

"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCAmelCase =				{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TapasForMaskedLM',
          'TapasForQuestionAnswering',
          'TapasForSequenceClassification',
          'TapasModel',
          'TapasPreTrainedModel',
          'load_tf_weights_in_tapas',
      ]
try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFTapasForMaskedLM',
          'TFTapasForQuestionAnswering',
          'TFTapasForSequenceClassification',
          'TFTapasModel',
          'TFTapasPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
      from .tokenization_tapas import TapasTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tapas import (
                TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TapasForMaskedLM,
                TapasForQuestionAnswering,
                TapasForSequenceClassification,
                TapasModel,
                TapasPreTrainedModel,
                load_tf_weights_in_tapas,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_tapas import (
                TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFTapasForMaskedLM,
                TFTapasForQuestionAnswering,
                TFTapasForSequenceClassification,
                TFTapasModel,
                TFTapasPreTrainedModel,
            )


else:
      import sys

      __UpperCAmelCase =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








"""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      __lowercase			(    __lowerCamelCase					):
  snake_case_				       =       """markuplm"""

  def __init__(  self		:			str					,A		:			List[Any]=30_522					,A		:			Tuple=768					,A		:			str=12					,A		:			int=12					,A		:			int=3_072					,A		:			Optional[int]="gelu"					,A		:			Optional[int]=0.1					,A		:			Optional[int]=0.1					,A		:			Any=512					,A		:			Any=2					,A		:			str=0.0_2					,A		:			int=1e-12					,A		:			int=0					,A		:			str=0					,A		:			List[Any]=2					,A		:			List[str]=256					,A		:			Union[str, Any]=1_024					,A		:			List[Any]=216					,A		:			Any=1_001					,A		:			Optional[int]=32					,A		:			Any=50					,A		:			int="absolute"					,A		:			Dict=True					,A		:			int=None					,**A		:			Optional[int]					,):






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





