
from __future__ import annotations

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

UpperCAmelCase       =					TypeVar('''T''')
UpperCAmelCase       =					TypeVar('''U''')







class 			A_  (     Generic[T, U]  ):




	'''simple docstring'''


	def __init__(			self						,       snake_case						,       snake_case			):
		lowercase	    =       key
		lowercase	    =       val
		lowercase	    =       None
		lowercase	    =       None





	def __repr__(			self			):
		return (
		    F'''Node: key: {self.key}, val: {self.val}, '''
		    F'''has next: {bool(self.next			)}, has prev: {bool(self.prev			)}'''
		)







class 			A_  (     Generic[T, U]  ):




	'''simple docstring'''


	def __init__(			self			):
		lowercase	    =       DoubleLinkedListNode(snake_case						,       snake_case			)
		lowercase	    =       DoubleLinkedListNode(snake_case						,       snake_case			)
		lowercase    ,   lowercase	    =       self.rear, self.head





	def __repr__(			self			):
		lowercase	    =       ['DoubleLinkedList']
		lowercase	    =       self.head
		while node.next is not None:
			rep.append(str(snake_case			)			)
			lowercase	    =       node.next
		rep.append(str(self.rear			)			)
		return ",\n    ".join(snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		lowercase	    =       self.rear.prev

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

		lowercase	    =       node
		lowercase	    =       previous
		lowercase	    =       node
		lowercase	    =       self.rear





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):

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

		lowercase	    =       node.next
		lowercase	    =       node.prev
		lowercase	    =       None
		lowercase	    =       None
		return node







class 			A_  (     Generic[T, U]  ):




	'''simple docstring'''


	_UpperCamelCase	:	dict[Callable[[T], U], LRUCache[T, U]]									=			{}





	def __init__(			self						,       snake_case			):
		lowercase	    =       DoubleLinkedList()
		lowercase	    =       capacity
		lowercase	    =       0
		lowercase	    =       0
		lowercase	    =       0
		lowercase	    =       {}





	def __repr__(			self			):
		return (
		    F'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
		    F'''capacity={self.capacity}, current size={self.num_keys})'''
		)





	def __contains__(			self						,       snake_case			):
		return key in self.cache





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		# Note: pythonic interface would throw KeyError rather than return None

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





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case			):

		if key not in self.cache:
			if self.num_keys >= self.capacity:
				# delete first node (oldest) when over capacity
				lowercase	    =       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(snake_case			) is not None
				)  # node guaranteed to be in list assert node.key is not None

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

		else:
			# bump node to the end of the list, update value
			lowercase	    =       self.list.remove(self.cache[key]			)
			assert node is not None  # node guaranteed to be in list
			lowercase	    =       value
			self.list.add(snake_case			)





	@classmethod
	def       SCREAMING_SNAKE_CASE__		(			cls						,       snake_case = 128			):

		def cache_decorator_inner(snake_case			) -> Callable[..., U]:
			def cache_decorator_wrapper(*snake_case			) -> U:
				if func not in cls.decorator_function_to_instance_map:
					lowercase	    =       LRUCache(snake_case			)

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

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

			setattr(snake_case						,       'cache_info'						,       snake_case			)  # noqa: B010

			return cache_decorator_wrapper

		return cache_decorator_inner


if __name__ == "__main__":
	import doctest

	doctest.testmod()









import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   # Initialise PyTorch model
   UpperCamelCase_						=  AlbertConfig.from_json_file(__lowercase)
   print(f"""Building PyTorch model from configuration: {config}""")
   UpperCamelCase_						=  AlbertForPreTraining(__lowercase)

   # Load weights from tf checkpoint
   load_tf_weights_in_albert(__lowercase     ,   __lowercase     ,   __lowercase)

   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}""")
   torch.save(model.state_dict()     ,   __lowercase)


if __name__ == "__main__":
 snake_case__   :						Tuple													=   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(
     """--albert_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained ALBERT model. \n"""
         """This specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 snake_case__   :						str													=   parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





