

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[Any] 							=			{
    """facebook/xlm-roberta-xl""": """https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json""",
    """facebook/xlm-roberta-xxl""": """https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json""",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     'xlm-roberta-xl'




	def __init__(		self	:						Tuple		,	lowerCAmelCase	:						List[Any]=250_880		,	lowerCAmelCase	:						Union[str, Any]=2_560		,	lowerCAmelCase	:						List[str]=36		,	lowerCAmelCase	:						Union[str, Any]=32		,	lowerCAmelCase	:						List[Any]=10_240		,	lowerCAmelCase	:						Any="gelu"		,	lowerCAmelCase	:						Any=0.1		,	lowerCAmelCase	:						Tuple=0.1		,	lowerCAmelCase	:						Optional[Any]=514		,	lowerCAmelCase	:						str=1		,	lowerCAmelCase	:						List[Any]=0.02		,	lowerCAmelCase	:						List[Any]=1e-05		,	lowerCAmelCase	:						Dict=1		,	lowerCAmelCase	:						List[str]=0		,	lowerCAmelCase	:						str=2		,	lowerCAmelCase	:						int="absolute"		,	lowerCAmelCase	:						int=True		,	lowerCAmelCase	:						Optional[int]=None		,	**lowerCAmelCase	:						Union[str, Any]		,	):


		'''simple docstring'''







		super().__init__(pad_token_id=lowerCAmelCase		,	bos_token_id=lowerCAmelCase		,	eos_token_id=lowerCAmelCase		,	**lowerCAmelCase							)
		UpperCAmelCase_       				=			vocab_size
		UpperCAmelCase_       				=			hidden_size
		UpperCAmelCase_       				=			num_hidden_layers
		UpperCAmelCase_       				=			num_attention_heads
		UpperCAmelCase_       				=			hidden_act
		UpperCAmelCase_       				=			intermediate_size
		UpperCAmelCase_       				=			hidden_dropout_prob
		UpperCAmelCase_       				=			attention_probs_dropout_prob
		UpperCAmelCase_       				=			max_position_embeddings
		UpperCAmelCase_       				=			type_vocab_size
		UpperCAmelCase_       				=			initializer_range
		UpperCAmelCase_       				=			layer_norm_eps
		UpperCAmelCase_       				=			position_embedding_type
		UpperCAmelCase_       				=			use_cache
		UpperCAmelCase_       				=			classifier_dropout


class 						__UpperCamelCase    (					lowercase						):




	@property
	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







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


from collections.abc import Sequence



def       __lowerCAmelCase      (						A       ,     A = False    ):
	if not arr:
		return 0

	UpperCAmelCase_       				=			0 if allow_empty_subarrays else float("-inf"    )
	UpperCAmelCase_       				=			0.0
	for num in arr:
		UpperCAmelCase_       				=			max(0 if allow_empty_subarrays else num       ,     curr_sum + num    )
		UpperCAmelCase_       				=			max(A       ,     A    )

	return max_sum


if __name__ == "__main__":
				from doctest import testmod

				testmod()

				_a:    Any 							=			[-2, 1, -3, 4, -1, 2, 1, -5, 4]
				print(F'{max_subarray_sum(nums) = }')