code: 
  
  
  
  """simple docstring"""
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  _lowerCAmelCase					:Dict  =      logging.get_logger(__name__)
  
  _lowerCAmelCase					:Union[str, Any]  =      {
      'google/mobilebert-uncased': 'https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'
  }
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
       a__             ='''mobilebert'''
  
  
  
  
  
       def __init__(      self ,			A=3_0_5_2_2 ,			A=5_1_2 ,			A=2_4 ,			A=4 ,			A=5_1_2 ,			A="relu" ,			A=0.0 ,			A=0.1 ,			A=5_1_2 ,			A=2 ,			A=0.02 ,			A=1E-12 ,			A=0 ,			A=1_2_8 ,			A=True ,			A=True ,			A=1_2_8 ,			A=False ,			A=True ,			A=4 ,			A="no_norm" ,			A=True ,			A=None ,			**A ,			)							->      List[Any]:
           super().__init__(pad_token_id=A ,			**A	)
  
           _UpperCAmelCase							:						Any       							=					vocab_size
           _UpperCAmelCase							:						str       							=					hidden_size
           _UpperCAmelCase							:						Optional[int]       							=					num_hidden_layers
           _UpperCAmelCase							:						Tuple       							=					num_attention_heads
           _UpperCAmelCase							:						List[str]       							=					hidden_act
           _UpperCAmelCase							:						Tuple       							=					intermediate_size
           _UpperCAmelCase							:						List[Any]       							=					hidden_dropout_prob
           _UpperCAmelCase							:						Union[str, Any]       							=					attention_probs_dropout_prob
           _UpperCAmelCase							:						str       							=					max_position_embeddings
           _UpperCAmelCase							:						List[str]       							=					type_vocab_size
           _UpperCAmelCase							:						Optional[Any]       							=					initializer_range
           _UpperCAmelCase							:						Dict       							=					layer_norm_eps
           _UpperCAmelCase							:						Any       							=					embedding_size
           _UpperCAmelCase							:						str       							=					trigram_input
           _UpperCAmelCase							:						Dict       							=					use_bottleneck
           _UpperCAmelCase							:						Dict       							=					intra_bottleneck_size
           _UpperCAmelCase							:						str       							=					use_bottleneck_attention
           _UpperCAmelCase							:						List[str]       							=					key_query_shared_bottleneck
           _UpperCAmelCase							:						Tuple       							=					num_feedforward_networks
           _UpperCAmelCase							:						str       							=					normalization_type
           _UpperCAmelCase							:						Optional[Any]       							=					classifier_activation
  
           if self.use_bottleneck:
               _UpperCAmelCase							:						Dict       							=					intra_bottleneck_size
           else:
               _UpperCAmelCase							:						Optional[Any]       							=					hidden_size
  
           _UpperCAmelCase							:						List[Any]       							=					classifier_dropout
  
  
  
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      Mapping[str, Mapping[int, str]]:
           if self.task == "multiple-choice":
               _UpperCAmelCase							:						List[Any]       							=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
           else:
               _UpperCAmelCase							:						Any       							=					{0: '''batch''', 1: '''sequence'''}
           return OrderedDict(
               [
                   ('''input_ids''', dynamic_axis),
                   ('''attention_mask''', dynamic_axis),
                   ('''token_type_ids''', dynamic_axis),
               ]	)
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  import os
  
  from apache_beam.io.filesystems import FileSystems
  from apache_beam.pipeline import Pipeline
  
  from .logging import get_logger
  
  
  _lowerCAmelCase					:Optional[int]  =      2 << 20  # 2mb
  _lowerCAmelCase					:Union[str, Any]  =      get_logger(__name__)
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      Any:
           _UpperCAmelCase							:						str       							=					self._options.get_all_options().get('''runner'''	)
           return runner in [None, "DirectRunner", "PortableRunner"]
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	str  , UpperCamelCase__	:	Dict=False							):
      _UpperCAmelCase							:						Union[str, Any]       							=					FileSystems
      if fs.exists(UpperCamelCase__							):
          if force_upload:
              logger.info(F'Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'							)
          else:
              logger.info(F'Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'							)
              return
      with fs.create(UpperCamelCase__							) as remote_file:
          with open(UpperCamelCase__  , '''rb'''							) as local_file:
              _UpperCAmelCase							:						Optional[Any]       							=					local_file.read(UpperCamelCase__							)
              while chunk:
                  remote_file.write(UpperCamelCase__							)
                  _UpperCAmelCase							:						List[Any]       							=					local_file.read(UpperCamelCase__							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	int  , UpperCamelCase__	:	Dict  , UpperCamelCase__	:	str=False							):
      _UpperCAmelCase							:						int       							=					FileSystems
      if os.path.exists(UpperCamelCase__							):
          if force_download:
              logger.info(F'Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'							)
          else:
              logger.info(F'Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'							)
              return
      with fs.open(UpperCamelCase__							) as remote_file:
          with open(UpperCamelCase__  , '''wb'''							) as local_file:
              _UpperCAmelCase							:						List[str]       							=					remote_file.read(UpperCamelCase__							)
              while chunk:
                  local_file.write(UpperCamelCase__							)
                  _UpperCAmelCase							:						List[Any]       							=					remote_file.read(UpperCamelCase__							)
  
  
  