






'''simple docstring'''


from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Optional[torch.FloatTensor]					     =					None
			__a							:							torch.FloatTensor					     =					None
			__a							:							Optional[Tuple[torch.FloatTensor]]					     =					None
			__a							:							Optional[Tuple[torch.FloatTensor]]					     =					None
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def __init__(   self							:						str ,      lowercase							:						List[Any]=1 ,      lowercase							:						str=0 ,      lowercase							:						Tuple=2 ,      lowercase							:						Optional[int]=5_1_2 ,      lowercase							:						List[Any]="cls" ,      lowercase							:						str=False ,      lowercase							:						str=True ,      **lowercase							:						int ,      )							->    Tuple:

									'''simple docstring'''







									super().__init__(pad_token_id=lowercase ,      bos_token_id=lowercase ,      eos_token_id=lowercase ,      **lowercase			)
									UpperCamelCase__         = project_dim
									UpperCamelCase__         = pooler_fn
									UpperCamelCase__         = learn_encoder
									UpperCamelCase__         = use_attention_mask
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Optional[int]					     =					[R"pooler", R"logit_scale"]
			__a							:							Dict					     =					[R"position_ids", R"predictions.decoder.bias"]
			__a							:							Any					     =					"roberta"
			__a							:							Tuple					     =					RobertaSeriesConfig






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

									'''simple docstring'''







									super().__init__(lowercase			)
									UpperCamelCase__         = XLMRobertaModel(lowercase			)
									UpperCamelCase__         = nn.Linear(config.hidden_size ,      config.project_dim			)
									UpperCamelCase__         = getattr(lowercase ,      """has_pre_transformation""" ,      lowercase			)
									if self.has_pre_transformation:
															UpperCamelCase__         = nn.Linear(config.hidden_size ,      config.project_dim			)
															UpperCamelCase__         = nn.LayerNorm(config.hidden_size ,      eps=config.layer_norm_eps			)
									self.post_init()







			def 				A      (   self							:						int ,      lowercase							:						Optional[torch.Tensor] = None ,      lowercase							:						Optional[torch.Tensor] = None ,      lowercase							:						Optional[torch.Tensor] = None ,      lowercase							:						Optional[torch.Tensor] = None ,      lowercase							:						Optional[torch.Tensor] = None ,      lowercase							:						Optional[torch.Tensor] = None ,      lowercase							:						Optional[torch.Tensor] = None ,      lowercase							:						Optional[torch.Tensor] = None ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[bool] = None ,      )							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = return_dict if return_dict is not None else self.config.use_return_dict

									UpperCamelCase__         = self.base_model(
									    input_ids=lowercase ,      attention_mask=lowercase ,      token_type_ids=lowercase ,      position_ids=lowercase ,      head_mask=lowercase ,      inputs_embeds=lowercase ,      encoder_hidden_states=lowercase ,      encoder_attention_mask=lowercase ,      output_attentions=lowercase ,      output_hidden_states=True if self.has_pre_transformation else output_hidden_states ,      return_dict=lowercase ,      )

									if self.has_pre_transformation:
															UpperCamelCase__         = outputs["""hidden_states"""][-2]
															UpperCamelCase__         = self.pre_LN(lowercase			)
															UpperCamelCase__         = self.transformation_pre(lowercase			)

															return TransformationModelOutput(
															    projection_state=lowercase ,      last_hidden_state=outputs.last_hidden_state ,      hidden_states=outputs.hidden_states ,      attentions=outputs.attentions ,      )
									else:
															UpperCamelCase__         = self.transformation(outputs.last_hidden_state			)
															return TransformationModelOutput(
															    projection_state=lowercase ,      last_hidden_state=outputs.last_hidden_state ,      hidden_states=outputs.hidden_states ,      attentions=outputs.attentions ,      )








'''simple docstring'''


from __future__ import annotations

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

lowerCamelCase_				: List[str]    	=							TypeVar('''T''')
lowerCamelCase_				: Optional[int]    	=							TypeVar('''U''')
class    _SCREAMING_SNAKE_CASE							(		Generic[T, U]	):


			'''simple docstring'''





			def __init__(   self							:						Dict ,      lowercase							:						T | None ,      lowercase							:						U | None			)							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = key
									UpperCamelCase__         = val
									UpperCamelCase__         = None
									UpperCamelCase__         = 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    _SCREAMING_SNAKE_CASE							(		Generic[T, U]	):


			'''simple docstring'''





			def __init__(   self							:						Union[str, Any]			)							->    None:

									'''simple docstring'''







									UpperCamelCase__         = DoubleLinkedListNode(lowercase ,      lowercase			)
									UpperCamelCase__         = DoubleLinkedListNode(lowercase ,      lowercase			)
									UpperCamelCase__    ,   UpperCamelCase__         = self.rear, self.head






			def __repr__(   self							:						int			)							->    str:

									'''simple docstring'''







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






			def 				A      (   self							:						str ,      lowercase							:						DoubleLinkedListNode[T, U]			)							->    None:

									'''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







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

									'''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    _SCREAMING_SNAKE_CASE							(		Generic[T, U]	):


			'''simple docstring'''





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






			def __init__(   self							:						int ,      lowercase							:						int			)							->    Union[str, Any]:

									'''simple docstring'''







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






			def __repr__(   self							:						Any			)							->    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 ,      lowercase							:						T			)							->    bool:

									'''simple docstring'''







									return key in self.cache






			def 				A      (   self							:						Tuple ,      lowercase							:						T			)							->    U | None:

									'''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(lowercase			)
															return node.val
									self.miss += 1
									return None






			def 				A      (   self							:						Dict ,      lowercase							:						T ,      lowercase							:						U			)							->    None:

									'''simple docstring'''







									if key not in self.cache:
															if self.num_keys >= self.capacity:
																					# delete first node (oldest) 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(lowercase			) 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__         = DoubleLinkedListNode(lowercase ,      lowercase			)
															self.list.add(self.cache[key]			)
															self.num_keys += 1

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







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

									'''simple docstring'''







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

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

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

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

															return cache_decorator_wrapper

									return cache_decorator_inner


if __name__ == "__main__":
					import doctest

					doctest.testmod()
