




"""simple docstring"""
from __future__ import annotations

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

_SCREAMING_SNAKE_CASE						 =     TypeVar("""T""")
_SCREAMING_SNAKE_CASE						 =     TypeVar("""U""")







class  _SCREAMING_SNAKE_CASE       (     Generic[T, U]				):



 '''simple docstring'''





 def __init__(self					,     lowerCAmelCase__					,     lowerCAmelCase__   ):

  '''simple docstring'''







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






 def __repr__(self   ):

  '''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]				):



 '''simple docstring'''





 def __init__(self   ):

  '''simple docstring'''







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






 def __repr__(self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Tuple					      =  ["DoubleLinkedList"]
  _UpperCamelCase      :	Tuple					      =  self.head
  while node.next is not None:
   rep.append(str(lowerCAmelCase__   )   )
   _UpperCamelCase      :	List[str]					      =  node.next
  rep.append(str(self.rear   )   )
  return ",\n    ".join(lowerCAmelCase__   )






 def lowercase_      (self					,     lowerCAmelCase__   ):

  '''simple docstring'''







  _UpperCamelCase      :	Optional[Any]					      =  self.rear.prev

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

  _UpperCamelCase      :	Optional[int]					      =  node
  _UpperCamelCase      :	Tuple					      =  previous
  _UpperCamelCase      :	Union[str, Any]					      =  node
  _UpperCamelCase      :	Optional[Any]					      =  self.rear
  node.freq += 1
  self._position_node(lowerCAmelCase__   )






 def lowercase_      (self					,     lowerCAmelCase__   ):

  '''simple docstring'''







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

   _UpperCamelCase      :	List[Any]					      =  previous_node.prev
   _UpperCamelCase      :	int					      =  node.prev
   _UpperCamelCase      :	str					      =  previous_node
   _UpperCamelCase      :	List[str]					      =  node






 def lowercase_      (self					,     lowerCAmelCase__   ):

  '''simple docstring'''







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

  _UpperCamelCase      :	int					      =  node.next
  _UpperCamelCase      :	int					      =  node.prev
  _UpperCamelCase      :	str					      =  None
  _UpperCamelCase      :	Dict					      =  None
  return node







class  _SCREAMING_SNAKE_CASE       (     Generic[T, U]				):



 '''simple docstring'''





 __UpperCAmelCase		   =							{}






 def __init__(self					,     lowerCAmelCase__   ):

  '''simple docstring'''







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






 def __repr__(self   ):

  '''simple docstring'''







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






 def __contains__(self					,     lowerCAmelCase__   ):

  '''simple docstring'''







  return key in self.cache






 def lowercase_      (self					,     lowerCAmelCase__   ):

  '''simple docstring'''







  if key in self.cache:
   self.hits += 1
   _UpperCamelCase      :	DoubleLinkedListNode[T, U]					      =  self.cache[key]
   _UpperCamelCase      :	Tuple					      =  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 lowercase_      (self					,     lowerCAmelCase__					,     lowerCAmelCase__   ):

  '''simple docstring'''







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

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

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






 @classmethod
 def lowercase_      (cls					,     lowerCAmelCase__ = 1_28   ):

  '''simple docstring'''







  def cache_decorator_inner(lowerCAmelCase__   ) -> Callable[..., U]:
   def cache_decorator_wrapper(*lowerCAmelCase__   ) -> U:
    if func not in cls.decorator_function_to_instance_map:
     _UpperCamelCase      :	str					      =  LFUCache(lowerCAmelCase__   )

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

   def cache_info() -> LFUCache[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()









"""simple docstring"""
import json
import os
import tempfile

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


if is_torch_available():
    import numpy as np
    import torch

if is_vision_available():
    from PIL import Image






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Any  ,	__lowerCAmelCase			:		Dict=False  ,	__lowerCAmelCase			:		Optional[Any]=False  ,	__lowerCAmelCase			:		int=False						)    ->       List[str]:
 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 _UpperCamelCase      :	Optional[int]					      =  []
 for i in range(image_processor_tester.batch_size						):
  if equal_resolution:
   _UpperCamelCase      :	List[str]					      =  image_processor_tester.max_resolution
  else:
   # To avoid getting image width/height 0
   _UpperCamelCase      :	int					      =  image_processor_tester.min_resolution
   if getattr(__lowerCAmelCase  ,	"size_divisor"  ,	__lowerCAmelCase						):
    # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
    _UpperCamelCase      :	Optional[int]					      =  max(image_processor_tester.size_divisor  ,	__lowerCAmelCase						)
   _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  np.random.choice(np.arange(__lowerCAmelCase  ,	image_processor_tester.max_resolution						)  ,	2						)
  image_inputs.append(
      np.random.randint(255  ,	size=(image_processor_tester.num_channels, width, height)  ,	dtype=np.uinta						)						)

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  _UpperCamelCase      :	Optional[Any]					      =  [Image.fromarray(np.moveaxis(__lowerCAmelCase  ,	0  ,	-1						)						) for image in image_inputs]

 if torchify:
  _UpperCamelCase      :	str					      =  [torch.from_numpy(__lowerCAmelCase						) for image in image_inputs]

 return image_inputs






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Optional[Any]  ,	__lowerCAmelCase			:		Tuple=10  ,	__lowerCAmelCase			:		str=10  ,	__lowerCAmelCase			:		Any=False  ,	__lowerCAmelCase			:		List[Any]=False						)    ->       Optional[int]:
 _UpperCamelCase      :	Optional[int]					      =  []
 for i in range(image_processor_tester.num_frames						):
  video.append(np.random.randint(255  ,	size=(image_processor_tester.num_channels, width, height)  ,	dtype=np.uinta						)						)

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  _UpperCamelCase      :	Optional[int]					      =  [Image.fromarray(np.moveaxis(__lowerCAmelCase  ,	0  ,	-1						)						) for frame in video]

 if torchify:
  _UpperCamelCase      :	int					      =  [torch.from_numpy(__lowerCAmelCase						) for frame in video]

 return video






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		List[Any]  ,	__lowerCAmelCase			:		Union[str, Any]=False  ,	__lowerCAmelCase			:		Any=False  ,	__lowerCAmelCase			:		Dict=False						)    ->       Any:
 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 _UpperCamelCase      :	Optional[Any]					      =  []
 for i in range(image_processor_tester.batch_size						):
  if equal_resolution:
   _UpperCamelCase      :	List[Any]					      =  image_processor_tester.max_resolution
  else:
   _UpperCamelCase      ,	_UpperCamelCase      :	Optional[int]					      =  np.random.choice(
       np.arange(image_processor_tester.min_resolution  ,	image_processor_tester.max_resolution						)  ,	2						)
   _UpperCamelCase      :	List[Any]					      =  prepare_video(
       image_processor_tester=__lowerCAmelCase  ,	width=__lowerCAmelCase  ,	height=__lowerCAmelCase  ,	numpify=__lowerCAmelCase  ,	torchify=__lowerCAmelCase  ,	)
  video_inputs.append(__lowerCAmelCase						)

 return video_inputs







class  _SCREAMING_SNAKE_CASE       :



 '''simple docstring'''





 __UpperCAmelCase		   =							None






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	str					      =  self.image_processing_class(**self.image_processor_dict   )
  _UpperCamelCase      :	Optional[int]					      =  json.loads(image_processor.to_json_string()   )
  for key, value in self.image_processor_dict.items():
   self.assertEqual(obj[key]					,     lowerCAmelCase__   )






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Union[str, Any]					      =  self.image_processing_class(**self.image_processor_dict   )

  with tempfile.TemporaryDirectory() as tmpdirname:
   _UpperCamelCase      :	Tuple					      =  os.path.join(lowerCAmelCase__					,     "image_processor.json"   )
   image_processor_first.to_json_file(lowerCAmelCase__   )
   _UpperCamelCase      :	Tuple					      =  self.image_processing_class.from_json_file(lowerCAmelCase__   )

  self.assertEqual(image_processor_second.to_dict()					,     image_processor_first.to_dict()   )






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Dict					      =  self.image_processing_class(**self.image_processor_dict   )

  with tempfile.TemporaryDirectory() as tmpdirname:
   _UpperCamelCase      :	Optional[int]					      =  image_processor_first.save_pretrained(lowerCAmelCase__   )[0]
   check_json_file_has_correct_format(lowerCAmelCase__   )
   _UpperCamelCase      :	int					      =  self.image_processing_class.from_pretrained(lowerCAmelCase__   )

  self.assertEqual(image_processor_second.to_dict()					,     image_processor_first.to_dict()   )






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Tuple					      =  self.image_processing_class()
  self.assertIsNotNone(lowerCAmelCase__   )






 @require_torch
 @require_vision
 def lowercase_      (self   ):

  '''simple docstring'''







  if self.test_cast_dtype is not None:
   # Initialize image_processor
   _UpperCamelCase      :	Optional[int]					      =  self.image_processing_class(**self.image_processor_dict   )

   # create random PyTorch tensors
   _UpperCamelCase      :	str					      =  prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase__					,     torchify=lowerCAmelCase__   )

   _UpperCamelCase      :	List[Any]					      =  image_processor(lowerCAmelCase__					,     return_tensors="pt"   )
   # for layoutLM compatiblity
   self.assertEqual(encoding.pixel_values.device					,     torch.device("cpu"   )   )
   self.assertEqual(encoding.pixel_values.dtype					,     torch.floataa   )

   _UpperCamelCase      :	List[Any]					      =  image_processor(lowerCAmelCase__					,     return_tensors="pt"   ).to(torch.floataa   )
   self.assertEqual(encoding.pixel_values.device					,     torch.device("cpu"   )   )
   self.assertEqual(encoding.pixel_values.dtype					,     torch.floataa   )

   _UpperCamelCase      :	str					      =  image_processor(lowerCAmelCase__					,     return_tensors="pt"   ).to("cpu"					,     torch.bfloataa   )
   self.assertEqual(encoding.pixel_values.device					,     torch.device("cpu"   )   )
   self.assertEqual(encoding.pixel_values.dtype					,     torch.bfloataa   )

   with self.assertRaises(lowerCAmelCase__   ):
    _UpperCamelCase      :	Optional[int]					      =  image_processor(lowerCAmelCase__					,     return_tensors="pt"   ).to(torch.bfloataa					,     "cpu"   )

   # Try with text + image feature
   _UpperCamelCase      :	Union[str, Any]					      =  image_processor(lowerCAmelCase__					,     return_tensors="pt"   )
   encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]]   )}   )
   _UpperCamelCase      :	Optional[int]					      =  encoding.to(torch.floataa   )

   self.assertEqual(encoding.pixel_values.device					,     torch.device("cpu"   )   )
   self.assertEqual(encoding.pixel_values.dtype					,     torch.floataa   )
   self.assertEqual(encoding.input_ids.dtype					,     torch.long   )



