"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


_a	:	Optional[int]=					logging.get_logger(__name__)

_a	:	List[str]=					{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}

class 						UpperCamelCase     (       __lowerCamelCase		):
				UpperCAmelCase							: Any       =						"""rembert"""



				def __init__(self							: Optional[int]      ,    _A							: Any=25_03_00      ,    _A							: Tuple=11_52      ,    _A							: Dict=32      ,    _A							: List[Any]=18      ,    _A							: Union[str, Any]=2_56      ,    _A							: Dict=16_64      ,    _A							: List[str]=46_08      ,    _A							: List[str]="gelu"      ,    _A							: int=0.0      ,    _A							: Optional[int]=0.0      ,    _A							: int=0.1      ,    _A							: Dict=5_12      ,    _A							: Dict=2      ,    _A							: Any=0.02      ,    _A							: int=1E-12      ,    _A							: Union[str, Any]=True      ,    _A							: List[Any]=0      ,    _A							: Union[str, Any]=3_12      ,    _A							: Any=3_13      ,    **_A							: List[str]      ,    )       ->      Union[str, Any]:
					super().__init__(pad_token_id=_lowercase      ,    bos_token_id=_lowercase      ,    eos_token_id=_lowercase      ,    **_lowercase)

					__snake_case     :				Optional[Any] 						=					vocab_size
					__snake_case     :				Optional[Any] 						=					input_embedding_size
					__snake_case     :				Dict 						=					output_embedding_size
					__snake_case     :				Union[str, Any] 						=					max_position_embeddings
					__snake_case     :				Tuple 						=					hidden_size
					__snake_case     :				int 						=					num_hidden_layers
					__snake_case     :				List[Any] 						=					num_attention_heads
					__snake_case     :				Any 						=					intermediate_size
					__snake_case     :				str 						=					hidden_act
					__snake_case     :				Optional[int] 						=					hidden_dropout_prob
					__snake_case     :				Tuple 						=					attention_probs_dropout_prob
					__snake_case     :				List[str] 						=					classifier_dropout_prob
					__snake_case     :				str 						=					initializer_range
					__snake_case     :				List[str] 						=					type_vocab_size
					__snake_case     :				Dict 						=					layer_norm_eps
					__snake_case     :				str 						=					use_cache
					__snake_case     :				Optional[int] 						=					False







class 						UpperCamelCase     (       __lowerCamelCase		):



				@property
				def    _lowercase	(self							: List[str])       ->      Tuple:
					if self.task == "multiple-choice":
						__snake_case     :				List[Any] 						=					{0: 'batch', 1: 'choice', 2: 'sequence'}
					else:
						__snake_case     :				Optional[Any] 						=					{0: 'batch', 1: 'sequence'}
					return OrderedDict(
					    [
					        ('input_ids', dynamic_axis),
					        ('attention_mask', dynamic_axis),
					        ('token_type_ids', dynamic_axis),
					    ])






				@property
				def    _lowercase	(self							: Tuple)       ->      Tuple:
					return 1E-4






"""simple docstring"""







import numpy as np


def 	a__   (		UpperCAmelCase__							:      List[str]		,      UpperCAmelCase__							:      int		,      UpperCAmelCase__							:      str      )->						Optional[int]:
						UpperCAmelCase__       						=		n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						UpperCAmelCase__       						=		np.floor(UpperCAmelCase__      )
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						UpperCAmelCase__       						=		int(n_draws - floored.sum()      )
						if need_to_add > 0:
												UpperCAmelCase__       						=		continuous - floored
												UpperCAmelCase__       						=		np.sort(np.unique(UpperCAmelCase__      )      )[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		((UpperCAmelCase__)						,   )       						=		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
																		UpperCAmelCase__       						=		min(len(UpperCAmelCase__      )		,      UpperCAmelCase__      )
																		UpperCAmelCase__       						=		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 	a__   (		UpperCAmelCase__							:      Optional[int]		,      UpperCAmelCase__							:      Optional[int]		,      UpperCAmelCase__							:      Dict		,      UpperCAmelCase__							:      Any		,      UpperCAmelCase__							:      Union[str, Any]=1_0      )->						Tuple:
						UpperCAmelCase__						,   UpperCAmelCase__       						=		np.unique(UpperCAmelCase__		,      return_inverse=UpperCAmelCase__      )
						UpperCAmelCase__       						=		classes.shape[0]
						UpperCAmelCase__       						=		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)      )
						UpperCAmelCase__       						=		np.split(np.argsort(UpperCAmelCase__		,      kind="""mergesort"""      )		,      np.cumsum(UpperCAmelCase__      )[:-1]      )
						for _ in range(UpperCAmelCase__      ):
												UpperCAmelCase__       						=		approximate_mode(UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__      )
												UpperCAmelCase__       						=		class_counts - n_i
												UpperCAmelCase__       						=		approximate_mode(UpperCAmelCase__		,      UpperCAmelCase__		,      UpperCAmelCase__      )

												UpperCAmelCase__       						=		[]
												UpperCAmelCase__       						=		[]

												for i in range(UpperCAmelCase__      ):
																		UpperCAmelCase__       						=		rng.permutation(class_counts[i]      )
																		UpperCAmelCase__       						=		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]]      )
												UpperCAmelCase__       						=		rng.permutation(UpperCAmelCase__      )
												UpperCAmelCase__       						=		rng.permutation(UpperCAmelCase__      )

												yield train, test

