





'''simple docstring'''






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


A   : Optional[Any]             =			logging.get_logger(__name__)

A   : Tuple             =			{
    'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
        'https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}







class 			lowerCamelCase					(						__UpperCAmelCase  ):
     _SCREAMING_SNAKE_CASE							   =    "trajectory_transformer"
     _SCREAMING_SNAKE_CASE							   =    ["past_key_values"]
     _SCREAMING_SNAKE_CASE							   =    {
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }


     def __init__(					self	:  Dict       ,     __snake_case	:  Dict=1_00       ,     __snake_case	:  Tuple=5       ,     __snake_case	:  Tuple=1       ,     __snake_case	:  List[str]=1       ,     __snake_case	:  str=2_49       ,     __snake_case	:  Optional[Any]=6       ,     __snake_case	:  str=17       ,     __snake_case	:  Dict=25       ,     __snake_case	:  Union[str, Any]=4       ,     __snake_case	:  Any=4       ,     __snake_case	:  Dict=1_28       ,     __snake_case	:  Dict=0.1       ,     __snake_case	:  Union[str, Any]=0.1       ,     __snake_case	:  Optional[Any]=0.1       ,     __snake_case	:  List[Any]=0.0_006       ,     __snake_case	:  Any=5_12       ,     __snake_case	:  Optional[int]=0.02       ,     __snake_case	:  List[Any]=1e-12       ,     __snake_case	:  List[str]=1       ,     __snake_case	:  List[Any]=True       ,     __snake_case	:  Dict=1       ,     __snake_case	:  List[Any]=5_02_56       ,     __snake_case	:  str=5_02_56       ,     **__snake_case	:  int       ,     ):





           '''simple docstring'''





           _snake_case:   List[Any]  					=		vocab_size
           _snake_case:   Optional[Any]  					=		action_weight
           _snake_case:   Any  					=		reward_weight
           _snake_case:   str  					=		value_weight
           _snake_case:   Union[str, Any]  					=		max_position_embeddings
           _snake_case:   Optional[int]  					=		block_size
           _snake_case:   int  					=		action_dim
           _snake_case:   Tuple  					=		observation_dim
           _snake_case:   Dict  					=		transition_dim
           _snake_case:   int  					=		learning_rate
           _snake_case:   Optional[int]  					=		n_layer
           _snake_case:   Optional[int]  					=		n_head
           _snake_case:   List[Any]  					=		n_embd
           _snake_case:   List[Any]  					=		embd_pdrop
           _snake_case:   Union[str, Any]  					=		attn_pdrop
           _snake_case:   Dict  					=		resid_pdrop
           _snake_case:   List[Any]  					=		initializer_range
           _snake_case:   List[Any]  					=		layer_norm_eps
           _snake_case:   int  					=		kaiming_initializer_range
           _snake_case:   Optional[Any]  					=		use_cache
           super().__init__(pad_token_id=__snake_case       ,     bos_token_id=__snake_case       ,     eos_token_id=__snake_case       ,     **__snake_case       )












'''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]"





