
from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase__        =						logging.get_logger(__name__)

lowerCamelCase__        =						{
    """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 	A__				(	__magic_name__      ):
				lowercase            =    'electra'






				def __init__(		self   : Optional[Any]       ,	a   : Any=30_522       ,	a   : int=128       ,	a   : Optional[int]=256       ,	a   : Dict=12       ,	a   : Any=4       ,	a   : Optional[Any]=1_024       ,	a   : Optional[Any]="gelu"       ,	a   : List[str]=0.1       ,	a   : Optional[int]=0.1       ,	a   : Any=512       ,	a   : Tuple=2       ,	a   : int=0.0_2       ,	a   : Any=1E-12       ,	a   : List[Any]="first"       ,	a   : Union[str, Any]=True       ,	a   : List[Any]="gelu"       ,	a   : Dict=0.1       ,	a   : Any=0       ,	a   : Dict="absolute"       ,	a   : Any=True       ,	a   : Tuple=None       ,	**a   : List[str]       ,	):






							'''simple docstring'''







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

							lowerCAmelCase__ : Optional[Any]   =						vocab_size
							lowerCAmelCase__ : Any   =						embedding_size
							lowerCAmelCase__ : int   =						hidden_size
							lowerCAmelCase__ : Dict   =						num_hidden_layers
							lowerCAmelCase__ : Any   =						num_attention_heads
							lowerCAmelCase__ : str   =						intermediate_size
							lowerCAmelCase__ : Optional[int]   =						hidden_act
							lowerCAmelCase__ : Any   =						hidden_dropout_prob
							lowerCAmelCase__ : int   =						attention_probs_dropout_prob
							lowerCAmelCase__ : int   =						max_position_embeddings
							lowerCAmelCase__ : List[Any]   =						type_vocab_size
							lowerCAmelCase__ : Union[str, Any]   =						initializer_range
							lowerCAmelCase__ : Optional[Any]   =						layer_norm_eps

							lowerCAmelCase__ : Union[str, Any]   =						summary_type
							lowerCAmelCase__ : Union[str, Any]   =						summary_use_proj
							lowerCAmelCase__ : List[str]   =						summary_activation
							lowerCAmelCase__ : int   =						summary_last_dropout
							lowerCAmelCase__ : Optional[Any]   =						position_embedding_type
							lowerCAmelCase__ : int   =						use_cache
							lowerCAmelCase__ : int   =						classifier_dropout







class 	A__				(	__magic_name__      ):






				@property
				def 				_lowerCamelCase						(		self   : Union[str, Any]  ):






							'''simple docstring'''







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

import torch

from accelerate import Accelerator, DistributedType







class 	A__				:






				def __enter__(		self   : Optional[Any]  ):






							'''simple docstring'''







							if self.enabled:
										lowerCAmelCase__ : Any   =						self.model.no_sync()
										self.model_sync_obj.__enter__()

							return self






				def __exit__(		self   : int       ,	a   : int       ,	a   : Optional[Any]       ,	a   : str  ):






							'''simple docstring'''







							if self.enabled:
										# Average all models on exit
										self._sync_and_avg_model_params()
										self.model_sync_obj.__exit__(a       ,	a       ,	a  )






				def __init__(		self   : List[str]       ,	a   : Accelerator       ,	a   : torch.nn.Module       ,	a   : int       ,	a   : bool = True  ):






							'''simple docstring'''







							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__ : Union[str, Any]   =						enabled and accelerator.distributed_type != DistributedType.NO
							lowerCAmelCase__ : str   =						0
							if self.enabled:
										lowerCAmelCase__ : Dict   =						accelerator
										lowerCAmelCase__ : Dict   =						model
										lowerCAmelCase__ : Optional[int]   =						local_sgd_steps






				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







							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 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							self.accelerator.wait_for_everyone()
							with self.accelerator.autocast():
										for param in self.model.parameters():
													lowerCAmelCase__ : Dict   =						self.accelerator.reduce(param.data       ,	reduction='mean'  )