






import warnings

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


lowercase    				=   logging.get_logger(__name__)

lowercase    				=   {
    "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
    "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					'''xlnet'''
	lowerCAmelCase						=					['''mems''']
	lowerCAmelCase						=					{
	    '''n_token''': '''vocab_size''',  # Backward compatibility
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''n_head''',
	    '''num_hidden_layers''': '''n_layer''',
	}




	def __init__(			self			,     a=3_20_00			,     a=10_24			,     a=24			,     a=16			,     a=40_96			,     a="gelu"			,     a=True			,     a="bi"			,     a=0.02			,     a=1E-12			,     a=0.1			,     a=5_12			,     a=None			,     a=True			,     a=False			,     a=False			,     a=-1			,     a=False			,     a="last"			,     a=True			,     a="tanh"			,     a=0.1			,     a=5			,     a=5			,     a=5			,     a=1			,     a=2			,     **a			,     )			->       Union[str, Any]:
								snake_case_           =				vocab_size
								snake_case_           =				d_model
								snake_case_           =				n_layer
								snake_case_           =				n_head
								if d_model % n_head != 0:
															raise ValueError(F'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'''   )
								if "d_head" in kwargs:
															if kwargs["d_head"] != d_model // n_head:
																						raise ValueError(
																						    F'''`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})'''   )
								snake_case_           =				d_model // n_head
								snake_case_           =				ff_activation
								snake_case_           =				d_inner
								snake_case_           =				untie_r
								snake_case_           =				attn_type

								snake_case_           =				initializer_range
								snake_case_           =				layer_norm_eps

								snake_case_           =				dropout
								snake_case_           =				mem_len
								snake_case_           =				reuse_len
								snake_case_           =				bi_data
								snake_case_           =				clamp_len
								snake_case_           =				same_length

								snake_case_           =				summary_type
								snake_case_           =				summary_use_proj
								snake_case_           =				summary_activation
								snake_case_           =				summary_last_dropout
								snake_case_           =				start_n_top
								snake_case_           =				end_n_top

								snake_case_           =				bos_token_id
								snake_case_           =				pad_token_id
								snake_case_           =				eos_token_id

								if "use_cache" in kwargs:
															warnings.warn(
															    'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
															    ' instead.'			,     a			,     )
															snake_case_           =				kwargs['use_cache']

								snake_case_           =				use_mems_eval
								snake_case_           =				use_mems_train
								super().__init__(pad_token_id=a			,     bos_token_id=a			,     eos_token_id=a			,     **a   )




	@property
	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )
								return -1






	@max_position_embeddings.setter
	def 				_UpperCamelCase     (			self			,     a   )			->       str:
								# Message copied from Transformer-XL documentation
								raise NotImplementedError(
								    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )













from __future__ import annotations

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

lowercase    				=   TypeVar("T")




class 					UpperCamelCase_ (     Generic[T]							):



	'''simple docstring'''




	lowerCAmelCase						=					42  # Cache store of keys
	lowerCAmelCase						=					42  # References of the keys in cache
	lowerCAmelCase						=					1_0  # Maximum capacity of cache




	def __init__(			self			,     a   )			->       None:
								snake_case_           =				deque()
								snake_case_           =				set()
								if not n:
															snake_case_           =				sys.maxsize
								elif n < 0:
															raise ValueError('n should be an integer greater than 0.'   )
								else:
															snake_case_           =				n




	def 				_UpperCamelCase     (			self			,     a   )			->       None:
								if x not in self.key_reference:
															if len(self.dq_store   ) == LRUCache._MAX_CAPACITY:
																						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 				_UpperCamelCase     (			self   )			->       None:
								for k in self.dq_store:
															print(a   )






	def __repr__(			self   )			->       str:
								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]"





