



'''simple docstring'''
from __future__ import annotations

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

snake_case_					:      str    			=    TypeVar('T')
snake_case_					:      int    			=    TypeVar('U')






class 				lowercase__      (				Generic[T, U]  ):





	def __init__(					self   :							List[str]					,lowerCamelCase__   :							T | None					,lowerCamelCase__   :							U | None					):







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  key
							_UpperCamelCase   :						Union[str, Any]												=  val
							_UpperCamelCase   :						DoubleLinkedListNode[T, U] | None												=  None
							_UpperCamelCase   :						DoubleLinkedListNode[T, U] | None												=  None





	def __repr__(					self   :							str					):







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






class 				lowercase__      (				Generic[T, U]  ):





	def __init__(					self   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						DoubleLinkedListNode[T, U]												=  DoubleLinkedListNode(lowerCamelCase__					,lowerCamelCase__					)
							_UpperCamelCase   :						DoubleLinkedListNode[T, U]												=  DoubleLinkedListNode(lowerCamelCase__					,lowerCamelCase__					)
							_UpperCamelCase							,     _UpperCamelCase   :						Union[str, Any]												=  self.rear, self.head





	def __repr__(					self   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  ['DoubleLinkedList']
							_UpperCamelCase   :						str												=  self.head
							while node.next is not None:
													rep.append(str(lowerCamelCase__					)					)
													_UpperCamelCase   :						Union[str, Any]												=  node.next
							rep.append(str(self.rear					)					)
							return ",\n    ".join(lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Optional[Any]					,lowerCamelCase__   :							DoubleLinkedListNode[T, U]					):







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  self.rear.prev

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

							_UpperCamelCase   :						str												=  node
							_UpperCamelCase   :						Any												=  previous
							_UpperCamelCase   :						List[str]												=  node
							_UpperCamelCase   :						Dict												=  self.rear





	def       UpperCamelCase_    (					self   :							Optional[Any]					,lowerCamelCase__   :							DoubleLinkedListNode[T, U]					):







							'''simple docstring'''

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

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






class 				lowercase__      (				Generic[T, U]  ):
	lowercase__    							=     {}





	def __init__(					self   :							Any					,lowerCamelCase__   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						DoubleLinkedList[T, U]												=  DoubleLinkedList()
							_UpperCamelCase   :						str												=  capacity
							_UpperCamelCase   :						Any												=  0
							_UpperCamelCase   :						Any												=  0
							_UpperCamelCase   :						Any												=  0
							_UpperCamelCase   :						dict[T, DoubleLinkedListNode[T, U]]												=  {}





	def __repr__(					self   :							int					):







							'''simple docstring'''
							return (
							    F'CacheInfo(hits={self.hits}, misses={self.miss}, '
							    F'capacity={self.capacity}, current size={self.num_keys})'
							)





	def __contains__(					self   :							Dict					,lowerCamelCase__   :							T					):







							'''simple docstring'''
							return key in self.cache





	def       UpperCamelCase_    (					self   :							int					,lowerCamelCase__   :							T					):







							'''simple docstring'''
							# Note: pythonic interface would throw KeyError rather than return None

							if key in self.cache:
													self.hits += 1
													_UpperCamelCase   :						DoubleLinkedListNode[T, U]												=  self.cache[key]
													_UpperCamelCase   :						Dict												=  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(lowerCamelCase__					)
													return node.val
							self.miss += 1
							return None





	def       UpperCamelCase_    (					self   :							int					,lowerCamelCase__   :							T					,lowerCamelCase__   :							U					):







							'''simple docstring'''

							if key not in self.cache:
													if self.num_keys >= self.capacity:
																			# delete first node (oldest) when over capacity
																			_UpperCamelCase   :						str												=  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(lowerCamelCase__					) 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
													_UpperCamelCase   :						Any												=  DoubleLinkedListNode(lowerCamelCase__					,lowerCamelCase__					)
													self.list.add(self.cache[key]					)
													self.num_keys += 1

							else:
													# bump node to the end of the list, update value
													_UpperCamelCase   :						List[Any]												=  self.list.remove(self.cache[key]					)
													assert node is not None  # node guaranteed to be in list
													_UpperCamelCase   :						Union[str, Any]												=  value
													self.list.add(lowerCamelCase__					)





	@classmethod
	def       UpperCamelCase_    (					cls   :							Union[str, Any]					,lowerCamelCase__   :							int = 128					):







							'''simple docstring'''

							def cache_decorator_inner(lowerCamelCase__   :							Callable[[T], U]					) -> Callable[..., U]:
													def cache_decorator_wrapper(*lowerCamelCase__   :							T					) -> U:
																			if func not in cls.decorator_function_to_instance_map:
																									_UpperCamelCase   :						Dict												=  LRUCache(lowerCamelCase__					)

																			_UpperCamelCase   :						int												=  cls.decorator_function_to_instance_map[func].get(args[0]					)
																			if result is None:
																									_UpperCamelCase   :						List[Any]												=  func(*lowerCamelCase__					)
																									cls.decorator_function_to_instance_map[func].put(args[0]					,lowerCamelCase__					)
																			return result

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

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

													return cache_decorator_wrapper

							return cache_decorator_inner


if __name__ == "__main__":
					import doctest

					doctest.testmod()







'''simple docstring'''
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def A__       (	UpperCAmelCase_				):
						if "model" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('model.'    ,    ''				)
						if "norm1" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('norm1'    ,    'attention.output.LayerNorm'				)
						if "norm2" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.replace('norm2'    ,    'output.LayerNorm'				)
						if "norm" in orig_key:
												_UpperCamelCase   :						str												=  orig_key.replace('norm'    ,    'LayerNorm'				)
						if "transformer" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.split('.'				)[0].split('_'				)[-1]
												_UpperCamelCase   :						Tuple												=  orig_key.replace(f'transformer_{layer_num}'    ,    f'encoder.layer.{layer_num}'				)
						if "mha.attn" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('mha.attn'    ,    'attention.self'				)
						if "mha" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('mha'    ,    'attention'				)
						if "W_q" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('W_q'    ,    'self.query'				)
						if "W_k" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('W_k'    ,    'self.key'				)
						if "W_v" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('W_v'    ,    'self.value'				)
						if "ff1" in orig_key:
												_UpperCamelCase   :						Optional[Any]												=  orig_key.replace('ff1'    ,    'intermediate.dense'				)
						if "ff2" in orig_key:
												_UpperCamelCase   :						List[str]												=  orig_key.replace('ff2'    ,    'output.dense'				)
						if "ff" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('ff'    ,    'output.dense'				)
						if "mlm_class" in orig_key:
												_UpperCamelCase   :						Tuple												=  orig_key.replace('mlm.mlm_class'    ,    'cls.predictions.decoder'				)
						if "mlm" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.replace('mlm'    ,    'cls.predictions.transform'				)
						if "cls" not in orig_key:
												_UpperCamelCase   :						Optional[int]												=  'yoso.' + orig_key

						return orig_key






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						for key in orig_state_dict.copy().keys():
												_UpperCamelCase   :						Any												=  orig_state_dict.pop(UpperCAmelCase_				)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		_UpperCamelCase   :						Union[str, Any]												=  val

						_UpperCamelCase   :						Any												=  orig_state_dict['cls.predictions.decoder.bias']
						_UpperCamelCase   :						Union[str, Any]												=  torch.arange(UpperCAmelCase_				).expand((1, -1)				) + 2

						return orig_state_dict






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						List[Any]												=  torch.load(UpperCAmelCase_    ,    map_location='cpu'				)['model_state_dict']
						_UpperCamelCase   :						List[str]												=  YosoConfig.from_json_file(UpperCAmelCase_				)
						_UpperCamelCase   :						Union[str, Any]												=  YosoForMaskedLM(UpperCAmelCase_				)

						_UpperCamelCase   :						Tuple												=  convert_checkpoint_helper(config.max_position_embeddings    ,    UpperCAmelCase_				)

						print(model.load_state_dict(UpperCAmelCase_				)				)
						model.eval()
						model.save_pretrained(UpperCAmelCase_				)

						print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'				)


if __name__ == "__main__":
					snake_case_					:      int    			=    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					snake_case_					:      List[Any]    			=    parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


