

"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__UpperCamelCase  :      Optional[Any]							      =		{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      List[Any]							      =		[
							    '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''RobertaPreLayerNormForCausalLM''',
							    '''RobertaPreLayerNormForMaskedLM''',
							    '''RobertaPreLayerNormForMultipleChoice''',
							    '''RobertaPreLayerNormForQuestionAnswering''',
							    '''RobertaPreLayerNormForSequenceClassification''',
							    '''RobertaPreLayerNormForTokenClassification''',
							    '''RobertaPreLayerNormModel''',
							    '''RobertaPreLayerNormPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      int							      =		[
							    '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFRobertaPreLayerNormForCausalLM''',
							    '''TFRobertaPreLayerNormForMaskedLM''',
							    '''TFRobertaPreLayerNormForMultipleChoice''',
							    '''TFRobertaPreLayerNormForQuestionAnswering''',
							    '''TFRobertaPreLayerNormForSequenceClassification''',
							    '''TFRobertaPreLayerNormForTokenClassification''',
							    '''TFRobertaPreLayerNormMainLayer''',
							    '''TFRobertaPreLayerNormModel''',
							    '''TFRobertaPreLayerNormPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      List[Any]							      =		[
							    '''FlaxRobertaPreLayerNormForCausalLM''',
							    '''FlaxRobertaPreLayerNormForMaskedLM''',
							    '''FlaxRobertaPreLayerNormForMultipleChoice''',
							    '''FlaxRobertaPreLayerNormForQuestionAnswering''',
							    '''FlaxRobertaPreLayerNormForSequenceClassification''',
							    '''FlaxRobertaPreLayerNormForTokenClassification''',
							    '''FlaxRobertaPreLayerNormModel''',
							    '''FlaxRobertaPreLayerNormPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_roberta_prelayernorm import (
							    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    RobertaPreLayerNormConfig,
							    RobertaPreLayerNormOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roberta_prelayernorm import (
														    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RobertaPreLayerNormForCausalLM,
														    RobertaPreLayerNormForMaskedLM,
														    RobertaPreLayerNormForMultipleChoice,
														    RobertaPreLayerNormForQuestionAnswering,
														    RobertaPreLayerNormForSequenceClassification,
														    RobertaPreLayerNormForTokenClassification,
														    RobertaPreLayerNormModel,
														    RobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roberta_prelayernorm import (
														    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRobertaPreLayerNormForCausalLM,
														    TFRobertaPreLayerNormForMaskedLM,
														    TFRobertaPreLayerNormForMultipleChoice,
														    TFRobertaPreLayerNormForQuestionAnswering,
														    TFRobertaPreLayerNormForSequenceClassification,
														    TFRobertaPreLayerNormForTokenClassification,
														    TFRobertaPreLayerNormMainLayer,
														    TFRobertaPreLayerNormModel,
														    TFRobertaPreLayerNormPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roberta_prelayernorm import (
														    FlaxRobertaPreLayerNormForCausalLM,
														    FlaxRobertaPreLayerNormForMaskedLM,
														    FlaxRobertaPreLayerNormForMultipleChoice,
														    FlaxRobertaPreLayerNormForQuestionAnswering,
														    FlaxRobertaPreLayerNormForSequenceClassification,
														    FlaxRobertaPreLayerNormForTokenClassification,
														    FlaxRobertaPreLayerNormModel,
														    FlaxRobertaPreLayerNormPreTrainedModel,
														)

else:
							import sys

							__UpperCamelCase  :      Union[str, Any]							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







"""simple docstring"""





from __future__ import annotations

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

__UpperCamelCase  :      Tuple							      =		TypeVar('''T''')







class 				SCREAMING_SNAKE_CASE					(		Generic[T]       ):






					"""simple docstring"""



					lowercase__				 =       42  # Cache store of keys
					lowercase__				 =       42  # References of the keys in cache
					lowercase__				 =       10  # Maximum capacity of cache







					def __init__(       self			:				Dict   ,lowercase_			:				int		):
						lowerCAmelCase__	:						str  				=	deque()
						lowerCAmelCase__	:						Any  				=	set()
						if not n:
							lowerCAmelCase__	:						Optional[Any]  				=	sys.maxsize
						elif n < 0:
							raise ValueError('''n should be an integer greater than 0.'''		)
						else:
							lowerCAmelCase__	:						int  				=	n







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				T		):
						if x not in self.key_reference:
							if len(self.dq_store		) == LRUCache._MAX_CAPACITY:
								lowerCAmelCase__	:						Any  				=	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        __lowerCAmelCase (       self			:				int		):
						for k in self.dq_store:
							print(lowercase_		)







					def __repr__(       self			:				Tuple		):
						return F'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store		)}'


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							__UpperCamelCase  :      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]"




