:   Optional[int]=3_2,			lowerCAmelCase__		:   Optional[Any]=5_0,			lowerCAmelCase__		:   int="absolute",			lowerCAmelCase__		:   int=True,			lowerCAmelCase__		:   List[Any]=None,			**lowerCAmelCase__		:   List[str],			)     ->				List[Any]:




										'''simple docstring'''



										super().__init__(
										    pad_token_id=__lowercase,			bos_token_id=__lowercase,			eos_token_id=__lowercase,			**__lowercase,			)
										_UpperCamelCase      : Union[str, Any]         =   vocab_size
										_UpperCamelCase      : List[Any]         =   hidden_size
										_UpperCamelCase      : Optional[int]         =   num_hidden_layers
										_UpperCamelCase      : str         =   num_attention_heads
										_UpperCamelCase      : Optional[int]         =   hidden_act
										_UpperCamelCase      : str         =   intermediate_size
										_UpperCamelCase      : Optional[Any]         =   hidden_dropout_prob
										_UpperCamelCase      : Any         =   attention_probs_dropout_prob
										_UpperCamelCase      : str         =   max_position_embeddings
										_UpperCamelCase      : str         =   type_vocab_size
										_UpperCamelCase      : str         =   initializer_range
										_UpperCamelCase      : Optional[int]         =   layer_norm_eps
										_UpperCamelCase      : Optional[int]         =   position_embedding_type
										_UpperCamelCase      : Optional[int]         =   use_cache
										_UpperCamelCase      : int         =   classifier_dropout
										# additional properties
										_UpperCamelCase      : str         =   max_depth
										_UpperCamelCase      : List[Any]         =   max_xpath_tag_unit_embeddings
										_UpperCamelCase      : Any         =   max_xpath_subs_unit_embeddings
										_UpperCamelCase      : Optional[int]         =   tag_pad_id
										_UpperCamelCase      : Any         =   subs_pad_id
										_UpperCamelCase      : Any         =   xpath_unit_hidden_size










"""simple docstring"""




from ..utils import is_flax_available, is_torch_available


if is_torch_available():
						from .autoencoder_kl import AutoencoderKL
						from .controlnet import ControlNetModel
						from .dual_transformer_ad import DualTransformeraDModel
						from .modeling_utils import ModelMixin
						from .prior_transformer import PriorTransformer
						from .ta_film_transformer import TaFilmDecoder
						from .transformer_ad import TransformeraDModel
						from .unet_ad import UNetaDModel
						from .unet_ad import UNetaDModel
						from .unet_ad_condition import UNetaDConditionModel
						from .unet_ad_condition import UNetaDConditionModel
						from .vq_model import VQModel

if is_flax_available():
						from .controlnet_flax import FlaxControlNetModel
						from .unet_ad_condition_flax import FlaxUNetaDConditionModel
						from .vae_flax import FlaxAutoencoderKL



