code: '''simple docstring'''
  
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  lowercase__    :       Dict						=			logging.get_logger(__name__)
  
  lowercase__    :       Dict						=			{
      '''google/electra-small-generator''': '''https://huggingface.co/google/electra-small-generator/resolve/main/config.json''',
      '''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/config.json''',
      '''google/electra-large-generator''': '''https://huggingface.co/google/electra-large-generator/resolve/main/config.json''',
      '''google/electra-small-discriminator''': (
          '''https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json'''
      ),
      '''google/electra-base-discriminator''': (
          '''https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json'''
      ),
      '''google/electra-large-discriminator''': (
          '''https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json'''
      ),
  }
  
  
  
  
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      List[str]     =       """electra"""
  
  
  	def __init__(							self	,    __SCREAMING_SNAKE_CASE=30522	,    __SCREAMING_SNAKE_CASE=128	,    __SCREAMING_SNAKE_CASE=256	,    __SCREAMING_SNAKE_CASE=12	,    __SCREAMING_SNAKE_CASE=4	,    __SCREAMING_SNAKE_CASE=1024	,    __SCREAMING_SNAKE_CASE="gelu"	,    __SCREAMING_SNAKE_CASE=0.1	,    __SCREAMING_SNAKE_CASE=0.1	,    __SCREAMING_SNAKE_CASE=512	,    __SCREAMING_SNAKE_CASE=2	,    __SCREAMING_SNAKE_CASE=0.0_2	,    __SCREAMING_SNAKE_CASE=1e-12	,    __SCREAMING_SNAKE_CASE="first"	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE="gelu"	,    __SCREAMING_SNAKE_CASE=0.1	,    __SCREAMING_SNAKE_CASE=0	,    __SCREAMING_SNAKE_CASE="absolute"	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=None	,    **__SCREAMING_SNAKE_CASE	,    )		->str:
  								super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )
  
  								lowerCAmelCase				=	vocab_size
  								lowerCAmelCase				=	embedding_size
  								lowerCAmelCase				=	hidden_size
  								lowerCAmelCase				=	num_hidden_layers
  								lowerCAmelCase				=	num_attention_heads
  								lowerCAmelCase				=	intermediate_size
  								lowerCAmelCase				=	hidden_act
  								lowerCAmelCase				=	hidden_dropout_prob
  								lowerCAmelCase				=	attention_probs_dropout_prob
  								lowerCAmelCase				=	max_position_embeddings
  								lowerCAmelCase				=	type_vocab_size
  								lowerCAmelCase				=	initializer_range
  								lowerCAmelCase				=	layer_norm_eps
  
  								lowerCAmelCase				=	summary_type
  								lowerCAmelCase				=	summary_use_proj
  								lowerCAmelCase				=	summary_activation
  								lowerCAmelCase				=	summary_last_dropout
  								lowerCAmelCase				=	position_embedding_type
  								lowerCAmelCase				=	use_cache
  								lowerCAmelCase				=	classifier_dropout
  
  
  
  
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	@property
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Mapping[str, Mapping[int, str]]:
  								if self.task == "multiple-choice":
  															lowerCAmelCase				=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
  								else:
  															lowerCAmelCase				=	{0: '''batch''', 1: '''sequence'''}
  								return OrderedDict(
  								    [
  								        ('''input_ids''', dynamic_axis),
  								        ('''attention_mask''', dynamic_axis),
  								        ('''token_type_ids''', dynamic_axis),
  								    ]       )
  
  
  
  
style_context: import torch
  
  from accelerate import Accelerator, DistributedType
  
  
  
  
  class      lowercase_						:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	def __enter__(							self       )		->Dict:
  								if self.enabled:
  															lowerCAmelCase				=	self.model.no_sync()
  															self.model_sync_obj.__enter__()
  
  								return self
  
  
  	def __exit__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->Any:
  								if self.enabled:
  															# Average all models on exit
  															self._sync_and_avg_model_params()
  															self.model_sync_obj.__exit__(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
  
  
  	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = True       )		->Tuple:
  								if accelerator.distributed_type not in [
  								    DistributedType.NO,
  								    DistributedType.MULTI_CPU,
  								    DistributedType.MULTI_GPU,
  								]:
  															raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''       )
  								lowerCAmelCase				=	enabled and accelerator.distributed_type != DistributedType.NO
  								lowerCAmelCase				=	0
  								if self.enabled:
  															lowerCAmelCase				=	accelerator
  															lowerCAmelCase				=	model
  															lowerCAmelCase				=	local_sgd_steps
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[Any]:
  								self.num_steps += 1
  								if not self.enabled:
  															return
  
  								if self.num_steps % self.local_sgd_steps == 0:
  															self._sync_and_avg_model_params()
  
  
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Optional[int]:
  								self.accelerator.wait_for_everyone()
  								with self.accelerator.autocast():
  															for param in self.model.parameters():
  																						lowerCAmelCase				=	self.accelerator.reduce(param.data	,    reduction='''mean'''       )
  
  
  
  