





'''simple docstring'''

from __future__ import annotations

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

lowercase_      							=					TypeVar("""T""")


class    a_	(							Generic[T]			):

    '''simple docstring'''
    UpperCamelCase					       =					42  # Cache store of keys
    UpperCamelCase					       =					42  # References of the keys in cache
    UpperCamelCase					       =					10  # Maximum capacity of cache


    def __init__(						self					,						A			)						->  int:
        _SCREAMING_SNAKE_CASE   							=     deque()
        _SCREAMING_SNAKE_CASE   							=     set()
        if not n:
            _SCREAMING_SNAKE_CASE   							=     sys.maxsize
        elif n < 0:
            raise ValueError("""n should be an integer greater than 0."""			)
        else:
            _SCREAMING_SNAKE_CASE   							=     n


    def      snake_case_(						self					,						A			)						->  Union[str, Any]:
        if x not in self.key_reference:
            if len(self.dq_store			) == LRUCache._MAX_CAPACITY:
                _SCREAMING_SNAKE_CASE   							=     self.dq_store.pop()
                self.key_reference.remove(a__			)
        else:
            self.dq_store.remove(a__			)

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


    def      snake_case_(						self			)						->  Optional[int]:
        for k in self.dq_store:
            print(a__			)





    def __repr__(						self			)						->  int:
        return f'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store			)}'


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      lowercase_      							=					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]"








from collections import OrderedDict
from typing import Mapping

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


_lowercase    	=		logging.get_logger(__name__)

_lowercase    	=		{
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''xlm-roberta'''




     def __init__(					self			,    a__=3_0_5_2_2			,    a__=7_6_8			,    a__=1_2			,    a__=1_2			,    a__=3_0_7_2			,    a__="gelu"			,    a__=0.1			,    a__=0.1			,    a__=5_1_2			,    a__=2			,    a__=0.0_2			,    a__=1e-12			,    a__=1			,    a__=0			,    a__=2			,    a__="absolute"			,    a__=True			,    a__=None			,    **a__			,    ):
         super().__init__(pad_token_id=a__			,    bos_token_id=a__			,    eos_token_id=a__			,    **a__)

         A__					=       vocab_size
         A__					=       hidden_size
         A__					=       num_hidden_layers
         A__					=       num_attention_heads
         A__					=       hidden_act
         A__					=       intermediate_size
         A__					=       hidden_dropout_prob
         A__					=       attention_probs_dropout_prob
         A__					=       max_position_embeddings
         A__					=       type_vocab_size
         A__					=       initializer_range
         A__					=       layer_norm_eps
         A__					=       position_embedding_type
         A__					=       use_cache
         A__					=       classifier_dropout







class      _UpperCAmelCase      (							A__  ):




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