

import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import YolosForObjectDetection, YolosModel
					from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor






class 	A_		:
							def __init__(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      List[str]=1_3       ,SCREAMING_SNAKE_CASE__		:      Dict=[3_0, 3_0]       ,SCREAMING_SNAKE_CASE__		:      Any=2       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=3       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=True       ,SCREAMING_SNAKE_CASE__		:      Any=True       ,SCREAMING_SNAKE_CASE__		:      str=3_2       ,SCREAMING_SNAKE_CASE__		:      Tuple=5       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=4       ,SCREAMING_SNAKE_CASE__		:      str=3_7       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]="gelu"       ,SCREAMING_SNAKE_CASE__		:      Any=0.1       ,SCREAMING_SNAKE_CASE__		:      List[Any]=0.1       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=1_0       ,SCREAMING_SNAKE_CASE__		:      str=0.02       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=3       ,SCREAMING_SNAKE_CASE__		:      List[str]=None       ,SCREAMING_SNAKE_CASE__		:      int=8       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=1_0       ,):
								__lowerCamelCase    :    int 	=			parent
								__lowerCamelCase    :    Any 	=			batch_size
								__lowerCamelCase    :    Tuple 	=			image_size
								__lowerCamelCase    :    List[Any] 	=			patch_size
								__lowerCamelCase    :    Tuple 	=			num_channels
								__lowerCamelCase    :    List[Any] 	=			is_training
								__lowerCamelCase    :    Any 	=			use_labels
								__lowerCamelCase    :    Union[str, Any] 	=			hidden_size
								__lowerCamelCase    :    Dict 	=			num_hidden_layers
								__lowerCamelCase    :    Optional[int] 	=			num_attention_heads
								__lowerCamelCase    :    Optional[int] 	=			intermediate_size
								__lowerCamelCase    :    List[Any] 	=			hidden_act
								__lowerCamelCase    :    Optional[Any] 	=			hidden_dropout_prob
								__lowerCamelCase    :    Tuple 	=			attention_probs_dropout_prob
								__lowerCamelCase    :    Union[str, Any] 	=			type_sequence_label_size
								__lowerCamelCase    :    int 	=			initializer_range
								__lowerCamelCase    :    Optional[Any] 	=			num_labels
								__lowerCamelCase    :    Any 	=			scope
								__lowerCamelCase    :    Optional[Any] 	=			n_targets
								__lowerCamelCase    :    Union[str, Any] 	=			num_detection_tokens
								# we set the expected sequence length (which is used in several tests)
								# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
								__lowerCamelCase    :    Optional[Any] 	=			(image_size[1] // patch_size) * (image_size[0] // patch_size)
								__lowerCamelCase    :    Tuple 	=			num_patches + 1 + self.num_detection_tokens
							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    Any 	=			floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])

								__lowerCamelCase    :    Union[str, Any] 	=			None
								if self.use_labels:
									# labels is a list of Dict (each Dict being the labels for a given example in the batch)
									__lowerCamelCase    :    Any 	=			[]
									for i in range(self.batch_size):
										__lowerCamelCase    :    Optional[Any] 	=			{}
										__lowerCamelCase    :    List[Any] 	=			torch.randint(
										    high=self.num_labels       ,size=(self.n_targets,)       ,device=SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    int 	=			torch.rand(self.n_targets       ,4       ,device=SCREAMING_SNAKE_CASE__)
										labels.append(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Optional[int] 	=			self.get_config()

								return config, pixel_values, labels
							def    lowerCAmelCase					(		self		:      Tuple):
								return YolosConfig(
								    image_size=self.image_size       ,patch_size=self.patch_size       ,num_channels=self.num_channels       ,hidden_size=self.hidden_size       ,num_hidden_layers=self.num_hidden_layers       ,num_attention_heads=self.num_attention_heads       ,intermediate_size=self.intermediate_size       ,hidden_act=self.hidden_act       ,hidden_dropout_prob=self.hidden_dropout_prob       ,attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,is_decoder=SCREAMING_SNAKE_CASE__       ,initializer_range=self.initializer_range       ,num_detection_tokens=self.num_detection_tokens       ,num_labels=self.num_labels       ,)
							def    lowerCAmelCase					(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
								__lowerCamelCase    :    int 	=			YolosModel(config=SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()
								__lowerCamelCase    :    Union[str, Any] 	=			model(SCREAMING_SNAKE_CASE__)
								self.parent.assertEqual(
								    result.last_hidden_state.shape       ,(self.batch_size, self.expected_seq_len, self.hidden_size))
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]):
								__lowerCamelCase    :    int 	=			YolosForObjectDetection(SCREAMING_SNAKE_CASE__)
								model.to(SCREAMING_SNAKE_CASE__)
								model.eval()

								__lowerCamelCase    :    Union[str, Any] 	=			model(pixel_values=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Dict 	=			model(SCREAMING_SNAKE_CASE__)

								self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_detection_tokens, self.num_labels + 1))
								self.parent.assertEqual(result.pred_boxes.shape       ,(self.batch_size, self.num_detection_tokens, 4))

								__lowerCamelCase    :    Dict 	=			model(pixel_values=SCREAMING_SNAKE_CASE__       ,labels=SCREAMING_SNAKE_CASE__)

								self.parent.assertEqual(result.loss.shape       ,())
								self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_detection_tokens, self.num_labels + 1))
								self.parent.assertEqual(result.pred_boxes.shape       ,(self.batch_size, self.num_detection_tokens, 4))


							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Optional[Any] 	=			self.prepare_config_and_inputs()
								__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Tuple 	=			config_and_inputs
								__lowerCamelCase    :    Any 	=			{'pixel_values': pixel_values}
								return config, inputs_dict






@require_torch
class 	A_		(	SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  Optional[Any]										=				(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
							_UpperCAmelCase   :  str										=				(
							    {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
							)

							_UpperCAmelCase   :  List[Any]										=				False
							_UpperCAmelCase   :  Any										=				False
							_UpperCAmelCase   :  Union[str, Any]										=				False
							_UpperCAmelCase   :  List[str]										=				False
							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      int=False):
								__lowerCamelCase    :    Optional[int] 	=			super()._prepare_for_class(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,return_labels=SCREAMING_SNAKE_CASE__)

								if return_labels:
									if model_class.__name__ == "YolosForObjectDetection":
										__lowerCamelCase    :    int 	=			[]
										for i in range(self.model_tester.batch_size):
											__lowerCamelCase    :    Optional[Any] 	=			{}
											__lowerCamelCase    :    Union[str, Any] 	=			torch.ones(
											    size=(self.model_tester.n_targets,)       ,device=SCREAMING_SNAKE_CASE__       ,dtype=torch.long)
											__lowerCamelCase    :    Tuple 	=			torch.ones(
											    self.model_tester.n_targets       ,4       ,device=SCREAMING_SNAKE_CASE__       ,dtype=torch.float)
											labels.append(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Union[str, Any] 	=			labels

								return inputs_dict
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    int 	=			YolosModelTester(self)
								__lowerCamelCase    :    Dict 	=			ConfigTester(self       ,config_class=SCREAMING_SNAKE_CASE__       ,has_text_modality=SCREAMING_SNAKE_CASE__       ,hidden_size=3_7)
							def    lowerCAmelCase					(		self		:      Any):
								self.config_tester.run_common_tests()
							def    lowerCAmelCase					(		self		:      Tuple):
								# YOLOS does not use inputs_embeds
								pass
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase	,			__lowerCamelCase    :    Any 	=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase    :    Any 	=			model_class(SCREAMING_SNAKE_CASE__)
									self.assertIsInstance(model.get_input_embeddings()       ,(nn.Module))
									__lowerCamelCase    :    Dict 	=			model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__       ,nn.Linear))
							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase    :    List[Any] 	=			model_class(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    List[Any] 	=			inspect.signature(model.forward)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									__lowerCamelCase    :    List[str] 	=			[*signature.parameters.keys()]

									__lowerCamelCase    :    Optional[Any] 	=			['pixel_values']
									self.assertListEqual(arg_names[:1]       ,SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    List[Any] 	=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			self.model_tester.prepare_config_and_inputs_for_common()
								__lowerCamelCase    :    Dict 	=			True

								# in YOLOS, the seq_len is different
								__lowerCamelCase    :    Union[str, Any] 	=			self.model_tester.expected_seq_len
								for model_class in self.all_model_classes:
									__lowerCamelCase    :    int 	=			True
									__lowerCamelCase    :    Dict 	=			False
									__lowerCamelCase    :    Optional[Any] 	=			True
									__lowerCamelCase    :    Dict 	=			model_class(SCREAMING_SNAKE_CASE__)
									model.to(SCREAMING_SNAKE_CASE__)
									model.eval()
									with torch.no_grad():
										__lowerCamelCase    :    List[Any] 	=			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__))
									__lowerCamelCase    :    List[str] 	=			outputs.attentions
									self.assertEqual(len(SCREAMING_SNAKE_CASE__)       ,self.model_tester.num_hidden_layers)

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									__lowerCamelCase    :    Optional[int] 	=			True
									__lowerCamelCase    :    str 	=			model_class(SCREAMING_SNAKE_CASE__)
									model.to(SCREAMING_SNAKE_CASE__)
									model.eval()
									with torch.no_grad():
										__lowerCamelCase    :    Any 	=			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__))
									__lowerCamelCase    :    List[str] 	=			outputs.attentions
									self.assertEqual(len(SCREAMING_SNAKE_CASE__)       ,self.model_tester.num_hidden_layers)

									self.assertListEqual(
									    list(attentions[0].shape[-3:])       ,[self.model_tester.num_attention_heads, seq_len, seq_len]       ,)
									__lowerCamelCase    :    Any 	=			len(SCREAMING_SNAKE_CASE__)

									# Check attention is always last and order is fine
									__lowerCamelCase    :    Union[str, Any] 	=			True
									__lowerCamelCase    :    Union[str, Any] 	=			True
									__lowerCamelCase    :    int 	=			model_class(SCREAMING_SNAKE_CASE__)
									model.to(SCREAMING_SNAKE_CASE__)
									model.eval()
									with torch.no_grad():
										__lowerCamelCase    :    int 	=			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__))

									__lowerCamelCase    :    Any 	=			1
									self.assertEqual(out_len + added_hidden_states       ,len(SCREAMING_SNAKE_CASE__))

									__lowerCamelCase    :    Optional[Any] 	=			outputs.attentions

									self.assertEqual(len(SCREAMING_SNAKE_CASE__)       ,self.model_tester.num_hidden_layers)
									self.assertListEqual(
									    list(self_attentions[0].shape[-3:])       ,[self.model_tester.num_attention_heads, seq_len, seq_len]       ,)
							def    lowerCAmelCase					(		self		:      Optional[int]):
								def check_hidden_states_output(SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      str):
									__lowerCamelCase    :    Any 	=			model_class(SCREAMING_SNAKE_CASE__)
									model.to(SCREAMING_SNAKE_CASE__)
									model.eval()

									with torch.no_grad():
										__lowerCamelCase    :    int 	=			model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__))

									__lowerCamelCase    :    Dict 	=			outputs.hidden_states

									__lowerCamelCase    :    List[Any] 	=			getattr(
									    self.model_tester       ,'expected_num_hidden_layers'       ,self.model_tester.num_hidden_layers + 1)
									self.assertEqual(len(SCREAMING_SNAKE_CASE__)       ,SCREAMING_SNAKE_CASE__)

									# YOLOS has a different seq_length
									__lowerCamelCase    :    str 	=			self.model_tester.expected_seq_len

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:])       ,[seq_length, self.model_tester.hidden_size]       ,)

								__lowerCamelCase	,			__lowerCamelCase    :    int 	=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase    :    Optional[int] 	=			True
									check_hidden_states_output(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									__lowerCamelCase    :    Optional[int] 	=			True

									check_hidden_states_output(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    Optional[Any] 	=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_object_detection(*SCREAMING_SNAKE_CASE__)


							@slow
							def    lowerCAmelCase					(		self		:      Dict):
								for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCamelCase    :    Optional[int] 	=			YolosModel.from_pretrained(SCREAMING_SNAKE_CASE__)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE__)






def        SCREAMING_SNAKE_CASE__	(  )  ->					Union[str, Any]:
	__lowerCamelCase    :    Any 	=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
	return image






@require_torch
@require_vision
class 	A_		(	unittest.TestCase    ):
							@cached_property
							def    lowerCAmelCase					(		self		:      Tuple):
								return AutoImageProcessor.from_pretrained('hustvl/yolos-small') if is_vision_available() else None


							@slow
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Optional[Any] 	=			YolosForObjectDetection.from_pretrained('hustvl/yolos-small').to(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[str] 	=			self.default_image_processor
								__lowerCamelCase    :    Any 	=			prepare_img()
								__lowerCamelCase    :    Tuple 	=			image_processor(images=SCREAMING_SNAKE_CASE__       ,return_tensors='pt').to(SCREAMING_SNAKE_CASE__)

								# forward pass
								with torch.no_grad():
									__lowerCamelCase    :    List[Any] 	=			model(inputs.pixel_values)

								# verify outputs
								__lowerCamelCase    :    Union[str, Any] 	=			torch.Size((1, 1_0_0, 9_2))
								self.assertEqual(outputs.logits.shape       ,SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Union[str, Any] 	=			torch.tensor(
								    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]       ,device=SCREAMING_SNAKE_CASE__       ,)
								__lowerCamelCase    :    Union[str, Any] 	=			torch.tensor(
								    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]       ,device=SCREAMING_SNAKE_CASE__)
								self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]       ,SCREAMING_SNAKE_CASE__       ,atol=1E-4))
								self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]       ,SCREAMING_SNAKE_CASE__       ,atol=1E-4))

								# verify postprocessing
								__lowerCamelCase    :    Dict 	=			image_processor.post_process_object_detection(
								    SCREAMING_SNAKE_CASE__       ,threshold=0.3       ,target_sizes=[image.size[::-1]])[0]
								__lowerCamelCase    :    Optional[int] 	=			torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]).to(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[str] 	=			[7_5, 7_5, 1_7, 6_3, 1_7]
								__lowerCamelCase    :    Union[str, Any] 	=			torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]).to(SCREAMING_SNAKE_CASE__)

								self.assertEqual(len(results['scores'])       ,5)
								self.assertTrue(torch.allclose(results['scores']       ,SCREAMING_SNAKE_CASE__       ,atol=1E-4))
								self.assertSequenceEqual(results['labels'].tolist()       ,SCREAMING_SNAKE_CASE__)
								self.assertTrue(torch.allclose(results['boxes'][0, :]       ,SCREAMING_SNAKE_CASE__))







from __future__ import annotations

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

a		=TypeVar("""T""")
a		=TypeVar("""U""")






class 	A_		(	Generic[T, U]    ):
							def __init__(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      T | None       ,SCREAMING_SNAKE_CASE__		:      U | None):
								__lowerCamelCase    :    Optional[Any] 	=			key
								__lowerCamelCase    :    Tuple 	=			val
								__lowerCamelCase    :    DoubleLinkedListNode[T, U] | None 	=			None
								__lowerCamelCase    :    DoubleLinkedListNode[T, U] | None 	=			None


							def __repr__(		self		:      Optional[int]):
								return (
								    F"Node: key: {self.key}, val: {self.val}, "
								    F"has next: {bool(self.next)}, has prev: {bool(self.prev)}"
								)






class 	A_		(	Generic[T, U]    ):
							def __init__(		self		:      List[Any]):
								__lowerCamelCase    :    DoubleLinkedListNode[T, U] 	=			DoubleLinkedListNode(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    DoubleLinkedListNode[T, U] 	=			DoubleLinkedListNode(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase	,			__lowerCamelCase    :    List[Any] 	=			self.rear, self.head
							def __repr__(		self		:      Optional[int]):
								__lowerCamelCase    :    int 	=			['DoubleLinkedList']
								__lowerCamelCase    :    Tuple 	=			self.head
								while node.next is not None:
									rep.append(str(SCREAMING_SNAKE_CASE__))
									__lowerCamelCase    :    Union[str, Any] 	=			node.next
								rep.append(str(self.rear))
								return ",\n    ".join(SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      DoubleLinkedListNode[T, U]):
								__lowerCamelCase    :    str 	=			self.rear.prev

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

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


							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      DoubleLinkedListNode[T, U]):

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

								__lowerCamelCase    :    Union[str, Any] 	=			node.next
								__lowerCamelCase    :    Dict 	=			node.prev
								__lowerCamelCase    :    Optional[int] 	=			None
								__lowerCamelCase    :    List[str] 	=			None
								return node






class 	A_		(	Generic[T, U]    ):
							_UpperCAmelCase   :  dict[Callable[[T], U], LRUCache[T, U]]										=				{}
							def __init__(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      int):
								__lowerCamelCase    :    DoubleLinkedList[T, U] 	=			DoubleLinkedList()
								__lowerCamelCase    :    List[Any] 	=			capacity
								__lowerCamelCase    :    Optional[int] 	=			0
								__lowerCamelCase    :    int 	=			0
								__lowerCamelCase    :    Optional[Any] 	=			0
								__lowerCamelCase    :    dict[T, DoubleLinkedListNode[T, U]] 	=			{}
							def __repr__(		self		:      Optional[Any]):
								return (
								    F"CacheInfo(hits={self.hits}, misses={self.miss}, "
								    F"capacity={self.capacity}, current size={self.num_keys})"
								)
							def __contains__(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      T):
								return key in self.cache
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      T):
								# Note: pythonic interface would throw KeyError rather than return None

								if key in self.cache:
									self.hits += 1
									__lowerCamelCase    :    DoubleLinkedListNode[T, U] 	=			self.cache[key]
									__lowerCamelCase    :    List[str] 	=			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    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      T       ,SCREAMING_SNAKE_CASE__		:      U):

								if key not in self.cache:
									if self.num_keys >= self.capacity:
										# delete first node (oldest) when over capacity
										__lowerCamelCase    :    Optional[Any] 	=			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
									__lowerCamelCase    :    List[Any] 	=			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
									__lowerCamelCase    :    List[str] 	=			self.list.remove(self.cache[key])
									assert node is not None  # node guaranteed to be in list
									__lowerCamelCase    :    List[Any] 	=			value
									self.list.add(SCREAMING_SNAKE_CASE__)


							@classmethod
							def    lowerCAmelCase					(		cls		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      int = 1_2_8):

								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:
											__lowerCamelCase    :    str 	=			LRUCache(SCREAMING_SNAKE_CASE__)

										__lowerCamelCase    :    Dict 	=			cls.decorator_function_to_instance_map[func].get(args[0])
										if result is None:
											__lowerCamelCase    :    str 	=			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()




