






from __future__ import annotations

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

lowerCAmelCase_           = TypeVar('T')
lowerCAmelCase_           = TypeVar('U')
class      UpperCamelCase (				Generic[T, U] ):







		"""simple docstring"""



		def __init__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       T | None						,_SCREAMING_SNAKE_CASE		:       U | None		)    ->   Union[str, Any]:


						'''simple docstring'''
						A	 =      key
						A	 =      val
						A	 =      None
						A	 =      None







		def __repr__(  self		:       List[Any]		)    ->   str:


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


class      UpperCamelCase (				Generic[T, U] ):







		"""simple docstring"""



		def __init__(  self		:       List[Any]		)    ->   None:


						'''simple docstring'''
						A	 =      DoubleLinkedListNode(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
						A	 =      DoubleLinkedListNode(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
						A ,		A	 =      self.rear, self.head
		def __repr__(  self		:       List[str]		)    ->   str:


						'''simple docstring'''
						A	 =      ['DoubleLinkedList']
						A	 =      self.head
						while node.next is not None:
										rep.append(str(_SCREAMING_SNAKE_CASE		)		)
										A	 =      node.next
						rep.append(str(self.rear		)		)
						return ",\n    ".join(_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       str						,_SCREAMING_SNAKE_CASE		:       DoubleLinkedListNode[T, U]		)    ->   None:


						'''simple docstring'''
						A	 =      self.rear.prev

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

						A	 =      node
						A	 =      previous
						A	 =      node
						A	 =      self.rear







		def       A(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       DoubleLinkedListNode[T, U]		)    ->   DoubleLinkedListNode[T, U] | None:


						'''simple docstring'''

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

						A	 =      node.next
						A	 =      node.prev
						A	 =      None
						A	 =      None
						return node


class      UpperCamelCase (				Generic[T, U] ):







		"""simple docstring"""



		snake_case    =   {}
		def __init__(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   str:


						'''simple docstring'''
						A	 =      DoubleLinkedList()
						A	 =      capacity
						A	 =      0
						A	 =      0
						A	 =      0
						A	 =      {}
		def __repr__(  self		:       Tuple		)    ->   str:


						'''simple docstring'''
						return (
						    f'CacheInfo(hits={self.hits}, misses={self.miss}, '
						    f'capacity={self.capacity}, current size={self.num_keys})'
						)
		def __contains__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       T		)    ->   bool:


						'''simple docstring'''
						return key in self.cache
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       T		)    ->   U | None:


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

						if key in self.cache:
										self.hits += 1
										A	 =      self.cache[key]
										A	 =      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(_SCREAMING_SNAKE_CASE		)
										return node.val
						self.miss += 1
						return None
		def       A(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       T						,_SCREAMING_SNAKE_CASE		:       U		)    ->   None:


						'''simple docstring'''

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

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







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


						'''simple docstring'''

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

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

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

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

										return cache_decorator_wrapper

						return cache_decorator_inner


if __name__ == "__main__":
					import doctest

					doctest.testmod()













import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







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

				return orig_key







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       str   ):
				for key in orig_state_dict.copy().keys():
								A	 =      orig_state_dict.pop(UpperCAmelCase   )

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												A	 =      val

				A	 =      orig_state_dict['cls.predictions.decoder.bias']
				A	 =      torch.arange(UpperCAmelCase   ).expand((1, -1)   ) + 2

				return orig_state_dict







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       str   ):
				A	 =      torch.load(UpperCAmelCase,						map_location='cpu'   )['model_state_dict']
				A	 =      YosoConfig.from_json_file(UpperCAmelCase   )
				A	 =      YosoForMaskedLM(UpperCAmelCase   )

				A	 =      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__":
					lowerCAmelCase_           = 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.'
					)
					lowerCAmelCase_           = parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





