



'''simple docstring'''



from __future__ import annotations

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

_lowercase       :						Tuple						=TypeVar("T")
_lowercase       :						str						=TypeVar("U")






class 			_SCREAMING_SNAKE_CASE				(Generic[T, U]      ):



			def __init__(     self		:						Union[str, Any]    ,			__UpperCamelCase		:						T | None    ,			__UpperCamelCase		:						U | None			)      ->      Dict:
							"""simple docstring"""





							snake_case__      :   Optional[int]      =    key
							snake_case__      :   Any      =    val
							snake_case__      :   int      =    0
							snake_case__      :   DoubleLinkedListNode[T, U] | None      =    None
							snake_case__      :   DoubleLinkedListNode[T, U] | None      =    None







			def __repr__(     self		:						Union[str, Any]			)      ->      str:
							"""simple docstring"""





							return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
							    self.key    ,			self.val    ,			self.freq    ,			self.next is not None    ,			self.prev is not None			)






class 			_SCREAMING_SNAKE_CASE				(Generic[T, U]      ):



			def __init__(     self		:						List[str]			)      ->      None:
							"""simple docstring"""





							snake_case__      :   DoubleLinkedListNode[T, U]      =    DoubleLinkedListNode(__UpperCamelCase    ,			__UpperCamelCase			)
							snake_case__      :   DoubleLinkedListNode[T, U]      =    DoubleLinkedListNode(__UpperCamelCase    ,			__UpperCamelCase			)
							snake_case__  ,      snake_case__      :   Tuple      =    self.rear, self.head



			def __repr__(     self		:						str			)      ->      str:
							"""simple docstring"""





							snake_case__      :   Union[str, Any]      =    ['''DoubleLinkedList''']
							snake_case__      :   Union[str, Any]      =    self.head
							while node.next is not None:
											rep.append(str(__UpperCamelCase			)			)
											snake_case__      :   List[Any]      =    node.next
							rep.append(str(self.rear			)			)
							return ",\n    ".join(__UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						DoubleLinkedListNode[T, U]			)      ->      None:
							"""simple docstring"""





							snake_case__      :   Tuple      =    self.rear.prev

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

							snake_case__      :   Dict      =    node
							snake_case__      :   str      =    previous
							snake_case__      :   List[Any]      =    node
							snake_case__      :   Optional[int]      =    self.rear
							node.freq += 1
							self._position_node(__UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						Optional[int]    ,			__UpperCamelCase		:						DoubleLinkedListNode[T, U]			)      ->      None:
							"""simple docstring"""





							while node.prev is not None and node.prev.freq > node.freq:
											# swap node with previous node
											snake_case__      :   str      =    node.prev

											snake_case__      :   Dict      =    previous_node.prev
											snake_case__      :   Union[str, Any]      =    node.prev
											snake_case__      :   Tuple      =    previous_node
											snake_case__      :   List[str]      =    node







			def 				lowerCAmelCase (     self		:						Any    ,			__UpperCamelCase		:						DoubleLinkedListNode[T, U]			)      ->      DoubleLinkedListNode[T, U] | None:
							"""simple docstring"""





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

							snake_case__      :   Any      =    node.next
							snake_case__      :   Any      =    node.prev
							snake_case__      :   Optional[int]      =    None
							snake_case__      :   Any      =    None
							return node






class 			_SCREAMING_SNAKE_CASE				(Generic[T, U]      ):
			A__								=  {}



			def __init__(     self		:						List[Any]    ,			__UpperCamelCase		:						int			)      ->      Optional[int]:
							"""simple docstring"""





							snake_case__      :   DoubleLinkedList[T, U]      =    DoubleLinkedList()
							snake_case__      :   Tuple      =    capacity
							snake_case__      :   str      =    0
							snake_case__      :   List[str]      =    0
							snake_case__      :   Optional[Any]      =    0
							snake_case__      :   dict[T, DoubleLinkedListNode[T, U]]      =    {}



			def __repr__(     self		:						Union[str, 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		:						Tuple    ,			__UpperCamelCase		:						T			)      ->      bool:
							"""simple docstring"""





							return key in self.cache



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						T			)      ->      U | None:
							"""simple docstring"""





							if key in self.cache:
											self.hits += 1
											snake_case__      :   DoubleLinkedListNode[T, U]      =    self.cache[key]
											snake_case__      :   Optional[int]      =    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(__UpperCamelCase			)
											return node.val
							self.miss += 1
							return None



			def 				lowerCAmelCase (     self		:						List[Any]    ,			__UpperCamelCase		:						T    ,			__UpperCamelCase		:						U			)      ->      None:
							"""simple docstring"""





							if key not in self.cache:
											if self.num_keys >= self.capacity:
															# delete first node when over capacity
															snake_case__      :   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(__UpperCamelCase			) is not None
															# first_node guaranteed to be in list

															del self.cache[first_node.key]
															self.num_keys -= 1
											snake_case__      :   List[Any]      =    DoubleLinkedListNode(__UpperCamelCase    ,			__UpperCamelCase			)
											self.list.add(self.cache[key]			)
											self.num_keys += 1

							else:
											snake_case__      :   Any      =    self.list.remove(self.cache[key]			)
											assert node is not None  # node guaranteed to be in list
											snake_case__      :   List[str]      =    value
											self.list.add(__UpperCamelCase			)







			@classmethod
			def 				lowerCAmelCase (     cls		:						type[LFUCache[T, U]]    ,			__UpperCamelCase		:						int = 128			)      ->      Callable[[Callable[[T], U]], Callable[..., U]]:
							"""simple docstring"""





							def cache_decorator_inner(__UpperCamelCase		:						Callable[[T], U]			) -> Callable[..., U]:
											def cache_decorator_wrapper(*__UpperCamelCase		:						T			) -> U:
															if func not in cls.decorator_function_to_instance_map:
																			snake_case__      :   int      =    LFUCache(__UpperCamelCase			)

															snake_case__      :   str      =    cls.decorator_function_to_instance_map[func].get(args[0]			)
															if result is None:
																			snake_case__      :   List[str]      =    func(*__UpperCamelCase			)
																			cls.decorator_function_to_instance_map[func].put(args[0]    ,			__UpperCamelCase			)
															return result

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

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

											return cache_decorator_wrapper

							return cache_decorator_inner


if __name__ == "__main__":
				import doctest

				doctest.testmod()











"""simple docstring"""
import inspect
import unittest

from transformers import ResNetConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
		from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor






class    _UpperCAmelCase  :


					def __init__(   self     :Optional[Any]	,			__UpperCamelCase     :Dict	,			__UpperCamelCase     :Optional[Any]=3	,			__UpperCamelCase     :Optional[int]=32	,			__UpperCamelCase     :str=3	,			__UpperCamelCase     :Union[str, Any]=10	,			__UpperCamelCase     :Union[str, Any]=[10, 20, 30, 40]	,			__UpperCamelCase     :int=[1, 1, 2, 1]	,			__UpperCamelCase     :Dict=True	,			__UpperCamelCase     :Optional[Any]=True	,			__UpperCamelCase     :Any="relu"	,			__UpperCamelCase     :Optional[Any]=3	,			__UpperCamelCase     :int=None	,			__UpperCamelCase     :str=["stage2", "stage3", "stage4"]	,			__UpperCamelCase     :str=[2, 3, 4]	,			):
												A						=    parent
												A						=    batch_size
												A						=    image_size
												A						=    num_channels
												A						=    embeddings_size
												A						=    hidden_sizes
												A						=    depths
												A						=    is_training
												A						=    use_labels
												A						=    hidden_act
												A						=    num_labels
												A						=    scope
												A						=    len(__UpperCamelCase						)
												A						=    out_features
												A						=    out_indices


					def       lowerCamelCase			(   self     :List[Any]						):
												A						=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

												A						=    None
												if self.use_labels:
																			A						=    ids_tensor([self.batch_size]	,			self.num_labels						)

												A						=    self.get_config()

												return config, pixel_values, labels


					def       lowerCamelCase			(   self     :Optional[Any]						):
												return ResNetConfig(
												    num_channels=self.num_channels	,			embeddings_size=self.embeddings_size	,			hidden_sizes=self.hidden_sizes	,			depths=self.depths	,			hidden_act=self.hidden_act	,			num_labels=self.num_labels	,			out_features=self.out_features	,			out_indices=self.out_indices	,			)


					def       lowerCamelCase			(   self     :int	,			__UpperCamelCase     :Optional[Any]	,			__UpperCamelCase     :str	,			__UpperCamelCase     :List[Any]						):
												A						=    ResNetModel(config=__UpperCamelCase						)
												model.to(__UpperCamelCase						)
												model.eval()
												A						=    model(__UpperCamelCase						)
												# expected last hidden states: B, C, H // 32, W // 32
												self.parent.assertEqual(
												    result.last_hidden_state.shape	,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)	,			)


					def       lowerCamelCase			(   self     :int	,			__UpperCamelCase     :Optional[Any]	,			__UpperCamelCase     :str	,			__UpperCamelCase     :str						):
												A						=    self.num_labels
												A						=    ResNetForImageClassification(__UpperCamelCase						)
												model.to(__UpperCamelCase						)
												model.eval()
												A						=    model(__UpperCamelCase	,			labels=__UpperCamelCase						)
												self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.num_labels)						)


					def       lowerCamelCase			(   self     :Dict	,			__UpperCamelCase     :Optional[Any]	,			__UpperCamelCase     :Optional[int]	,			__UpperCamelCase     :str						):
												A						=    ResNetBackbone(config=__UpperCamelCase						)
												model.to(__UpperCamelCase						)
												model.eval()
												A						=    model(__UpperCamelCase						)

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps						)	,			len(config.out_features						)						)
												self.parent.assertListEqual(list(result.feature_maps[0].shape						)	,			[self.batch_size, self.hidden_sizes[1], 4, 4]						)

												# verify channels
												self.parent.assertEqual(len(model.channels						)	,			len(config.out_features						)						)
												self.parent.assertListEqual(model.channels	,			config.hidden_sizes[1:]						)

												# verify backbone works with out_features=None
												A						=    None
												A						=    ResNetBackbone(config=__UpperCamelCase						)
												model.to(__UpperCamelCase						)
												model.eval()
												A						=    model(__UpperCamelCase						)

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps						)	,			1						)
												self.parent.assertListEqual(list(result.feature_maps[0].shape						)	,			[self.batch_size, self.hidden_sizes[-1], 1, 1]						)

												# verify channels
												self.parent.assertEqual(len(model.channels						)	,			1						)
												self.parent.assertListEqual(model.channels	,			[config.hidden_sizes[-1]]						)


					def       lowerCamelCase			(   self     :List[Any]						):
												A						=    self.prepare_config_and_inputs()
												A,      A,      A						=    config_and_inputs
												A						=    {"pixel_values": pixel_values}
												return config, inputs_dict







@require_torch
class    _UpperCAmelCase  (      lowercase_	,				lowercase_	,				unittest.TestCase					):
					UpperCamelCase						=				(
					    (
					        ResNetModel,
					        ResNetForImageClassification,
					        ResNetBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					UpperCamelCase						=				(
					    {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
					    if is_torch_available()
					    else {}
					)

					UpperCamelCase						=				True
					UpperCamelCase						=				False
					UpperCamelCase						=				False
					UpperCamelCase						=				False
					UpperCamelCase						=				False


					def       lowerCamelCase			(   self     :List[Any]						):
												A						=    ResNetModelTester(self						)
												A						=    ConfigTester(self	,			config_class=__UpperCamelCase	,			has_text_modality=__UpperCamelCase						)


					def       lowerCamelCase			(   self     :List[Any]						):
												self.create_and_test_config_common_properties()
												self.config_tester.create_and_test_config_to_json_string()
												self.config_tester.create_and_test_config_to_json_file()
												self.config_tester.create_and_test_config_from_and_save_pretrained()
												self.config_tester.create_and_test_config_with_num_labels()
												self.config_tester.check_config_can_be_init_without_params()
												self.config_tester.check_config_arguments_init()


					def       lowerCamelCase			(   self     :Union[str, Any]						):
												return


					@unittest.skip(reason="ResNet does not use inputs_embeds"						)
					def       lowerCamelCase			(   self     :Optional[int]						):
												pass


					@unittest.skip(reason="ResNet does not support input and output embeddings"						)
					def       lowerCamelCase			(   self     :Optional[Any]						):
												pass


					def       lowerCamelCase			(   self     :List[Any]						):
												A,      A						=    self.model_tester.prepare_config_and_inputs_for_common()

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

																			A						=    ["pixel_values"]
																			self.assertListEqual(arg_names[:1]	,			__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Tuple						):
												A						=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Union[str, Any]						):
												A						=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_backbone(*__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Optional[Any]						):
												A,      A						=    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A						=    model_class(config=__UpperCamelCase						)
																			for name, module in model.named_modules():
																										if isinstance(__UpperCamelCase	,			(nn.BatchNormad, nn.GroupNorm)						):
																																	self.assertTrue(
																																	    torch.all(module.weight == 1						)	,			msg=f"Parameter {name} of model {model_class} seems not properly initialized"	,			)
																																	self.assertTrue(
																																	    torch.all(module.bias == 0						)	,			msg=f"Parameter {name} of model {model_class} seems not properly initialized"	,			)


					def       lowerCamelCase			(   self     :Optional[Any]						):
												def check_hidden_states_output(__UpperCamelCase     :int	,			__UpperCamelCase     :Any	,			__UpperCamelCase     :Union[str, Any]						):
																			A						=    model_class(__UpperCamelCase						)
																			model.to(__UpperCamelCase						)
																			model.eval()

																			with torch.no_grad():
																										A						=    model(**self._prepare_for_class(__UpperCamelCase	,			__UpperCamelCase						)						)

																			A						=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																			A						=    self.model_tester.num_stages
																			self.assertEqual(len(__UpperCamelCase						)	,			expected_num_stages + 1						)

																			# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
																			self.assertListEqual(
																			    list(hidden_states[0].shape[-2:]						)	,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]	,			)

												A,      A						=    self.model_tester.prepare_config_and_inputs_for_common()
												A						=    ["basic", "bottleneck"]
												for model_class in self.all_model_classes:
																			for layer_type in layers_type:
																										A						=    layer_type
																										A						=    True
																										check_hidden_states_output(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase						)

																										# check that output_hidden_states also work using config
																										del inputs_dict["output_hidden_states"]
																										A						=    True

																										check_hidden_states_output(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase						)


					@unittest.skip(reason="ResNet does not use feedforward chunking"						)
					def       lowerCamelCase			(   self     :Tuple						):
												pass


					def       lowerCamelCase			(   self     :Optional[int]						):
												A						=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase						)


					@slow
					def       lowerCamelCase			(   self     :Union[str, Any]						):
												for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			A						=    ResNetModel.from_pretrained(__UpperCamelCase						)
																			self.assertIsNotNone(__UpperCamelCase						)







def    A__							(     ):
							A						=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
							return image






@require_torch
@require_vision
class    _UpperCAmelCase  (      unittest.TestCase					):


					@cached_property
					def       lowerCamelCase			(   self     :List[Any]						):
												return (
												    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
												    if is_vision_available()
												    else None
												)


					@slow
					def       lowerCamelCase			(   self     :Dict						):
												A						=    ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(__UpperCamelCase						)

												A						=    self.default_image_processor
												A						=    prepare_img()
												A						=    image_processor(images=__UpperCamelCase	,			return_tensors="pt"						).to(__UpperCamelCase						)

												# forward pass
												with torch.no_grad():
																			A						=    model(**__UpperCamelCase						)

												# verify the logits
												A						=    torch.Size((1, 10_00)						)
												self.assertEqual(outputs.logits.shape	,			__UpperCamelCase						)

												A						=    torch.tensor([-11.1_069, -9.7_877, -8.3_777]						).to(__UpperCamelCase						)

												self.assertTrue(torch.allclose(outputs.logits[0, :3]	,			__UpperCamelCase	,			atol=1e-4						)						)







@require_torch
class    _UpperCAmelCase  (      lowercase_	,				unittest.TestCase					):
					UpperCamelCase						=				(ResNetBackbone,) if is_torch_available() else ()
					UpperCamelCase						=				False
					UpperCamelCase						=				ResNetConfig


					def       lowerCamelCase			(   self     :Dict						):
												A						=    ResNetModelTester(self						)


