






'''simple docstring'''







from __future__ import annotations

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

__A      :       List[Any]      =	TypeVar('T')
__A      :       Tuple      =	TypeVar('U')






class 					a__    (			Generic[T, U]						):
     '''simple docstring'''







     def __init__(self    :	int				,				lowercase__    :	T | None				,				lowercase__    :	U | None					)  ->       str:
           __A		   =      key
           __A		   =      val
           __A		   =      0
           __A		   =      None
           __A		   =      None






     def __repr__(self    :	Optional[Any]					)  ->       str:
           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 					a__    (			Generic[T, U]						):
     '''simple docstring'''







     def __init__(self    :	Union[str, Any]					)  ->       None:
           __A		   =      DoubleLinkedListNode(lowercase__				,				lowercase__					)
           __A		   =      DoubleLinkedListNode(lowercase__				,				lowercase__					)
           __A   ,				__A		   =      self.rear, self.head






     def __repr__(self    :	str					)  ->       str:
           __A		   =      ["DoubleLinkedList"]
           __A		   =      self.head
           while node.next is not None:
                 rep.append(str(lowercase__					)					)
                 __A		   =      node.next
           rep.append(str(self.rear					)					)
           return ",\n    ".join(lowercase__					)






     def     _UpperCamelCase   (self    :	List[str]				,				lowercase__    :	DoubleLinkedListNode[T, U]					)  ->       None:
           __A		   =      self.rear.prev

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

           __A		   =      node
           __A		   =      previous
           __A		   =      node
           __A		   =      self.rear
           node.freq += 1
           self._position_node(lowercase__					)






     def     _UpperCamelCase   (self    :	Optional[int]				,				lowercase__    :	DoubleLinkedListNode[T, U]					)  ->       None:

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

                 __A		   =      previous_node.prev
                 __A		   =      node.prev
                 __A		   =      previous_node
                 __A		   =      node






     def     _UpperCamelCase   (self    :	Dict				,				lowercase__    :	DoubleLinkedListNode[T, U]					)  ->       DoubleLinkedListNode[T, U] | None:

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

           __A		   =      node.next
           __A		   =      node.prev
           __A		   =      None
           __A		   =      None
           return node







class 					a__    (			Generic[T, U]						):
     '''simple docstring'''







     _snake_case             =  {}






     def __init__(self    :	Optional[int]				,				lowercase__    :	int					)  ->       Optional[Any]:
           __A		   =      DoubleLinkedList()
           __A		   =      capacity
           __A		   =      0
           __A		   =      0
           __A		   =      0
           __A		   =      {}






     def __repr__(self    :	int					)  ->       str:
           return (
               f"CacheInfo(hits={self.hits}, misses={self.miss}, "
               f"capacity={self.capacity}, current_size={self.num_keys})"
           )






     def __contains__(self    :	List[Any]				,				lowercase__    :	T					)  ->       bool:
           return key in self.cache






     def     _UpperCamelCase   (self    :	Tuple				,				lowercase__    :	T					)  ->       U | None:

           if key in self.cache:
                 self.hits += 1
                 __A		   =      self.cache[key]
                 __A		   =      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     _UpperCamelCase   (self    :	int				,				lowercase__    :	T				,				lowercase__    :	U					)  ->       None:

           if key not in self.cache:
                 if self.num_keys >= self.capacity:
                       # delete first node when over capacity
                       __A		   =      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
                       # first_node guaranteed to be in list

                       del self.cache[first_node.key]
                       self.num_keys -= 1
                 __A		   =      DoubleLinkedListNode(lowercase__				,				lowercase__					)
                 self.list.add(self.cache[key]					)
                 self.num_keys += 1

           else:
                 __A		   =      self.list.remove(self.cache[key]					)
                 assert node is not None  # node guaranteed to be in list
                 __A		   =      value
                 self.list.add(lowercase__					)






     @classmethod
     def     _UpperCamelCase   (cls    :	type[LFUCache[T, U]]				,				lowercase__    :	int = 128					)  ->       Callable[[Callable[[T], U]], Callable[..., U]]:

           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:
                             __A		   =      LFUCache(lowercase__					)

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

                 def cache_info() -> LFUCache[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()










'''simple docstring'''







import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType






@require_torch
class 					a__    :
     '''simple docstring'''







     _snake_case             =  ()
     _snake_case             =  True






     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       Union[str, Any]:
           __A		   =      self.config_class

           # test default config
           __A		   =      config_class()
           self.assertIsNotNone(lowercase__					)
           __A		   =      ["stem"] + [f"stage{idx}" for idx in range(1				,				len(config.depths					) + 1					)]
           self.assertEqual(config.stage_names				,				lowercase__					)
           self.assertTrue(set(config.out_features					).issubset(set(config.stage_names					)					)					)

           # Test out_features and out_indices are correctly set
           # out_features and out_indices both None
           __A		   =      config_class(out_features=lowercase__				,				out_indices=lowercase__					)
           self.assertEqual(config.out_features				,				[config.stage_names[-1]]					)
           self.assertEqual(config.out_indices				,				[len(config.stage_names					) - 1]					)

           # out_features and out_indices both set
           __A		   =      config_class(out_features=["stem", "stage1"]				,				out_indices=[0, 1]					)
           self.assertEqual(config.out_features				,				["stem", "stage1"]					)
           self.assertEqual(config.out_indices				,				[0, 1]					)

           # Only out_features set
           __A		   =      config_class(out_features=["stage1", "stage3"]					)
           self.assertEqual(config.out_features				,				["stage1", "stage3"]					)
           self.assertEqual(config.out_indices				,				[1, 3]					)

           # Only out_indices set
           __A		   =      config_class(out_indices=[0, 2]					)
           self.assertEqual(config.out_features				,				[config.stage_names[0], config.stage_names[2]]					)
           self.assertEqual(config.out_indices				,				[0, 2]					)

           # Error raised when out_indices do not correspond to out_features
           with self.assertRaises(lowercase__					):
                 __A		   =      config_class(out_features=["stage1", "stage2"]				,				out_indices=[0, 2]					)






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Optional[Any]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 __A		   =      model_class(lowercase__					)
                 __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]				,				lowercase__					)






     def     _UpperCamelCase   (self    :	Dict					)  ->       Union[str, Any]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 __A		   =      model_class(lowercase__					)
                 self.assertEqual(len(model.channels					)				,				len(config.out_features					)					)
                 __A		   =      model.num_features
                 __A		   =      [config.stage_names.index(lowercase__					) for feat in config.out_features]
                 __A		   =      [num_features[idx] for idx in out_indices]
                 self.assertListEqual(model.channels				,				lowercase__					)

                 __A		   =      copy.deepcopy(lowercase__					)
                 __A		   =      None
                 __A		   =      model_class(lowercase__					)
                 self.assertEqual(len(model.channels					)				,				1					)
                 self.assertListEqual(model.channels				,				[num_features[-1]]					)

                 __A		   =      copy.deepcopy(lowercase__					)
                 __A		   =      None
                 __A		   =      model_class(lowercase__					)
                 self.assertEqual(len(model.channels					)				,				1					)
                 self.assertListEqual(model.channels				,				[num_features[-1]]					)






     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       Optional[Any]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 __A		   =      model_class(lowercase__					)
                 model.to(lowercase__					)
                 model.eval()
                 __A		   =      model(**lowercase__					)

                 self.assertEqual(len(result.feature_maps					)				,				len(config.out_features					)					)
                 self.assertEqual(len(model.channels					)				,				len(config.out_features					)					)
                 self.assertEqual(len(result.feature_maps					)				,				len(config.out_indices					)					)
                 self.assertEqual(len(model.channels					)				,				len(config.out_indices					)					)

                 # Check output of last stage is taken if out_features=None, out_indices=None
                 __A		   =      copy.deepcopy(lowercase__					)
                 __A		   =      None
                 __A		   =      model_class(lowercase__					)
                 model.to(lowercase__					)
                 model.eval()
                 __A		   =      model(**lowercase__					)

                 self.assertEqual(len(result.feature_maps					)				,				1					)
                 self.assertEqual(len(model.channels					)				,				1					)

                 __A		   =      copy.deepcopy(lowercase__					)
                 __A		   =      None
                 __A		   =      model_class(lowercase__					)
                 model.to(lowercase__					)
                 model.eval()
                 __A		   =      model(**lowercase__					)

                 self.assertEqual(len(result.feature_maps					)				,				1					)
                 self.assertEqual(len(model.channels					)				,				1					)

                 # Check backbone can be initialized with fresh weights
                 __A		   =      copy.deepcopy(lowercase__					)
                 __A		   =      False
                 __A		   =      model_class(lowercase__					)
                 model.to(lowercase__					)
                 model.eval()
                 __A		   =      model(**lowercase__					)






     def     _UpperCamelCase   (self    :	Dict					)  ->       Optional[int]:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()

           for backbone_class in self.all_model_classes:
                 __A		   =      backbone_class(lowercase__					)

                 self.assertTrue(hasattr(lowercase__				,				"backbone_type"					)					)
                 self.assertTrue(hasattr(lowercase__				,				"stage_names"					)					)
                 self.assertTrue(hasattr(lowercase__				,				"num_features"					)					)
                 self.assertTrue(hasattr(lowercase__				,				"out_indices"					)					)
                 self.assertTrue(hasattr(lowercase__				,				"out_features"					)					)
                 self.assertTrue(hasattr(lowercase__				,				"out_feature_channels"					)					)
                 self.assertTrue(hasattr(lowercase__				,				"channels"					)					)

                 self.assertIsInstance(backbone.backbone_type				,				lowercase__					)
                 # Verify num_features has been initialized in the backbone init
                 self.assertIsNotNone(backbone.num_features					)
                 self.assertTrue(len(backbone.channels					) == len(backbone.out_indices					)					)
                 self.assertTrue(len(backbone.stage_names					) == len(backbone.num_features					)					)
                 self.assertTrue(len(backbone.channels					) <= len(backbone.num_features					)					)
                 self.assertTrue(len(backbone.out_feature_channels					) == len(backbone.stage_names					)					)






     def     _UpperCamelCase   (self    :	Tuple					)  ->       int:
           __A   ,				__A		   =      self.model_tester.prepare_config_and_inputs_for_common()
           __A		   =      inputs_dict["pixel_values"].shape[0]

           for backbone_class in self.all_model_classes:
                 __A		   =      backbone_class(lowercase__					)
                 backbone.to(lowercase__					)
                 backbone.eval()

                 __A		   =      backbone(**lowercase__					)

                 # Test default outputs and verify feature maps
                 self.assertIsInstance(outputs.feature_maps				,				lowercase__					)
                 self.assertTrue(len(outputs.feature_maps					) == len(backbone.channels					)					)
                 for feature_map, n_channels in zip(outputs.feature_maps				,				backbone.channels					):
                       self.assertTrue(feature_map.shape[:2]				,				(batch_size, n_channels)					)
                 self.assertIsNone(outputs.hidden_states					)
                 self.assertIsNone(outputs.attentions					)

                 # Test output_hidden_states=True
                 __A		   =      backbone(**lowercase__				,				output_hidden_states=lowercase__					)
                 self.assertIsNotNone(outputs.hidden_states					)
                 self.assertTrue(len(outputs.hidden_states					)				,				len(backbone.stage_names					)					)
                 for hidden_state, n_channels in zip(outputs.hidden_states				,				backbone.channels					):
                       self.assertTrue(hidden_state.shape[:2]				,				(batch_size, n_channels)					)

                 # Test output_attentions=True
                 if self.has_attentions:
                       __A		   =      backbone(**lowercase__				,				output_attentions=lowercase__					)
                       self.assertIsNotNone(outputs.attentions					)


