



import numpy as np


def     UpperCamelCase(						__UpperCamelCase							:	Optional[Any]  ,__UpperCamelCase							:	Union[str, Any]  ,__UpperCamelCase							:	Optional[int]				):
		lowerCAmelCase_    :  Any             =				n_draws * class_counts / class_counts.sum()
		# floored means we don't overshoot n_samples, but probably undershoot
		lowerCAmelCase_    :  List[Any]             =				np.floor(__UpperCamelCase				)
		# we add samples according to how much "left over" probability
		# they had, until we arrive at n_samples
		lowerCAmelCase_    :  int             =				int(n_draws - floored.sum()				)
		if need_to_add > 0:
				lowerCAmelCase_    :  Optional[Any]             =				continuous - floored
				lowerCAmelCase_    :  List[str]             =				np.sort(np.unique(__UpperCamelCase				)				)[::-1]
				# add according to remainder, but break ties
				# randomly to avoid biases
				for value in values:
						((lowerCAmelCase_)      ,						)    :  Union[str, Any]             =				np.where(remainder == value				)
						# if we need_to_add less than what's in inds
						# we draw randomly from them.
						# if we need to add more, we add them all and
						# go to the next value
						lowerCAmelCase_    :  List[Any]             =				min(len(__UpperCamelCase				)  ,__UpperCamelCase				)
						lowerCAmelCase_    :  Tuple             =				rng.choice(__UpperCamelCase  ,size=__UpperCamelCase  ,replace=__UpperCamelCase				)
						floored[inds] += 1
						need_to_add -= add_now
						if need_to_add == 0:
								break
		return floored.astype(np.intaa				)





def     UpperCamelCase(						__UpperCamelCase							:	List[Any]  ,__UpperCamelCase							:	List[Any]  ,__UpperCamelCase							:	int  ,__UpperCamelCase							:	Any  ,__UpperCamelCase							:	Dict=10				):
		lowerCAmelCase_      ,						lowerCAmelCase_    :  Any             =				np.unique(__UpperCamelCase  ,return_inverse=__UpperCamelCase				)
		lowerCAmelCase_    :  Any             =				classes.shape[0]
		lowerCAmelCase_    :  str             =				np.bincount(__UpperCamelCase				)
		if np.min(__UpperCamelCase				) < 2:
				raise ValueError('''Minimum class count error'''				)
		if n_train < n_classes:
				raise ValueError(
				    '''The train_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_train, n_classes)				)
		if n_test < n_classes:
				raise ValueError(
				    '''The test_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_test, n_classes)				)
		lowerCAmelCase_    :  Optional[Any]             =				np.split(np.argsort(__UpperCamelCase  ,kind='''mergesort'''				)  ,np.cumsum(__UpperCamelCase				)[:-1]				)
		for _ in range(__UpperCamelCase				):
				lowerCAmelCase_    :  Dict             =				approximate_mode(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase				)
				lowerCAmelCase_    :  Any             =				class_counts - n_i
				lowerCAmelCase_    :  int             =				approximate_mode(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase				)

				lowerCAmelCase_    :  List[str]             =				[]
				lowerCAmelCase_    :  str             =				[]

				for i in range(__UpperCamelCase				):
						lowerCAmelCase_    :  Any             =				rng.permutation(class_counts[i]				)
						lowerCAmelCase_    :  Union[str, Any]             =				class_indices[i].take(__UpperCamelCase  ,mode='''clip'''				)
						train.extend(perm_indices_class_i[: n_i[i]]				)
						test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]				)
				lowerCAmelCase_    :  Any             =				rng.permutation(__UpperCamelCase				)
				lowerCAmelCase_    :  List[Any]             =				rng.permutation(__UpperCamelCase				)

				yield train, test










from collections import OrderedDict
from typing import Mapping

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


A__						:						Dict          =      logging.get_logger(__name__)

A__						:						List[str]          =      {
    '''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        __snake_case		(  UpperCamelCase_		):
							_a       =				'''electra'''




							def __init__( self  : Dict       ,       A_  : Dict=3_0_5_2_2       ,       A_  : List[Any]=1_2_8       ,       A_  : Dict=2_5_6       ,       A_  : Any=1_2       ,       A_  : Optional[Any]=4       ,       A_  : Optional[int]=1_0_2_4       ,       A_  : Optional[int]="gelu"       ,       A_  : str=0.1       ,       A_  : Optional[int]=0.1       ,       A_  : str=5_1_2       ,       A_  : Optional[int]=2       ,       A_  : Dict=0.02       ,       A_  : Union[str, Any]=1e-12       ,       A_  : List[Any]="first"       ,       A_  : List[str]=True       ,       A_  : List[str]="gelu"       ,       A_  : Any=0.1       ,       A_  : int=0       ,       A_  : Optional[Any]="absolute"       ,       A_  : Optional[int]=True       ,       A_  : str=None       ,       **A_  : List[Any]       ,       ):
									super().__init__(pad_token_id=A_       ,       **A_)

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

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





class        __snake_case		(  UpperCamelCase_		):




							@property
							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									if self.task == "multiple-choice":
											lowerCAmelCase_    :  Any             =				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
											lowerCAmelCase_    :  Union[str, Any]             =				{0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									        ('''token_type_ids''', dynamic_axis),
									    ])





