






'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_					= logging.get_logger(__name__)

a_					= {
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							"""pegasus"""
 snake_case_  							=							["""past_key_values"""]
 snake_case_  							=							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




 def __init__(      self	:							Optional[Any]					,      __lowercase	:							List[Any]=5_02_65					,      __lowercase	:							Union[str, Any]=10_24					,      __lowercase	:							Optional[Any]=12					,      __lowercase	:							List[str]=40_96					,      __lowercase	:							int=16					,      __lowercase	:							List[Any]=12					,      __lowercase	:							Any=40_96					,      __lowercase	:							List[str]=16					,      __lowercase	:							Any=0.0					,      __lowercase	:							Union[str, Any]=0.0					,      __lowercase	:							str=True					,      __lowercase	:							Dict=True					,      __lowercase	:							Optional[Any]="gelu"					,      __lowercase	:							Union[str, Any]=10_24					,      __lowercase	:							Optional[int]=0.1					,      __lowercase	:							List[Any]=0.0					,      __lowercase	:							Any=0.0					,      __lowercase	:							Tuple=0.02					,      __lowercase	:							Union[str, Any]=0					,      __lowercase	:							Tuple=False					,      __lowercase	:							str=0					,      __lowercase	:							Optional[int]=1					,      __lowercase	:							List[Any]=1					,      **__lowercase	:							Tuple					,      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				int									=vocab_size
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=max_position_embeddings
       SCREAMING_SNAKE_CASE__     :				List[str]									=d_model
       SCREAMING_SNAKE_CASE__     :				Tuple									=encoder_ffn_dim
       SCREAMING_SNAKE_CASE__     :				str									=encoder_layers
       SCREAMING_SNAKE_CASE__     :				List[str]									=encoder_attention_heads
       SCREAMING_SNAKE_CASE__     :				Any									=decoder_ffn_dim
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=decoder_layers
       SCREAMING_SNAKE_CASE__     :				Any									=decoder_attention_heads
       SCREAMING_SNAKE_CASE__     :				List[Any]									=dropout
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=attention_dropout
       SCREAMING_SNAKE_CASE__     :				Any									=activation_dropout
       SCREAMING_SNAKE_CASE__     :				List[Any]									=activation_function
       SCREAMING_SNAKE_CASE__     :				str									=init_std
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=encoder_layerdrop
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=decoder_layerdrop
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=use_cache
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=encoder_layers
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=scale_embedding  # scale factor will be sqrt(d_model) if True
       super().__init__(
           pad_token_id=__lowercase					,      eos_token_id=__lowercase					,      is_encoder_decoder=__lowercase					,      decoder_start_token_id=__lowercase					,      forced_eos_token_id=__lowercase					,      **__lowercase					,      )




 @property
 def      __magic_name__	(      self	:							List[Any]      )	->						int:
       return self.encoder_attention_heads




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						int:
       return self.d_model







'''simple docstring'''
from __future__ import annotations

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

a_					= TypeVar('T')






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




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




 def      __magic_name__	(      self	:							List[str]					,      __lowercase	:							T      )	->						None:
       if x not in self.key_reference:
             if len(self.dq_store      ) == LRUCache._MAX_CAPACITY:
                   SCREAMING_SNAKE_CASE__     :				int									=self.dq_store.pop()
                   self.key_reference.remove(__lowercase      )
       else:
             self.dq_store.remove(__lowercase      )

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




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						None:
       for k in self.dq_store:
             print(__lowercase      )




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


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  a_					= 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]"