




'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 EfficientNetImageProcessor






class 			SCREAMING_SNAKE_CASE(							unittest.TestCase	):

       def __init__(       self   ,						lowerCamelCase__   ,						lowerCamelCase__=13   ,						lowerCamelCase__=3   ,						lowerCamelCase__=18   ,						lowerCamelCase__=30   ,						lowerCamelCase__=400   ,						lowerCamelCase__=True   ,						lowerCamelCase__=None   ,						lowerCamelCase__=True   ,						lowerCamelCase__=[0.5, 0.5, 0.5]   ,						lowerCamelCase__=[0.5, 0.5, 0.5]   ,						)       ->   List[str]:

           """simple docstring"""



           __lowercase     							=		size if size is not None else {'''height''': 18, '''width''': 18}
           __lowercase     							=		parent
           __lowercase     							=		batch_size
           __lowercase     							=		num_channels
           __lowercase     							=		image_size
           __lowercase     							=		min_resolution
           __lowercase     							=		max_resolution
           __lowercase     							=		do_resize
           __lowercase     							=		size
           __lowercase     							=		do_normalize
           __lowercase     							=		image_mean
           __lowercase     							=		image_std




       def        snake_case__     (       self	)       ->   Optional[Any]:

           """simple docstring"""



           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
           }






@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE(							UpperCAmelCase_  , unittest.TestCase	):
       snake_case_					:	Union[str, Any]       =   EfficientNetImageProcessor if is_vision_available() else None

       def        snake_case__     (       self	)       ->   Dict:

           """simple docstring"""



           __lowercase     							=		EfficientNetImageProcessorTester(self	)

       @property
       def        snake_case__     (       self	)       ->   Optional[Any]:

           """simple docstring"""



           return self.image_processor_tester.prepare_image_processor_dict()

       def        snake_case__     (       self	)       ->   Any:

           """simple docstring"""



           __lowercase     							=		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        snake_case__     (       self	)       ->   Union[str, Any]:

           """simple docstring"""



           __lowercase     							=		self.image_processing_class.from_dict(self.image_processor_dict	)
           self.assertEqual(image_processor.size   ,						{"""height""": 18, """width""": 18}	)

           __lowercase     							=		self.image_processing_class.from_dict(self.image_processor_dict   ,						size=42	)
           self.assertEqual(image_processor.size   ,						{"""height""": 42, """width""": 42}	)

       def        snake_case__     (       self	)       ->   List[Any]:

           """simple docstring"""



           __lowercase     							=		self.image_processing_class(**self.image_processor_dict	)
           # create random PIL images
           __lowercase     							=		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
           __lowercase     							=		image_processing(image_inputs[0]   ,						return_tensors="""pt"""	).pixel_values
           self.assertEqual(
               encoded_images.shape   ,						(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )   ,						)

           # Test batched
           __lowercase     							=		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,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )   ,						)

       def        snake_case__     (       self	)       ->   Optional[int]:

           """simple docstring"""



           __lowercase     							=		self.image_processing_class(**self.image_processor_dict	)
           # create random numpy tensors
           __lowercase     							=		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
           __lowercase     							=		image_processing(image_inputs[0]   ,						return_tensors="""pt"""	).pixel_values
           self.assertEqual(
               encoded_images.shape   ,						(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )   ,						)

           # Test batched
           __lowercase     							=		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,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )   ,						)




       def        snake_case__     (       self	)       ->   str:

           """simple docstring"""



           __lowercase     							=		self.image_processing_class(**self.image_processor_dict	)
           # create random PyTorch tensors
           __lowercase     							=		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
           __lowercase     							=		image_processing(image_inputs[0]   ,						return_tensors="""pt"""	).pixel_values
           self.assertEqual(
               encoded_images.shape   ,						(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )   ,						)

           # Test batched
           __lowercase     							=		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,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )   ,						)










'''simple docstring'''


from __future__ import annotations

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

UpperCamelCase			     =					TypeVar('''T''')
UpperCamelCase			     =					TypeVar('''U''')






class 						lowerCAmelCase_	(       Generic[T, U]							):







  '''simple docstring'''

  def __init__( self				:							Tuple					,  SCREAMING_SNAKE_CASE_				:							T | None					,  SCREAMING_SNAKE_CASE_				:							U | None	)			->		Any:







       '''simple docstring'''



       A:		Union[str, Any]          =		key
       A:		Union[str, Any]          =		val
       A:		int          =		0
       A:		DoubleLinkedListNode[T, U] | None          =		None
       A:		DoubleLinkedListNode[T, U] | None          =		None




  def __repr__( self				:							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 						lowerCAmelCase_	(       Generic[T, U]							):







  '''simple docstring'''

  def __init__( self				:							int	)			->		None:







       '''simple docstring'''



       A:		DoubleLinkedListNode[T, U]          =		DoubleLinkedListNode(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       A:		DoubleLinkedListNode[T, U]          =		DoubleLinkedListNode(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       A     ,    A:		Any          =		self.rear, self.head



  def __repr__( self				:							Optional[int]	)			->		str:







       '''simple docstring'''



       A:		int          =		['''DoubleLinkedList''']
       A:		Dict          =		self.head
       while node.next is not None:
            rep.append(str(SCREAMING_SNAKE_CASE_	)	)
            A:		int          =		node.next
       rep.append(str(self.rear	)	)
       return ",\n    ".join(SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							Any					,  SCREAMING_SNAKE_CASE_				:							DoubleLinkedListNode[T, U]	)			->		None:







       '''simple docstring'''



       A:		Union[str, Any]          =		self.rear.prev

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

       A:		Optional[int]          =		node
       A:		int          =		previous
       A:		Dict          =		node
       A:		int          =		self.rear
       node.freq += 1
       self._position_node(SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							int					,  SCREAMING_SNAKE_CASE_				:							DoubleLinkedListNode[T, U]	)			->		None:







       '''simple docstring'''



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

            A:		Optional[Any]          =		previous_node.prev
            A:		int          =		node.prev
            A:		int          =		previous_node
            A:		Optional[int]          =		node




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







       '''simple docstring'''



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

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






class 						lowerCAmelCase_	(       Generic[T, U]							):







  '''simple docstring'''

  UpperCamelCase_			:      dict[Callable[[T], U], LFUCache[T, U]]										=				{}



  def __init__( self				:							List[str]					,  SCREAMING_SNAKE_CASE_				:							int	)			->		int:







       '''simple docstring'''



       A:		DoubleLinkedList[T, U]          =		DoubleLinkedList()
       A:		Any          =		capacity
       A:		List[Any]          =		0
       A:		Dict          =		0
       A:		Dict          =		0
       A:		dict[T, DoubleLinkedListNode[T, U]]          =		{}



  def __repr__( self				:							Optional[int]	)			->		str:







       '''simple docstring'''



       return (
           f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
           f"""capacity={self.capacity}, current_size={self.num_keys})"""
       )



  def __contains__( self				:							str					,  SCREAMING_SNAKE_CASE_				:							T	)			->		bool:







       '''simple docstring'''



       return key in self.cache



  def 					_snake_case     ( self				:							Dict					,  SCREAMING_SNAKE_CASE_				:							T	)			->		U | None:







       '''simple docstring'''



       if key in self.cache:
            self.hits += 1
            A:		DoubleLinkedListNode[T, U]          =		self.cache[key]
            A:		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 					_snake_case     ( self				:							List[str]					,  SCREAMING_SNAKE_CASE_				:							T					,  SCREAMING_SNAKE_CASE_				:							U	)			->		None:







       '''simple docstring'''



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

                 del self.cache[first_node.key]
                 self.num_keys -= 1
            A:		Tuple          =		DoubleLinkedListNode(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
            self.list.add(self.cache[key]	)
            self.num_keys += 1

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




  @classmethod
  def 					_snake_case     ( cls				:							type[LFUCache[T, U]]					,  SCREAMING_SNAKE_CASE_				:							int = 1_28	)			->		Callable[[Callable[[T], U]], Callable[..., U]]:







       '''simple docstring'''



       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:
                      A:		Tuple          =		LFUCache(SCREAMING_SNAKE_CASE_	)

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

            def cache_info() -> LFUCache[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()






