




'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_    :					List[Any]					      =						logging.get_logger(__name__)

UpperCAmelCase_    :					List[Any]					      =						{
    '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
}





class       lowerCAmelCase				(	__lowerCAmelCase):
       __lowercase					: Optional[int]	    =     MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
       __lowercase					: Union[str, Any]	    =     '''mobilebert'''


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE=3_0522      ,					__SCREAMING_SNAKE_CASE=512      ,					__SCREAMING_SNAKE_CASE=24      ,					__SCREAMING_SNAKE_CASE=4      ,					__SCREAMING_SNAKE_CASE=512      ,					__SCREAMING_SNAKE_CASE="relu"      ,					__SCREAMING_SNAKE_CASE=0.0      ,					__SCREAMING_SNAKE_CASE=0.1      ,					__SCREAMING_SNAKE_CASE=512      ,					__SCREAMING_SNAKE_CASE=2      ,					__SCREAMING_SNAKE_CASE=0.02      ,					__SCREAMING_SNAKE_CASE=1E-12      ,					__SCREAMING_SNAKE_CASE=0      ,					__SCREAMING_SNAKE_CASE=128      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=128      ,					__SCREAMING_SNAKE_CASE=False      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=4      ,					__SCREAMING_SNAKE_CASE="no_norm"      ,					__SCREAMING_SNAKE_CASE=True      ,					__SCREAMING_SNAKE_CASE=None      ,					**__SCREAMING_SNAKE_CASE      ,					) ->					List[Any]:



            '''simple docstring'''




            super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE      ,					**__SCREAMING_SNAKE_CASE					)

            __snake_case         =					vocab_size
            __snake_case         =					hidden_size
            __snake_case         =					num_hidden_layers
            __snake_case         =					num_attention_heads
            __snake_case         =					hidden_act
            __snake_case         =					intermediate_size
            __snake_case         =					hidden_dropout_prob
            __snake_case         =					attention_probs_dropout_prob
            __snake_case         =					max_position_embeddings
            __snake_case         =					type_vocab_size
            __snake_case         =					initializer_range
            __snake_case         =					layer_norm_eps
            __snake_case         =					embedding_size
            __snake_case         =					trigram_input
            __snake_case         =					use_bottleneck
            __snake_case         =					intra_bottleneck_size
            __snake_case         =					use_bottleneck_attention
            __snake_case         =					key_query_shared_bottleneck
            __snake_case         =					num_feedforward_networks
            __snake_case         =					normalization_type
            __snake_case         =					classifier_activation

            if self.use_bottleneck:
                 __snake_case         =					intra_bottleneck_size
            else:
                 __snake_case         =					hidden_size

            __snake_case         =					classifier_dropout





class       lowerCAmelCase				(	__lowerCAmelCase):


       @property
       def  lowerCAmelCase (				self					) ->					Mapping[str, Mapping[int, str]]:



            '''simple docstring'''




            if self.task == "multiple-choice":
                 __snake_case         =					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
            else:
                 __snake_case         =					{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_tokenizers_available, is_torch_available


UpperCAmelCase_    :					Any					      =						{
    '''configuration_retribert''': ['''RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RetriBertConfig'''],
    '''tokenization_retribert''': ['''RetriBertTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_    :					str					      =						['''RetriBertTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_    :					Optional[int]					      =						[
         '''RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''RetriBertModel''',
         '''RetriBertPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
     from .tokenization_retribert import RetriBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_retribert_fast import RetriBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_retribert import (
              RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              RetriBertModel,
              RetriBertPreTrainedModel,
          )

else:
     import sys

     UpperCAmelCase_    :					Any					      =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






