




'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


A									={
    'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
    'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
    'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
    'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
    'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
    'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
    'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
    'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}


class     _a						(			__a				):
       __a       :  Dict            =      """albert"""
       def __init__(			self		:			Tuple     ,       lowercase		:			Tuple=30_000     ,       lowercase		:			Optional[int]=128     ,       lowercase		:			Dict=4_096     ,       lowercase		:			Any=12     ,       lowercase		:			List[Any]=1     ,       lowercase		:			int=64     ,       lowercase		:			Union[str, Any]=16_384     ,       lowercase		:			Optional[Any]=1     ,       lowercase		:			str="gelu_new"     ,       lowercase		:			Union[str, Any]=0     ,       lowercase		:			Dict=0     ,       lowercase		:			Dict=512     ,       lowercase		:			List[str]=2     ,       lowercase		:			List[Any]=0.02     ,       lowercase		:			Union[str, Any]=1E-12     ,       lowercase		:			Union[str, Any]=0.1     ,       lowercase		:			Optional[int]="absolute"     ,       lowercase		:			Tuple=0     ,       lowercase		:			Tuple=2     ,       lowercase		:			int=3     ,       **lowercase		:			Any     ,       ):



           '''simple docstring'''

           super().__init__(pad_token_id=lowercase     ,       bos_token_id=lowercase     ,       eos_token_id=lowercase     ,       **lowercase       )

           UpperCAmelCase     =	vocab_size
           UpperCAmelCase     =	embedding_size
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	num_hidden_layers
           UpperCAmelCase     =	num_hidden_groups
           UpperCAmelCase     =	num_attention_heads
           UpperCAmelCase     =	inner_group_num
           UpperCAmelCase     =	hidden_act
           UpperCAmelCase     =	intermediate_size
           UpperCAmelCase     =	hidden_dropout_prob
           UpperCAmelCase     =	attention_probs_dropout_prob
           UpperCAmelCase     =	max_position_embeddings
           UpperCAmelCase     =	type_vocab_size
           UpperCAmelCase     =	initializer_range
           UpperCAmelCase     =	layer_norm_eps
           UpperCAmelCase     =	classifier_dropout_prob
           UpperCAmelCase     =	position_embedding_type






class     _a						(			__a				):
       @property
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           if self.task == "multiple-choice":
               UpperCAmelCase     =	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
           else:
               UpperCAmelCase     =	{0: '''batch''', 1: '''sequence'''}
           return OrderedDict(
               [
                   ('''input_ids''', dynamic_axis),
                   ('''attention_mask''', dynamic_axis),
                   ('''token_type_ids''', dynamic_axis),
               ]       )










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A									={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A									=[
       'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TableTransformerForObjectDetection',
       'TableTransformerModel',
       'TableTransformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_table_transformer import (
       TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TableTransformerConfig,
       TableTransformerOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_table_transformer import (
          TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TableTransformerForObjectDetection,
          TableTransformerModel,
          TableTransformerPreTrainedModel,
      )

else:
   import sys

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




