

'''simple docstring'''


from typing import Any



class 					lowercase						:


							def __init__(			self    ,			_snake_case)							->				Dict:
								UpperCAmelCase_		:      List[str]       	=					data
								UpperCAmelCase_		:      str       	=					None



class 					lowercase						:


							def __init__(			self)							->				Dict:
								UpperCAmelCase_		:      Any       	=					None


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      int       	=					self.head
								while temp is not None:
									print(temp.data    ,			end=' ')
									UpperCAmelCase_		:      Optional[Any]       	=					temp.next
								print()


							def 						_snake_case							(			self    ,			_snake_case)							->				Dict:
								UpperCAmelCase_		:      int       	=					Node(_snake_case)
								UpperCAmelCase_		:      Any       	=					self.head
								UpperCAmelCase_		:      Optional[Any]       	=					new_node





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				str:
								if node_data_a == node_data_a:
									return
								else:
									UpperCAmelCase_		:      Dict       	=					self.head
									while node_a is not None and node_a.data != node_data_a:
										UpperCAmelCase_		:      Union[str, Any]       	=					node_a.next

									UpperCAmelCase_		:      Tuple       	=					self.head
									while node_a is not None and node_a.data != node_data_a:
										UpperCAmelCase_		:      str       	=					node_a.next

									if node_a is None or node_a is None:
										return

									UpperCAmelCase_	,    UpperCAmelCase_		:      str       	=					node_a.data, node_a.data


if __name__ == "__main__":
						lowerCAmelCase__										= LinkedList()
						for i in range(5, 0, -1):
												ll.push(i)

						ll.print_list()

						ll.swap_nodes(1, 4)
						print("After swapping")
						ll.print_list()









'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "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 					lowercase						(  a_ ):
							_lowerCamelCase		:      Any=   "xlm-roberta-xl"


							def __init__(			self    ,			_snake_case=25_0880    ,			_snake_case=2560    ,			_snake_case=36    ,			_snake_case=32    ,			_snake_case=1_0240    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=514    ,			_snake_case=1    ,			_snake_case=0.02    ,			_snake_case=1e-05    ,			_snake_case=1    ,			_snake_case=0    ,			_snake_case=2    ,			_snake_case="absolute"    ,			_snake_case=True    ,			_snake_case=None    ,			**_snake_case    ,			)							->				Optional[Any]:
								super().__init__(pad_token_id=_snake_case    ,			bos_token_id=_snake_case    ,			eos_token_id=_snake_case    ,			**_snake_case)
								UpperCAmelCase_		:      List[str]       	=					vocab_size
								UpperCAmelCase_		:      int       	=					hidden_size
								UpperCAmelCase_		:      List[Any]       	=					num_hidden_layers
								UpperCAmelCase_		:      Optional[int]       	=					num_attention_heads
								UpperCAmelCase_		:      Union[str, Any]       	=					hidden_act
								UpperCAmelCase_		:      int       	=					intermediate_size
								UpperCAmelCase_		:      Tuple       	=					hidden_dropout_prob
								UpperCAmelCase_		:      List[str]       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      str       	=					max_position_embeddings
								UpperCAmelCase_		:      Union[str, Any]       	=					type_vocab_size
								UpperCAmelCase_		:      str       	=					initializer_range
								UpperCAmelCase_		:      List[str]       	=					layer_norm_eps
								UpperCAmelCase_		:      Dict       	=					position_embedding_type
								UpperCAmelCase_		:      int       	=					use_cache
								UpperCAmelCase_		:      List[Any]       	=					classifier_dropout



class 					lowercase						(  a_ ):


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






