from __future__ import annotations

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

_lowercase							:      List[Any]      							=TypeVar('''T''')



class  SCREAMING_SNAKE_CASE_ (					Generic[T]			):
  '''simple docstring'''


  lowercase		:     deque[T]  # Cache store of keys
  lowercase		:     set[T]  # References of the keys in cache
  lowercase		:     int            =				10  # Maximum capacity of cache







  def __init__(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				None:
         A						:     Optional[int]         =deque()
         A						:     List[str]         =set()
         if not n:
                A						:     Optional[Any]         =sys.maxsize
         elif n < 0:
                raise ValueError('n should be an integer greater than 0.'     )
         else:
                A						:     List[Any]         =n







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					T     )    ->				None:
         if x not in self.key_reference:
                if len(self.dq_store     ) == LRUCache._MAX_CAPACITY:
                       A						:     Any         =self.dq_store.pop()
                       self.key_reference.remove(SCREAMING_SNAKE_CASE__     )
         else:
                self.dq_store.remove(SCREAMING_SNAKE_CASE__     )

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				None:
         for k in self.dq_store:
                print(SCREAMING_SNAKE_CASE__     )







  def __repr__(				self    :					List[Any]     )    ->				str:
         return f'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store     )}'


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    _lowercase							:      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]"







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase							:      int      							=logging.get_logger(__name__)

_lowercase							:      Dict      							={
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				"xglm"
  lowercase		:     Any            =				["past_key_values"]

  lowercase		:     Dict            =				{
      "num_attention_heads": "attention_heads",
      "hidden_size": "d_model",
      "num_hidden_layers": "num_layers",
  }







  def __init__(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=25_60_08      ,	SCREAMING_SNAKE_CASE__    :					Dict=20_48      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=10_24      ,	SCREAMING_SNAKE_CASE__    :					str=40_96      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=24      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=16      ,	SCREAMING_SNAKE_CASE__    :					Any="gelu"      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.0      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					int=True      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					Any=2      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1      ,	SCREAMING_SNAKE_CASE__    :					str=0      ,	SCREAMING_SNAKE_CASE__    :					List[str]=2      ,	**SCREAMING_SNAKE_CASE__    :					Dict      ,	)    ->				int:
         A						:     str         =vocab_size
         A						:     Union[str, Any]         =max_position_embeddings
         A						:     Optional[Any]         =d_model
         A						:     Optional[int]         =ffn_dim
         A						:     int         =num_layers
         A						:     Any         =attention_heads
         A						:     Dict         =activation_function
         A						:     List[Any]         =dropout
         A						:     str         =attention_dropout
         A						:     List[Any]         =activation_dropout
         A						:     List[Any]         =layerdrop
         A						:     List[Any]         =init_std
         A						:     Union[str, Any]         =scale_embedding  # scale factor will be sqrt(d_model) if True
         A						:     List[str]         =use_cache

         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE__      ,	bos_token_id=SCREAMING_SNAKE_CASE__      ,	eos_token_id=SCREAMING_SNAKE_CASE__      ,	decoder_start_token_id=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__      ,	)






