

import json
import pathlib
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import YolosImageProcessor






class 	A_		(	unittest.TestCase    ):
							def __init__(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      int=7       ,SCREAMING_SNAKE_CASE__		:      Tuple=3       ,SCREAMING_SNAKE_CASE__		:      str=3_0       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=4_0_0       ,SCREAMING_SNAKE_CASE__		:      str=True       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=None       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=True       ,SCREAMING_SNAKE_CASE__		:      str=[0.5, 0.5, 0.5]       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=[0.5, 0.5, 0.5]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=True       ,SCREAMING_SNAKE_CASE__		:      int=1 / 2_5_5       ,SCREAMING_SNAKE_CASE__		:      str=True       ,):
								# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
								__lowerCamelCase    :    Dict 	=			size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
								__lowerCamelCase    :    Optional[Any] 	=			parent
								__lowerCamelCase    :    Dict 	=			batch_size
								__lowerCamelCase    :    int 	=			num_channels
								__lowerCamelCase    :    Optional[Any] 	=			min_resolution
								__lowerCamelCase    :    Union[str, Any] 	=			max_resolution
								__lowerCamelCase    :    Union[str, Any] 	=			do_resize
								__lowerCamelCase    :    str 	=			size
								__lowerCamelCase    :    Optional[Any] 	=			do_normalize
								__lowerCamelCase    :    Tuple 	=			image_mean
								__lowerCamelCase    :    Optional[int] 	=			image_std
								__lowerCamelCase    :    List[Any] 	=			do_rescale
								__lowerCamelCase    :    Any 	=			rescale_factor
								__lowerCamelCase    :    Union[str, Any] 	=			do_pad
							def    lowerCAmelCase					(		self		:      Any):
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_rescale": self.do_rescale,
								    "rescale_factor": self.rescale_factor,
								    "do_pad": self.do_pad,
								}


							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      int=False):
								if not batched:
									__lowerCamelCase    :    Optional[int] 	=			image_inputs[0]
									if isinstance(SCREAMING_SNAKE_CASE__       ,Image.Image):
										__lowerCamelCase    :    Tuple 	=			image.size
									else:
										__lowerCamelCase    :    str 	=			image.shape[1], image.shape[2]
									if w < h:
										__lowerCamelCase    :    Union[str, Any] 	=			int(self.size['shortest_edge'] * h / w)
										__lowerCamelCase    :    Any 	=			self.size['shortest_edge']
									elif w > h:
										__lowerCamelCase    :    Dict 	=			self.size['shortest_edge']
										__lowerCamelCase    :    List[Any] 	=			int(self.size['shortest_edge'] * w / h)
									else:
										__lowerCamelCase    :    Dict 	=			self.size['shortest_edge']
										__lowerCamelCase    :    Optional[Any] 	=			self.size['shortest_edge']

								else:
									__lowerCamelCase    :    Optional[Any] 	=			[]
									for image in image_inputs:
										__lowerCamelCase    :    Tuple 	=			self.get_expected_values([image])
										expected_values.append((expected_height, expected_width))
									__lowerCamelCase    :    Union[str, Any] 	=			max(SCREAMING_SNAKE_CASE__       ,key=lambda SCREAMING_SNAKE_CASE__: item[0])[0]
									__lowerCamelCase    :    List[Any] 	=			max(SCREAMING_SNAKE_CASE__       ,key=lambda SCREAMING_SNAKE_CASE__: item[1])[1]

								return expected_height, expected_width






@require_torch
@require_vision
class 	A_		(	SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  Dict										=				YolosImageProcessor if is_vision_available() else None
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    int 	=			YolosImageProcessingTester(self)
							@property
							def    lowerCAmelCase					(		self		:      List[Any]):
								return self.image_processor_tester.prepare_image_processor_dict()
							def    lowerCAmelCase					(		self		:      List[str]):
								__lowerCamelCase    :    Tuple 	=			self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,'image_mean'))
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,'image_std'))
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,'do_normalize'))
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,'do_resize'))
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__       ,'size'))
							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    Tuple 	=			self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size       ,{'shortest_edge': 1_8, 'longest_edge': 1_3_3_3})
								self.assertEqual(image_processor.do_pad       ,SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Dict 	=			self.image_processing_class.from_dict(
								    self.image_processor_dict       ,size=4_2       ,max_size=8_4       ,pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE__)
								self.assertEqual(image_processor.size       ,{'shortest_edge': 4_2, 'longest_edge': 8_4})
								self.assertEqual(image_processor.do_pad       ,SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Tuple):
								pass
							def    lowerCAmelCase					(		self		:      List[str]):
								# Initialize image_processing
								__lowerCamelCase    :    Any 	=			self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								__lowerCamelCase    :    Dict 	=			prepare_image_inputs(self.image_processor_tester       ,equal_resolution=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,Image.Image)

								# Test not batched input
								__lowerCamelCase    :    str 	=			image_processing(image_inputs[0]       ,return_tensors='pt').pixel_values

								__lowerCamelCase    :    List[str] 	=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__)

								self.assertEqual(
								    encoded_images.shape       ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,)

								# Test batched
								__lowerCamelCase    :    Dict 	=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__       ,batched=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Union[str, Any] 	=			image_processing(SCREAMING_SNAKE_CASE__       ,return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape       ,(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )       ,)
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								# Initialize image_processing
								__lowerCamelCase    :    Dict 	=			self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								__lowerCamelCase    :    Dict 	=			prepare_image_inputs(self.image_processor_tester       ,equal_resolution=SCREAMING_SNAKE_CASE__       ,numpify=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,np.ndarray)

								# Test not batched input
								__lowerCamelCase    :    Tuple 	=			image_processing(image_inputs[0]       ,return_tensors='pt').pixel_values

								__lowerCamelCase    :    List[str] 	=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__)

								self.assertEqual(
								    encoded_images.shape       ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,)

								# Test batched
								__lowerCamelCase    :    Tuple 	=			image_processing(SCREAMING_SNAKE_CASE__       ,return_tensors='pt').pixel_values

								__lowerCamelCase    :    Dict 	=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__       ,batched=SCREAMING_SNAKE_CASE__)

								self.assertEqual(
								    encoded_images.shape       ,(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )       ,)
							def    lowerCAmelCase					(		self		:      Tuple):
								# Initialize image_processing
								__lowerCamelCase    :    str 	=			self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								__lowerCamelCase    :    Union[str, Any] 	=			prepare_image_inputs(self.image_processor_tester       ,equal_resolution=SCREAMING_SNAKE_CASE__       ,torchify=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,torch.Tensor)

								# Test not batched input
								__lowerCamelCase    :    Dict 	=			image_processing(image_inputs[0]       ,return_tensors='pt').pixel_values

								__lowerCamelCase    :    Optional[Any] 	=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__)

								self.assertEqual(
								    encoded_images.shape       ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,)

								# Test batched
								__lowerCamelCase    :    List[Any] 	=			image_processing(SCREAMING_SNAKE_CASE__       ,return_tensors='pt').pixel_values

								__lowerCamelCase    :    str 	=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__       ,batched=SCREAMING_SNAKE_CASE__)

								self.assertEqual(
								    encoded_images.shape       ,(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )       ,)
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								# Initialize image_processings
								__lowerCamelCase    :    Optional[int] 	=			self.image_processing_class(**self.image_processor_dict)
								__lowerCamelCase    :    Dict 	=			self.image_processing_class(do_resize=SCREAMING_SNAKE_CASE__       ,do_normalize=SCREAMING_SNAKE_CASE__       ,do_rescale=SCREAMING_SNAKE_CASE__)
								# create random PyTorch tensors
								__lowerCamelCase    :    Union[str, Any] 	=			prepare_image_inputs(self.image_processor_tester       ,equal_resolution=SCREAMING_SNAKE_CASE__       ,torchify=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,torch.Tensor)

								# Test whether the method "pad" and calling the image processor return the same tensors
								__lowerCamelCase    :    List[Any] 	=			image_processing_a.pad(SCREAMING_SNAKE_CASE__       ,return_tensors='pt')
								__lowerCamelCase    :    Optional[Any] 	=			image_processing_a(SCREAMING_SNAKE_CASE__       ,return_tensors='pt')

								self.assertTrue(
								    torch.allclose(encoded_images_with_method['pixel_values']       ,encoded_images['pixel_values']       ,atol=1E-4))
							@slow
							def    lowerCAmelCase					(		self		:      Optional[int]):
								# prepare image and target
								__lowerCamelCase    :    Tuple 	=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
								with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'       ,'r') as f:
									__lowerCamelCase    :    List[str] 	=			json.loads(f.read())

								__lowerCamelCase    :    Optional[Any] 	=			{'image_id': 3_9_7_6_9, 'annotations': target}

								# encode them
								__lowerCamelCase    :    Union[str, Any] 	=			YolosImageProcessor.from_pretrained('hustvl/yolos-small')
								__lowerCamelCase    :    str 	=			image_processing(images=SCREAMING_SNAKE_CASE__       ,annotations=SCREAMING_SNAKE_CASE__       ,return_tensors='pt')

								# verify pixel values
								__lowerCamelCase    :    int 	=			torch.Size([1, 3, 8_0_0, 1_0_6_6])
								self.assertEqual(encoding['pixel_values'].shape       ,SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[str] 	=			torch.tensor([0.2796, 0.3138, 0.3481])
								self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,SCREAMING_SNAKE_CASE__       ,atol=1E-4))

								# verify area
								__lowerCamelCase    :    int 	=			torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
								self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,SCREAMING_SNAKE_CASE__))
								# verify boxes
								__lowerCamelCase    :    Any 	=			torch.Size([6, 4])
								self.assertEqual(encoding['labels'][0]['boxes'].shape       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[Any] 	=			torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
								self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,SCREAMING_SNAKE_CASE__       ,atol=1E-3))
								# verify image_id
								__lowerCamelCase    :    Tuple 	=			torch.tensor([3_9_7_6_9])
								self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,SCREAMING_SNAKE_CASE__))
								# verify is_crowd
								__lowerCamelCase    :    Optional[int] 	=			torch.tensor([0, 0, 0, 0, 0, 0])
								self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,SCREAMING_SNAKE_CASE__))
								# verify class_labels
								__lowerCamelCase    :    int 	=			torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7])
								self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,SCREAMING_SNAKE_CASE__))
								# verify orig_size
								__lowerCamelCase    :    Dict 	=			torch.tensor([4_8_0, 6_4_0])
								self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,SCREAMING_SNAKE_CASE__))
								# verify size
								__lowerCamelCase    :    Tuple 	=			torch.tensor([8_0_0, 1_0_6_6])
								self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,SCREAMING_SNAKE_CASE__))


							@slow
							def    lowerCAmelCase					(		self		:      Optional[int]):
								# prepare image, target and masks_path
								__lowerCamelCase    :    Dict 	=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
								with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'       ,'r') as f:
									__lowerCamelCase    :    Dict 	=			json.loads(f.read())

								__lowerCamelCase    :    Dict 	=			{'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

								__lowerCamelCase    :    Optional[Any] 	=			pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic')

								# encode them
								__lowerCamelCase    :    Union[str, Any] 	=			YolosImageProcessor(format='coco_panoptic')
								__lowerCamelCase    :    Any 	=			image_processing(images=SCREAMING_SNAKE_CASE__       ,annotations=SCREAMING_SNAKE_CASE__       ,masks_path=SCREAMING_SNAKE_CASE__       ,return_tensors='pt')

								# verify pixel values
								__lowerCamelCase    :    Union[str, Any] 	=			torch.Size([1, 3, 8_0_0, 1_0_6_6])
								self.assertEqual(encoding['pixel_values'].shape       ,SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Optional[int] 	=			torch.tensor([0.2796, 0.3138, 0.3481])
								self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,SCREAMING_SNAKE_CASE__       ,atol=1E-4))

								# verify area
								__lowerCamelCase    :    Any 	=			torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
								self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,SCREAMING_SNAKE_CASE__))
								# verify boxes
								__lowerCamelCase    :    List[str] 	=			torch.Size([6, 4])
								self.assertEqual(encoding['labels'][0]['boxes'].shape       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[Any] 	=			torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
								self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,SCREAMING_SNAKE_CASE__       ,atol=1E-3))
								# verify image_id
								__lowerCamelCase    :    Any 	=			torch.tensor([3_9_7_6_9])
								self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,SCREAMING_SNAKE_CASE__))
								# verify is_crowd
								__lowerCamelCase    :    List[Any] 	=			torch.tensor([0, 0, 0, 0, 0, 0])
								self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,SCREAMING_SNAKE_CASE__))
								# verify class_labels
								__lowerCamelCase    :    Union[str, Any] 	=			torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3])
								self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,SCREAMING_SNAKE_CASE__))
								# verify masks
								__lowerCamelCase    :    List[Any] 	=			8_2_2_8_7_3
								self.assertEqual(encoding['labels'][0]['masks'].sum().item()       ,SCREAMING_SNAKE_CASE__)
								# verify orig_size
								__lowerCamelCase    :    List[str] 	=			torch.tensor([4_8_0, 6_4_0])
								self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,SCREAMING_SNAKE_CASE__))
								# verify size
								__lowerCamelCase    :    Optional[int] 	=			torch.tensor([8_0_0, 1_0_6_6])
								self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,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()




