





'''simple docstring'''






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   : List[Any]             =			{
    'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json',
    'xlm-mlm-ende-1024': 'https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json',
    'xlm-mlm-enfr-1024': 'https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json',
    'xlm-mlm-enro-1024': 'https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json',
    'xlm-mlm-tlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json',
    'xlm-mlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json',
    'xlm-clm-enfr-1024': 'https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json',
    'xlm-clm-ende-1024': 'https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json',
    'xlm-mlm-17-1280': 'https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json',
    'xlm-mlm-100-1280': 'https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json',
}







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    "xlm"
					_SCREAMING_SNAKE_CASE							   =    {
					    "hidden_size": "emb_dim",
					    "num_attention_heads": "n_heads",
					    "num_hidden_layers": "n_layers",
					    "n_words": "vocab_size",  # For backward compatibility
					}


					def __init__(					self	:  Dict       ,     __snake_case	:  Union[str, Any]=3_01_45       ,     __snake_case	:  Any=20_48       ,     __snake_case	:  Union[str, Any]=12       ,     __snake_case	:  Dict=16       ,     __snake_case	:  Optional[int]=0.1       ,     __snake_case	:  List[str]=0.1       ,     __snake_case	:  str=True       ,     __snake_case	:  Optional[Any]=False       ,     __snake_case	:  Dict=False       ,     __snake_case	:  Tuple=False       ,     __snake_case	:  str=1       ,     __snake_case	:  Dict=True       ,     __snake_case	:  List[str]=5_12       ,     __snake_case	:  Dict=20_48**-0.5       ,     __snake_case	:  int=1e-12       ,     __snake_case	:  Any=0.02       ,     __snake_case	:  Optional[int]=0       ,     __snake_case	:  List[Any]=1       ,     __snake_case	:  Dict=2       ,     __snake_case	:  Union[str, Any]=3       ,     __snake_case	:  Optional[int]=5       ,     __snake_case	:  Tuple=True       ,     __snake_case	:  Optional[Any]="first"       ,     __snake_case	:  Any=True       ,     __snake_case	:  str=None       ,     __snake_case	:  Tuple=True       ,     __snake_case	:  Optional[Any]=0.1       ,     __snake_case	:  Any=5       ,     __snake_case	:  Dict=5       ,     __snake_case	:  str=0       ,     __snake_case	:  int=0       ,     __snake_case	:  str=2       ,     __snake_case	:  Any=0       ,     **__snake_case	:  Optional[Any]       ,     ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		vocab_size
											_snake_case:   str  					=		emb_dim
											_snake_case:   Union[str, Any]  					=		n_layers
											_snake_case:   str  					=		n_heads
											_snake_case:   Tuple  					=		dropout
											_snake_case:   Dict  					=		attention_dropout
											_snake_case:   int  					=		gelu_activation
											_snake_case:   Optional[int]  					=		sinusoidal_embeddings
											_snake_case:   Optional[int]  					=		causal
											_snake_case:   str  					=		asm
											_snake_case:   Tuple  					=		n_langs
											_snake_case:   Tuple  					=		use_lang_emb
											_snake_case:   List[str]  					=		layer_norm_eps
											_snake_case:   Union[str, Any]  					=		bos_index
											_snake_case:   Optional[Any]  					=		eos_index
											_snake_case:   Any  					=		pad_index
											_snake_case:   Dict  					=		unk_index
											_snake_case:   Optional[Any]  					=		mask_index
											_snake_case:   List[Any]  					=		is_encoder
											_snake_case:   str  					=		max_position_embeddings
											_snake_case:   Optional[int]  					=		embed_init_std
											_snake_case:   List[Any]  					=		init_std
											_snake_case:   int  					=		summary_type
											_snake_case:   int  					=		summary_use_proj
											_snake_case:   Tuple  					=		summary_activation
											_snake_case:   Any  					=		summary_proj_to_labels
											_snake_case:   str  					=		summary_first_dropout
											_snake_case:   Any  					=		start_n_top
											_snake_case:   Dict  					=		end_n_top
											_snake_case:   Optional[int]  					=		mask_token_id
											_snake_case:   int  					=		lang_id

											if "n_words" in kwargs:
																	_snake_case:   int  					=		kwargs['n_words']

											super().__init__(pad_token_id=__snake_case       ,     bos_token_id=__snake_case       ,     **__snake_case       )







class 			lowerCamelCase					(						__UpperCAmelCase  ):


					@property
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





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












'''simple docstring'''






from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

A   : str             =			TypeVar('T')







class 			lowerCamelCase					(						Generic[T]  ):
					_SCREAMING_SNAKE_CASE							   =    42  # Cache store of keys
					_SCREAMING_SNAKE_CASE							   =    42  # References of the keys in cache
					_SCREAMING_SNAKE_CASE							   =    10  # Maximum capacity of cache


					def __init__(					self	:  List[Any]       ,     __snake_case	:  int       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		deque()
											_snake_case:   Union[str, Any]  					=		set()
											if not n:
																	_snake_case:   Optional[int]  					=		sys.maxsize
											elif n < 0:
																	raise ValueError('n should be an integer greater than 0.'       )
											else:
																	_snake_case:   Tuple  					=		n


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ,     __snake_case	:  T       ):





											'''simple docstring'''





											if x not in self.key_reference:
																	if len(self.dq_store       ) == LRUCache._MAX_CAPACITY:
																							_snake_case:   int  					=		self.dq_store.pop()
																							self.key_reference.remove(__snake_case       )
											else:
																	self.dq_store.remove(__snake_case       )

											self.dq_store.appendleft(__snake_case       )
											self.key_reference.add(__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											for k in self.dq_store:
																	print(__snake_case       )




					def __repr__(					self	:  List[Any]       ):





											'''simple docstring'''





											return f'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store       )}'''


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		A   : LRUCache[str | int]             =			LRUCache(4)
		lru_cache.refer('A')
		lru_cache.refer(2)
		lru_cache.refer(3)
		lru_cache.refer('A')
		lru_cache.refer(4)
		lru_cache.refer(5)
		lru_cache.display()

		print(lru_cache)
		assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"





