
"""simple docstring"""





from __future__ import annotations

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

__magic_name__       =			TypeVar("T")
__magic_name__       =			TypeVar("U")
class 						SCREAMING_SNAKE_CASE_ (						Generic[T, U] ):




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = key
				__SCREAMING_SNAKE_CASE            = val
				__SCREAMING_SNAKE_CASE            = None
				__SCREAMING_SNAKE_CASE            = 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 						SCREAMING_SNAKE_CASE_ (						Generic[T, U] ):




		"""simple docstring"""





		def __init__(     self):
				__SCREAMING_SNAKE_CASE            = DoubleLinkedListNode(__UpperCAmelCase							,  __UpperCAmelCase)
				__SCREAMING_SNAKE_CASE            = DoubleLinkedListNode(__UpperCAmelCase							,  __UpperCAmelCase)
				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = self.rear, self.head




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




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = self.rear.prev

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

				__SCREAMING_SNAKE_CASE            = node
				__SCREAMING_SNAKE_CASE            = previous
				__SCREAMING_SNAKE_CASE            = node
				__SCREAMING_SNAKE_CASE            = self.rear




		def 	snake_case_     (     self							,  lowerCAmelCase__):

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

				__SCREAMING_SNAKE_CASE            = node.next
				__SCREAMING_SNAKE_CASE            = node.prev
				__SCREAMING_SNAKE_CASE            = None
				__SCREAMING_SNAKE_CASE            = None
				return node



class 						SCREAMING_SNAKE_CASE_ (						Generic[T, U] ):




		"""simple docstring"""





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




		def __init__(     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = DoubleLinkedList()
				__SCREAMING_SNAKE_CASE            = capacity
				__SCREAMING_SNAKE_CASE            = 0
				__SCREAMING_SNAKE_CASE            = 0
				__SCREAMING_SNAKE_CASE            = 0
				__SCREAMING_SNAKE_CASE            = {}




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




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




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

				if key in self.cache:
						self.hits += 1
						__SCREAMING_SNAKE_CASE            = self.cache[key]
						__SCREAMING_SNAKE_CASE            = 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 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__):

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

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

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




		@classmethod
		def 	snake_case_     (     cls							,  lowerCAmelCase__ = 1_2_8):

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

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

						def cache_info() -> LRUCache[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

import torch

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


logging.set_verbosity_info()






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							AlbertConfig.from_json_file(UpperCamelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							A__		   =							AlbertForPreTraining(UpperCamelCase__ )

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

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}''' )
							torch.save(model.state_dict()				,			UpperCamelCase__ )


if __name__ == "__main__":
				__lowerCamelCase							 =     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."
				)
				__lowerCamelCase							 =     parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





