


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_    :  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__						:						int          =      logging.get_logger(__name__)

A__						:						Dict          =      {
    '''mnaylor/mega-base-wikitext''': '''https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json''',
}



class        __snake_case		(  UpperCamelCase_		):
							_a       =				'''mega'''




							def __init__( self  : Any       ,       A_  : Dict=3_0_5_2_2       ,       A_  : int=1_2_8       ,       A_  : str=4       ,       A_  : int=2_5_6       ,       A_  : int=1_6       ,       A_  : str=True       ,       A_  : List[str]=6_4       ,       A_  : Optional[Any]=False       ,       A_  : str=-1       ,       A_  : Optional[int]=None       ,       A_  : Tuple=True       ,       A_  : Tuple="scalenorm"       ,       A_  : Optional[Any]=True       ,       A_  : int="silu"       ,       A_  : Optional[int]="softmax"       ,       A_  : Optional[int]=0.1       ,       A_  : Union[str, Any]=0.1       ,       A_  : Any=0.1       ,       A_  : Dict=False       ,       A_  : int=True       ,       A_  : Tuple=2_5_6       ,       A_  : int=True       ,       A_  : List[Any]=0.1       ,       A_  : Optional[Any]=2_0_4_8       ,       A_  : Dict=False       ,       A_  : Dict=2       ,       A_  : Tuple=0.02       ,       A_  : List[Any]=0.2       ,       A_  : str=0.02       ,       A_  : List[str]=1.0       ,       A_  : Union[str, Any]=1       ,       A_  : List[Any]=0       ,       A_  : int=2       ,       A_  : Tuple="rotary"       ,       A_  : Any=None       ,       A_  : Tuple=True       ,       A_  : Any=True       ,       **A_  : Optional[int]       ,       ):
									super().__init__(pad_token_id=A_       ,       bos_token_id=A_       ,       eos_token_id=A_       ,       **A_)

									lowerCAmelCase_    :  Tuple             =				vocab_size
									lowerCAmelCase_    :  str             =				hidden_size
									lowerCAmelCase_    :  Any             =				num_hidden_layers
									lowerCAmelCase_    :  Optional[int]             =				activation
									lowerCAmelCase_    :  Any             =				attention_activation
									lowerCAmelCase_    :  List[Any]             =				intermediate_size
									lowerCAmelCase_    :  Any             =				ema_projection_size
									lowerCAmelCase_    :  Optional[int]             =				bidirectional
									lowerCAmelCase_    :  Tuple             =				shared_representation_size
									lowerCAmelCase_    :  Any             =				use_chunking
									lowerCAmelCase_    :  List[str]             =				chunk_size
									lowerCAmelCase_    :  int             =				truncation
									lowerCAmelCase_    :  Tuple             =				normalize_before_mega
									lowerCAmelCase_    :  Tuple             =				normalization_type
									lowerCAmelCase_    :  List[str]             =				norm_affine
									lowerCAmelCase_    :  List[str]             =				dropout_prob
									lowerCAmelCase_    :  List[str]             =				hidden_dropout_prob
									lowerCAmelCase_    :  Tuple             =				attention_probs_dropout_prob
									lowerCAmelCase_    :  str             =				use_feature_dropout
									lowerCAmelCase_    :  Union[str, Any]             =				use_normalized_ffn
									lowerCAmelCase_    :  Union[str, Any]             =				nffn_hidden_size
									lowerCAmelCase_    :  int             =				normalize_before_ffn
									lowerCAmelCase_    :  Tuple             =				nffn_activation_dropout_prob
									lowerCAmelCase_    :  List[str]             =				max_positions
									lowerCAmelCase_    :  Union[str, Any]             =				add_token_type_embeddings
									lowerCAmelCase_    :  str             =				type_vocab_size
									lowerCAmelCase_    :  int             =				initializer_range
									lowerCAmelCase_    :  str             =				ema_delta_alpha_range
									lowerCAmelCase_    :  Tuple             =				ema_beta_range
									lowerCAmelCase_    :  Any             =				ema_gamma_omega_range
									lowerCAmelCase_    :  Any             =				relative_positional_bias
									lowerCAmelCase_    :  Optional[int]             =				use_cache
									lowerCAmelCase_    :  int             =				classifier_dropout
									lowerCAmelCase_    :  List[Any]             =				add_lm_hidden_dense_layer
									lowerCAmelCase_    :  Optional[Any]             =				1  # not used but required by Hugging Face





class        __snake_case		(  UpperCamelCase_		):




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





