






"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


A__       :			str=						logging.get_logger(__name__)

A__       :			List[str]=						{
    """google/mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"""
}
class  __lowerCamelCase     (	_a	):
   a   :					Optional[Any]			    =MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
   a   :					List[str]			    ="""mobilebert"""

   def __init__(       self			,					snake_case_=3_0522			,					snake_case_=512			,					snake_case_=24			,					snake_case_=4			,					snake_case_=512			,					snake_case_="relu"			,					snake_case_=0.0			,					snake_case_=0.1			,					snake_case_=512			,					snake_case_=2			,					snake_case_=0.02			,					snake_case_=1E-12			,					snake_case_=0			,					snake_case_=128			,					snake_case_=True			,					snake_case_=True			,					snake_case_=128			,					snake_case_=False			,					snake_case_=True			,					snake_case_=4			,					snake_case_="no_norm"			,					snake_case_=True			,					snake_case_=None			,					**snake_case_			,					)		->							Dict:
       super().__init__(pad_token_id=snake_case_			,					**snake_case_   )

       UpperCamelCase__								=     vocab_size
       UpperCamelCase__								=     hidden_size
       UpperCamelCase__								=     num_hidden_layers
       UpperCamelCase__								=     num_attention_heads
       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__								=     embedding_size
       UpperCamelCase__								=     trigram_input
       UpperCamelCase__								=     use_bottleneck
       UpperCamelCase__								=     intra_bottleneck_size
       UpperCamelCase__								=     use_bottleneck_attention
       UpperCamelCase__								=     key_query_shared_bottleneck
       UpperCamelCase__								=     num_feedforward_networks
       UpperCamelCase__								=     normalization_type
       UpperCamelCase__								=     classifier_activation

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

       UpperCamelCase__								=     classifier_dropout







class  __lowerCamelCase     (	_a	):

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





import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig
class  __lowerCamelCase     (	_a	):
   a   :					List[Any]			    =CustomConfig

   def __init__(       self			,					snake_case_   )		->							str:
       super().__init__(snake_case_   )
       UpperCamelCase__								=     torch.nn.Linear(config.hidden_size			,					config.hidden_size   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Optional[int]:
       return self.linear(snake_case_   )







   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Union[str, Any]:
       pass







class  __lowerCamelCase     (	_a	):
   a   :					Optional[int]			    =NoSuperInitConfig

   def __init__(       self			,					snake_case_   )		->							Optional[int]:
       super().__init__(snake_case_   )
       UpperCamelCase__								=     torch.nn.Linear(config.attribute			,					config.attribute   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							int:
       return self.linear(snake_case_   )







   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Union[str, Any]:
       pass




