

from __future__ import annotations

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

__UpperCAmelCase								=			TypeVar("""T""")
__UpperCAmelCase								=			TypeVar("""U""")



class SCREAMING_SNAKE_CASE     (  Generic[T, U]			):
  """simple docstring"""






  def __init__(      self			:   Any	,							lowerCAmelCase			:   T | None	,							lowerCAmelCase			:   U | None   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						key
     __lowerCAmelCase  :      Optional[int]    =						val
     __lowerCAmelCase  :      DoubleLinkedListNode[T, U] | None    =						None
     __lowerCAmelCase  :      DoubleLinkedListNode[T, U] | None    =						None







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






     """simple docstring"""







     return (
         f'''Node: key: {self.key}, val: {self.val}, '''
         f'''has next: {bool(self.next   )}, has prev: {bool(self.prev   )}'''
     )






class SCREAMING_SNAKE_CASE     (  Generic[T, U]			):
  """simple docstring"""






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






     """simple docstring"""







     __lowerCAmelCase  :      DoubleLinkedListNode[T, U]    =						DoubleLinkedListNode(lowerCAmelCase	,							lowerCAmelCase   )
     __lowerCAmelCase  :      DoubleLinkedListNode[T, U]    =						DoubleLinkedListNode(lowerCAmelCase	,							lowerCAmelCase   )
     __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[Any]    =						self.rear, self.head







  def __repr__(      self			:   int   )							->     str:






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   str	,							lowerCAmelCase			:   DoubleLinkedListNode[T, U]   )							->     None:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						self.rear.prev

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

     __lowerCAmelCase  :      List[str]    =						node
     __lowerCAmelCase  :      List[str]    =						previous
     __lowerCAmelCase  :      Tuple    =						node
     __lowerCAmelCase  :      Union[str, Any]    =						self.rear







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   DoubleLinkedListNode[T, U]   )							->     DoubleLinkedListNode[T, U] | None:






     """simple docstring"""







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

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






class SCREAMING_SNAKE_CASE     (  Generic[T, U]			):
  """simple docstring"""






  lowerCamelCase   :      dict[Callable[[T], U], LRUCache[T, U]]										={}







  def __init__(      self			:   Optional[Any]	,							lowerCAmelCase			:   int   )							->     Optional[int]:






     """simple docstring"""







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







  def __repr__(      self			:   Tuple   )							->     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	,							lowerCAmelCase			:   T   )							->     bool:






     """simple docstring"""







     return key in self.cache







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   T   )							->     U | None:






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   T	,							lowerCAmelCase			:   U   )							->     None:






     """simple docstring"""







     if key not in self.cache:
        if self.num_keys >= self.capacity:
           # delete first node (oldest) when over capacity
           __lowerCAmelCase  :      Optional[int]    =						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(lowerCAmelCase   ) 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  :      Any    =						DoubleLinkedListNode(lowerCAmelCase	,							lowerCAmelCase   )
        self.list.add(self.cache[key]   )
        self.num_keys += 1

     else:
        # bump node to the end of the list, update value
        __lowerCAmelCase  :      Tuple    =						self.list.remove(self.cache[key]   )
        assert node is not None  # node guaranteed to be in list
        __lowerCAmelCase  :      Dict    =						value
        self.list.add(lowerCAmelCase   )







  @classmethod
  def 	SCREAMING_SNAKE_CASE   (      cls			:   Dict	,							lowerCAmelCase			:   int = 1_28   )							->     Callable[[Callable[[T], U]], Callable[..., U]]:






     """simple docstring"""







     def cache_decorator_inner(lowerCAmelCase			:   Callable[[T], U]   ) -> Callable[..., U]:
        def cache_decorator_wrapper(*lowerCAmelCase			:   T   ) -> U:
           if func not in cls.decorator_function_to_instance_map:
              __lowerCAmelCase  :      Optional[int]    =						LRUCache(lowerCAmelCase   )

           __lowerCAmelCase  :      int    =						cls.decorator_function_to_instance_map[func].get(args[0]   )
           if result is None:
              __lowerCAmelCase  :      Optional[int]    =						func(*lowerCAmelCase   )
              cls.decorator_function_to_instance_map[func].put(args[0]	,							lowerCAmelCase   )
           return result

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

        setattr(lowerCAmelCase	,							"""cache_info"""	,							lowerCAmelCase   )  # noqa: B010

        return cache_decorator_wrapper

     return cache_decorator_inner


if __name__ == "__main__":
   import doctest

   doctest.testmod()





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 MobileViTImageProcessor



class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def __init__(      self			:   List[Any]	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Optional[Any]=7	,							lowerCAmelCase			:   List[str]=3	,							lowerCAmelCase			:   int=18	,							lowerCAmelCase			:   int=30	,							lowerCAmelCase			:   Optional[int]=4_00	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   Dict=None	,							lowerCAmelCase			:   List[str]=True	,							lowerCAmelCase			:   Tuple=None	,							lowerCAmelCase			:   Any=True	,							)							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						size if size is not None else {"""shortest_edge""": 20}
     __lowerCAmelCase  :      Any    =						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
     __lowerCAmelCase  :      str    =						parent
     __lowerCAmelCase  :      List[str]    =						batch_size
     __lowerCAmelCase  :      int    =						num_channels
     __lowerCAmelCase  :      List[str]    =						image_size
     __lowerCAmelCase  :      Optional[int]    =						min_resolution
     __lowerCAmelCase  :      List[str]    =						max_resolution
     __lowerCAmelCase  :      List[Any]    =						do_resize
     __lowerCAmelCase  :      Optional[int]    =						size
     __lowerCAmelCase  :      List[Any]    =						do_center_crop
     __lowerCAmelCase  :      Optional[Any]    =						crop_size
     __lowerCAmelCase  :      int    =						do_flip_channel_order







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Union[str, Any]:






     """simple docstring"""







     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "do_flip_channel_order": self.do_flip_channel_order,
     }






@require_torch
@require_vision
class SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      List[str]										=MobileViTImageProcessor if is_vision_available() else None







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						MobileViTImageProcessingTester(self   )







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Dict:






     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						self.image_processing_class(**self.image_processor_dict   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_resize"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""size"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_center_crop"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""center_crop"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_flip_channel_order"""   )   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.image_processing_class.from_dict(self.image_processor_dict   )
     self.assertEqual(image_processor.size	,							{"""shortest_edge""": 20}   )
     self.assertEqual(image_processor.crop_size	,							{"""height""": 18, """width""": 18}   )

     __lowerCAmelCase  :      Optional[Any]    =						self.image_processing_class.from_dict(self.image_processor_dict	,							size=42	,							crop_size=84   )
     self.assertEqual(image_processor.size	,							{"""shortest_edge""": 42}   )
     self.assertEqual(image_processor.crop_size	,							{"""height""": 84, """width""": 84}   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     int:






     """simple docstring"""







     pass







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.image_processing_class(**self.image_processor_dict   )
     # create random PIL images
     __lowerCAmelCase  :      Union[str, Any]    =						prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowerCAmelCase   )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase	,							Image.Image   )

     # Test not batched input
     __lowerCAmelCase  :      List[Any]    =						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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)

     # Test batched
     __lowerCAmelCase  :      str    =						image_processing(lowerCAmelCase	,							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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)







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






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.image_processing_class(**self.image_processor_dict   )
     # create random numpy tensors
     __lowerCAmelCase  :      Any    =						prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowerCAmelCase	,							numpify=lowerCAmelCase   )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase	,							np.ndarray   )

     # Test not batched input
     __lowerCAmelCase  :      Optional[Any]    =						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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)

     # Test batched
     __lowerCAmelCase  :      Tuple    =						image_processing(lowerCAmelCase	,							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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						self.image_processing_class(**self.image_processor_dict   )
     # create random PyTorch tensors
     __lowerCAmelCase  :      str    =						prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowerCAmelCase	,							torchify=lowerCAmelCase   )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase	,							torch.Tensor   )

     # Test not batched input
     __lowerCAmelCase  :      Optional[int]    =						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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)

     # Test batched
     __lowerCAmelCase  :      Tuple    =						image_processing(lowerCAmelCase	,							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.crop_size["""height"""],
             self.image_processor_tester.crop_size["""width"""],
         )	,							)


