


from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

_snake_case   = TypeVar("T")
_snake_case   = TypeVar("U")




class    A_   ( Generic[T, U]     ):







 """simple docstring"""





 def __init__(			self					: int       ,    _a					: Any       ,    _a					: Union[str, Any]				)    ->			Dict:
      UpperCAmelCase						:				Any		  =			key
      UpperCAmelCase						:				Tuple		  =			val
      UpperCAmelCase						:				int		  =			0
      UpperCAmelCase						:				DoubleLinkedListNode[T, U] | None		  =			None
      UpperCAmelCase						:				DoubleLinkedListNode[T, U] | None		  =			None

 def __repr__(			self					: str				)    ->			str:
      return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
          self.key       ,    self.val       ,    self.freq       ,    self.next is not None       ,    self.prev is not None				)




class    A_   ( Generic[T, U]     ):







 """simple docstring"""





 def __init__(			self					: List[Any]				)    ->			None:
      UpperCAmelCase						:				DoubleLinkedListNode[T, U]		  =			DoubleLinkedListNode(_a       ,    _a				)
      UpperCAmelCase						:				DoubleLinkedListNode[T, U]		  =			DoubleLinkedListNode(_a       ,    _a				)
      UpperCAmelCase						:				Any		  =			self.rear, self.head
 def __repr__(			self					: Optional[Any]				)    ->			str:
      UpperCAmelCase						:				Union[str, Any]		  =			["DoubleLinkedList"]
      UpperCAmelCase						:				List[Any]		  =			self.head
      while node.next is not None:
           rep.append(str(_a				)				)
           UpperCAmelCase						:				List[Any]		  =			node.next
      rep.append(str(self.rear				)				)
      return ",\n    ".join(_a				)
 def   _UpperCamelCase    (			self					: Union[str, Any]       ,    _a					: Dict				)    ->			None:
      UpperCAmelCase						:				Dict		  =			self.rear.prev

      # All nodes other than self.head are guaranteed to have non-None previous
      assert previous is not None

      UpperCAmelCase						:				int		  =			node
      UpperCAmelCase						:				List[str]		  =			previous
      UpperCAmelCase						:				Optional[int]		  =			node
      UpperCAmelCase						:				int		  =			self.rear
      node.freq += 1
      self._position_node(_a				)
 def   _UpperCamelCase    (			self					: List[str]       ,    _a					: Tuple				)    ->			None:






      while node.prev is not None and node.prev.freq > node.freq:
           # swap node with previous node
           UpperCAmelCase						:				List[Any]		  =			node.prev

           UpperCAmelCase						:				Dict		  =			previous_node.prev
           UpperCAmelCase						:				Union[str, Any]		  =			node.prev
           UpperCAmelCase						:				Union[str, Any]		  =			previous_node
           UpperCAmelCase						:				int		  =			node

 def   _UpperCamelCase    (			self					: int       ,    _a					: int				)    ->			DoubleLinkedListNode[T, U] | None:






      if node.prev is None or node.next is None:
           return None

      UpperCAmelCase						:				Optional[int]		  =			node.next
      UpperCAmelCase						:				Optional[Any]		  =			node.prev
      UpperCAmelCase						:				Optional[int]		  =			None
      UpperCAmelCase						:				Any		  =			None
      return node




class    A_   ( Generic[T, U]     ):







 """simple docstring"""





 _UpperCamelCase				: dict[Callable[[T], U], LFUCache[T, U]]      =					{}
 def __init__(			self					: Optional[int]       ,    _a					: Union[str, Any]				)    ->			Tuple:
      UpperCAmelCase						:				DoubleLinkedList[T, U]		  =			DoubleLinkedList()
      UpperCAmelCase						:				str		  =			capacity
      UpperCAmelCase						:				str		  =			0
      UpperCAmelCase						:				Optional[int]		  =			0
      UpperCAmelCase						:				Any		  =			0
      UpperCAmelCase						:				dict[T, DoubleLinkedListNode[T, U]]		  =			{}
 def __repr__(			self					: Tuple				)    ->			str:
      return (
          f'CacheInfo(hits={self.hits}, misses={self.miss}, '
          f'capacity={self.capacity}, current_size={self.num_keys})'
      )
 def __contains__(			self					: Optional[int]       ,    _a					: str				)    ->			bool:
      return key in self.cache
 def   _UpperCamelCase    (			self					: Optional[int]       ,    _a					: Optional[int]				)    ->			U | None:






      if key in self.cache:
           self.hits += 1
           UpperCAmelCase						:				DoubleLinkedListNode[T, U]		  =			self.cache[key]
           UpperCAmelCase						:				Union[str, Any]		  =			self.list.remove(self.cache[key]				)
           assert node == value_node

           # node is guaranteed not None because it is in self.cache
           assert node is not None
           self.list.add(_a				)
           return node.val
      self.miss += 1
      return None
 def   _UpperCamelCase    (			self					: int       ,    _a					: List[str]       ,    _a					: Tuple				)    ->			None:






      if key not in self.cache:
           if self.num_keys >= self.capacity:
                # delete first node when over capacity
                UpperCAmelCase						:				Optional[Any]		  =			self.list.head.next

                # guaranteed to have a non-None first node when num_keys > 0
                # explain to type checker via assertions
                assert first_node is not None
                assert first_node.key is not None
                assert self.list.remove(_a				) is not None
                # first_node guaranteed to be in list

                del self.cache[first_node.key]
                self.num_keys -= 1
           UpperCAmelCase						:				Union[str, Any]		  =			DoubleLinkedListNode(_a       ,    _a				)
           self.list.add(self.cache[key]				)
           self.num_keys += 1

      else:
           UpperCAmelCase						:				Union[str, Any]		  =			self.list.remove(self.cache[key]				)
           assert node is not None  # node guaranteed to be in list
           UpperCAmelCase						:				Optional[Any]		  =			value
           self.list.add(_a				)

 @classmethod
 def   _UpperCamelCase    (			cls					: int       ,    _a					: int = 1_2_8				)    ->			Callable[[Callable[[T], U]], Callable[..., U]]:






      def cache_decorator_inner(_a					: Tuple				) -> Callable[..., U]:
           def cache_decorator_wrapper(*_a					: List[str]				) -> U:
                if func not in cls.decorator_function_to_instance_map:
                     UpperCAmelCase						:				str		  =			LFUCache(_a				)

                UpperCAmelCase						:				Dict		  =			cls.decorator_function_to_instance_map[func].get(args[0]				)
                if result is None:
                     UpperCAmelCase						:				Optional[int]		  =			func(*_a				)
                     cls.decorator_function_to_instance_map[func].put(args[0]       ,    _a				)
                return result

           def cache_info() -> LFUCache[T, U]:
                return cls.decorator_function_to_instance_map[func]

           setattr(_a       ,    "cache_info"       ,    _a				)  # noqa: B010

           return cache_decorator_wrapper

      return cache_decorator_inner


if __name__ == "__main__":
      import doctest

      doctest.testmod()








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,
)


_snake_case   = {"configuration_t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config", "T5OnnxConfig"]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = ["T5Tokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = ["T5TokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "T5_PRETRAINED_MODEL_ARCHIVE_LIST",
          "T5EncoderModel",
          "T5ForConditionalGeneration",
          "T5Model",
          "T5PreTrainedModel",
          "load_tf_weights_in_t5",
          "T5ForQuestionAnswering",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFT5EncoderModel",
          "TFT5ForConditionalGeneration",
          "TFT5Model",
          "TFT5PreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case   = [
          "FlaxT5EncoderModel",
          "FlaxT5ForConditionalGeneration",
          "FlaxT5Model",
          "FlaxT5PreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_ta import TaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_ta_fast import TaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ta import (
                T5_PRETRAINED_MODEL_ARCHIVE_LIST,
                TaEncoderModel,
                TaForConditionalGeneration,
                TaForQuestionAnswering,
                TaModel,
                TaPreTrainedModel,
                load_tf_weights_in_ta,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_ta import (
                TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFTaEncoderModel,
                TFTaForConditionalGeneration,
                TFTaModel,
                TFTaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_ta import (
                FlaxTaEncoderModel,
                FlaxTaForConditionalGeneration,
                FlaxTaModel,
                FlaxTaPreTrainedModel,
            )


else:
      import sys

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



