




'''simple docstring'''




from __future__ import annotations

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

UpperCAmelCase_       				=      TypeVar('T')
UpperCAmelCase_       				=      TypeVar('U')

class 						lowerCAmelCase_   ( Generic[T, U]   ):







  '''simple docstring'''







  def __init__(						self    :	str						,		_UpperCAmelCase    :	T | None						,		_UpperCAmelCase    :	U | None ):






   """simple docstring"""






   UpperCAmelCase__										=    key
   UpperCAmelCase__										=    val
   UpperCAmelCase__										=    0
   UpperCAmelCase__										=    None
   UpperCAmelCase__										=    None



  def __repr__(						self    :	List[str] ):






   """simple docstring"""






   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 						lowerCAmelCase_   ( Generic[T, U]   ):







  '''simple docstring'''







  def __init__(						self    :	Union[str, Any] ):






   """simple docstring"""






   UpperCAmelCase__										=    DoubleLinkedListNode(_UpperCAmelCase						,		_UpperCAmelCase )
   UpperCAmelCase__										=    DoubleLinkedListNode(_UpperCAmelCase						,		_UpperCAmelCase )
   UpperCAmelCase__							,			UpperCAmelCase__										=    self.rear, self.head


  def __repr__(						self    :	Any ):






   """simple docstring"""






   UpperCAmelCase__										=    ["""DoubleLinkedList"""]
   UpperCAmelCase__										=    self.head
   while node.next is not None:
    rep.append(str(_UpperCAmelCase ) )
    UpperCAmelCase__										=    node.next
   rep.append(str(self.rear ) )
   return ",\n    ".join(_UpperCAmelCase )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	DoubleLinkedListNode[T, U] ):






   """simple docstring"""






   UpperCAmelCase__										=    self.rear.prev

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

   UpperCAmelCase__										=    node
   UpperCAmelCase__										=    previous
   UpperCAmelCase__										=    node
   UpperCAmelCase__										=    self.rear
   node.freq += 1
   self._position_node(_UpperCAmelCase )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[Any]						,		_UpperCAmelCase    :	DoubleLinkedListNode[T, U] ):






   """simple docstring"""






   while node.prev is not None and node.prev.freq > node.freq:
    # swap node with previous node
    UpperCAmelCase__										=    node.prev

    UpperCAmelCase__										=    previous_node.prev
    UpperCAmelCase__										=    node.prev
    UpperCAmelCase__										=    previous_node
    UpperCAmelCase__										=    node



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int]						,		_UpperCAmelCase    :	DoubleLinkedListNode[T, U] ):






   """simple docstring"""






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

   UpperCAmelCase__										=    node.next
   UpperCAmelCase__										=    node.prev
   UpperCAmelCase__										=    None
   UpperCAmelCase__										=    None
   return node

class 						lowerCAmelCase_   ( Generic[T, U]   ):







  '''simple docstring'''







  lowerCAmelCase_				:					dict[Callable[[T], U], LFUCache[T, U]]				     =						{}


  def __init__(						self    :	str						,		_UpperCAmelCase    :	int ):






   """simple docstring"""






   UpperCAmelCase__										=    DoubleLinkedList()
   UpperCAmelCase__										=    capacity
   UpperCAmelCase__										=    0
   UpperCAmelCase__										=    0
   UpperCAmelCase__										=    0
   UpperCAmelCase__										=    {}


  def __repr__(						self    :	Dict ):






   """simple docstring"""






   return (
       f'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
       f'''capacity={self.capacity}, current_size={self.num_keys})'''
   )


  def __contains__(						self    :	Union[str, Any]						,		_UpperCAmelCase    :	T ):






   """simple docstring"""






   return key in self.cache


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any]						,		_UpperCAmelCase    :	T ):






   """simple docstring"""






   if key in self.cache:
    self.hits += 1
    UpperCAmelCase__										=    self.cache[key]
    UpperCAmelCase__										=    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(_UpperCAmelCase )
    return node.val
   self.miss += 1
   return None


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	T						,		_UpperCAmelCase    :	U ):






   """simple docstring"""






   if key not in self.cache:
    if self.num_keys >= self.capacity:
     # delete first node when over capacity
     UpperCAmelCase__										=    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(_UpperCAmelCase ) is not None
     # first_node guaranteed to be in list

     del self.cache[first_node.key]
     self.num_keys -= 1
    UpperCAmelCase__										=    DoubleLinkedListNode(_UpperCAmelCase						,		_UpperCAmelCase )
    self.list.add(self.cache[key] )
    self.num_keys += 1

   else:
    UpperCAmelCase__										=    self.list.remove(self.cache[key] )
    assert node is not None  # node guaranteed to be in list
    UpperCAmelCase__										=    value
    self.list.add(_UpperCAmelCase )



  @classmethod
  def 							SCREAMING_SNAKE_CASE__			(						cls    :	type[LFUCache[T, U]]						,		_UpperCAmelCase    :	int = 1_28 ):






   """simple docstring"""






   def cache_decorator_inner(_UpperCAmelCase    :	Callable[[T], U] ) -> Callable[..., U]:
    def cache_decorator_wrapper(*_UpperCAmelCase    :	T ) -> U:
     if func not in cls.decorator_function_to_instance_map:
      UpperCAmelCase__										=    LFUCache(_UpperCAmelCase )

     UpperCAmelCase__										=    cls.decorator_function_to_instance_map[func].get(args[0] )
     if result is None:
      UpperCAmelCase__										=    func(*_UpperCAmelCase )
      cls.decorator_function_to_instance_map[func].put(args[0]						,		_UpperCAmelCase )
     return result

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

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

    return cache_decorator_wrapper

   return cache_decorator_inner


if __name__ == "__main__":
    import doctest

    doctest.testmod()






'''simple docstring'''




import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      List[str]       ,						SCREAMING_SNAKE_CASE__			:      str       ,						SCREAMING_SNAKE_CASE__			:      Tuple       ):
 '''simple docstring'''





 UpperCAmelCase__										=    TaConfig.from_json_file(SCREAMING_SNAKE_CASE__       )
 print(F'''Building PyTorch model from configuration: {config}'''       )
 UpperCAmelCase__										=    TaForConditionalGeneration(SCREAMING_SNAKE_CASE__       )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       )

 # Save pytorch-model
 print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
 model.save_pretrained(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
    UpperCAmelCase_       				=      argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    UpperCAmelCase_       				=      parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
