

"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


__UpperCamelCase  :      str							      =		logging.get_logger(__name__)

__UpperCamelCase  :      Dict							      =		{
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       "camembert"







					def __init__(       self			:				Optional[int]   ,lowercase_			:				List[str]=3_0_5_2_2   ,lowercase_			:				Dict=7_6_8   ,lowercase_			:				str=1_2   ,lowercase_			:				int=1_2   ,lowercase_			:				Any=3_0_7_2   ,lowercase_			:				str="gelu"   ,lowercase_			:				List[str]=0.1   ,lowercase_			:				Optional[Any]=0.1   ,lowercase_			:				Tuple=5_1_2   ,lowercase_			:				Optional[Any]=2   ,lowercase_			:				Any=0.02   ,lowercase_			:				Union[str, Any]=1E-12   ,lowercase_			:				str=1   ,lowercase_			:				Tuple=0   ,lowercase_			:				Any=2   ,lowercase_			:				Optional[int]="absolute"   ,lowercase_			:				List[Any]=True   ,lowercase_			:				Optional[Any]=None   ,**lowercase_			:				Optional[Any]   ,):
						super().__init__(pad_token_id=lowercase_   ,bos_token_id=lowercase_   ,eos_token_id=lowercase_   ,**lowercase_		)

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







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



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







"""simple docstring"""





import random

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	[], [], []
	for element in data:
		if element < pivot:
			less.append(A_ )
		elif element > pivot:
			greater.append(A_ )
		else:
			equal.append(A_ )
	return less, equal, greater





def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	# index = len(items) // 2 when trying to find the median
	#   (value of index when items is sorted)

	# invalid input
	if index >= len(A_ ) or index < 0:
		return None

	lowerCAmelCase__	:						str  				=	items[random.randint(0						,     len(A_ ) - 1 )]
	lowerCAmelCase__	:						Optional[Any]  				=	0
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	_partition(A_						,     A_ )
	lowerCAmelCase__	:						str  				=	len(A_ )
	lowerCAmelCase__	:						Optional[Any]  				=	len(A_ )

	# index is the pivot
	if m <= index < m + count:
		return pivot
	# must be in smaller
	elif m > index:
		return quick_select(A_						,     A_ )
	# must be in larger
	else:
		return quick_select(A_						,     index - (m + count) )




