






"""simple docstring"""







from __future__ import annotations

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

_UpperCamelCase					      =  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     ,A ):
										UpperCAmelCase          =	deque()
										UpperCAmelCase          =	set()
										if not n:
																UpperCAmelCase          =	sys.maxsize
										elif n < 0:
																raise ValueError("""n should be an integer greater than 0.""" )
										else:
																UpperCAmelCase          =	n






				def     _UpperCamelCase							(			self     ,A ):
										if x not in self.key_reference:
																if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
																						UpperCAmelCase          =	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     _UpperCamelCase							(			self ):
										for k in self.dq_store:
																print(__lowercase )






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


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		_UpperCamelCase					      =  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]"











"""simple docstring"""







from typing import TYPE_CHECKING

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


_UpperCamelCase					      =  {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  ["""MBartTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  ["""MBartTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MBartForCausalLM""",
      """MBartForConditionalGeneration""",
      """MBartForQuestionAnswering""",
      """MBartForSequenceClassification""",
      """MBartModel""",
      """MBartPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """TFMBartForConditionalGeneration""",
      """TFMBartModel""",
      """TFMBartPreTrainedModel""",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _UpperCamelCase					      =  [
      """FlaxMBartForConditionalGeneration""",
      """FlaxMBartForQuestionAnswering""",
      """FlaxMBartForSequenceClassification""",
      """FlaxMBartModel""",
      """FlaxMBartPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbart import MBartTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbart_fast import MBartTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mbart import (
        MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
        MBartForCausalLM,
        MBartForConditionalGeneration,
        MBartForQuestionAnswering,
        MBartForSequenceClassification,
        MBartModel,
        MBartPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_mbart import (
        FlaxMBartForConditionalGeneration,
        FlaxMBartForQuestionAnswering,
        FlaxMBartForSequenceClassification,
        FlaxMBartModel,
        FlaxMBartPreTrainedModel,
    )

else:
  import sys

  _UpperCamelCase					      =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



