





"""simple docstring"""






from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

A 			=  TypeVar('''T''')




class 				__lowercase			(			Generic[T]							):







					'''simple docstring'''




					__lowerCAmelCase  =  42  # Cache store of keys
					__lowerCAmelCase  =  42  # References of the keys in cache
					__lowerCAmelCase  =  10  # Maximum capacity of cache







					def __init__(						self	,			_UpperCAmelCase     ):
									__a :						List[Any]			       =  deque()
									__a :						Optional[Any]			       =  set()
									if not n:
													__a :						List[str]			       =  sys.maxsize
									elif n < 0:
													raise ValueError('''n should be an integer greater than 0.'''     )
									else:
													__a :						Dict			       =  n







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									if x not in self.key_reference:
													if len(self.dq_store     ) == LRUCache._MAX_CAPACITY:
																	__a :						List[str]			       =  self.dq_store.pop()
																	self.key_reference.remove(_UpperCAmelCase     )
									else:
													self.dq_store.remove(_UpperCAmelCase     )

									self.dq_store.appendleft(_UpperCAmelCase     )
									self.key_reference.add(_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									for k in self.dq_store:
													print(_UpperCAmelCase     )







					def __repr__(						self     ):
									return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store     )}"""


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		A 			=  LRUCache(4)
		lru_cache.refer('''A''')
		lru_cache.refer(2)
		lru_cache.refer(3)
		lru_cache.refer('''A''')
		lru_cache.refer(4)
		lru_cache.refer(5)
		lru_cache.display()

		print(lru_cache)
		assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"






"""simple docstring"""






import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A 			=  logging.get_logger(__name__)  # pylint: disable=invalid-name




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=768     ):
									super().__init__(_UpperCAmelCase     )
									__a :						str			       =  proj_size

									__a :						Optional[Any]			       =  CLIPVisionModel(_UpperCAmelCase     )
									__a :						List[Any]			       =  PaintByExampleMapper(_UpperCAmelCase     )
									__a :						int			       =  nn.LayerNorm(config.hidden_size     )
									__a :						List[Any]			       =  nn.Linear(config.hidden_size	,			self.proj_size     )

									# uncondition for scaling
									__a :						int			       =  nn.Parameter(torch.randn((1, 1, self.proj_size)     )     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=False     ):
									__a :						str			       =  self.model(pixel_values=_UpperCAmelCase     )
									__a :						Union[str, Any]			       =  clip_output.pooler_output
									__a :						Optional[int]			       =  self.mapper(latent_states[:, None]     )
									__a :						int			       =  self.final_layer_norm(_UpperCAmelCase     )
									__a :						Optional[Any]			       =  self.proj_out(_UpperCAmelCase     )
									if return_uncond_vector:
													return latent_states, self.uncond_vector

									return latent_states






class 				__lowercase			(			nn.Module							):







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase     ):
									super().__init__()
									__a :						List[str]			       =  (config.num_hidden_layers + 1) // 5
									__a :						Optional[Any]			       =  config.hidden_size
									__a :						str			       =  1
									__a :						Union[str, Any]			       =  nn.ModuleList(
									    [
									        BasicTransformerBlock(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			activation_fn='''gelu'''	,			attention_bias=_UpperCAmelCase     )
									        for _ in range(_UpperCAmelCase     )
									    ]     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									for block in self.blocks:
													__a :						Union[str, Any]			       =  block(_UpperCAmelCase     )

									return hidden_states