





'''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 LevitImageProcessor







class 		UpperCAmelCase   (				unittest.TestCase):


  """simple docstring"""







  def __init__(				self    :					Tuple							,	UpperCamelCase__    :					List[Any]							,	UpperCamelCase__    :					Optional[Any]=7							,	UpperCamelCase__    :					Optional[int]=3							,	UpperCamelCase__    :					int=18							,	UpperCamelCase__    :					Dict=30							,	UpperCamelCase__    :					List[str]=400							,	UpperCamelCase__    :					int=True							,	UpperCamelCase__    :					List[Any]=None							,	UpperCamelCase__    :					int=True							,	UpperCamelCase__    :					Optional[int]=None							,	UpperCamelCase__    :					int=True							,	UpperCamelCase__    :					str=[0.5, 0.5, 0.5]							,	UpperCamelCase__    :					Union[str, Any]=[0.5, 0.5, 0.5]							,	)     ->						Union[str, Any]:
    _UpperCamelCase       			=size if size is not None else {'''shortest_edge''': 18}
    _UpperCamelCase       			=crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
    _UpperCamelCase       			=parent
    _UpperCamelCase       			=batch_size
    _UpperCamelCase       			=num_channels
    _UpperCamelCase       			=image_size
    _UpperCamelCase       			=min_resolution
    _UpperCamelCase       			=max_resolution
    _UpperCamelCase       			=do_resize
    _UpperCamelCase       			=size
    _UpperCamelCase       			=do_center_crop
    _UpperCamelCase       			=crop_size
    _UpperCamelCase       			=do_normalize
    _UpperCamelCase       			=image_mean
    _UpperCamelCase       			=image_std




  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						str:
    return {
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_normalize": self.do_normalize,
        "do_resize": self.do_resize,
        "do_center_crop": self.do_center_crop,
        "size": self.size,
        "crop_size": self.crop_size,
    }







@require_torch
@require_vision
class 		UpperCAmelCase   (				snake_case__   ,     unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_					=				LevitImageProcessor if is_vision_available() else None


  def   UpperCamelCase__      (				self    :					List[str]  )     ->						Optional[int]:
    _UpperCamelCase       			=LevitImageProcessingTester(self  )


  @property
  def   UpperCamelCase__      (				self    :					str  )     ->						Union[str, Any]:
    return self.image_processor_tester.prepare_image_processor_dict()


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						List[Any]:
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    self.assertTrue(hasattr(UpperCAmelCase_							,	'''image_mean'''  )  )
    self.assertTrue(hasattr(UpperCAmelCase_							,	'''image_std'''  )  )
    self.assertTrue(hasattr(UpperCAmelCase_							,	'''do_normalize'''  )  )
    self.assertTrue(hasattr(UpperCAmelCase_							,	'''do_resize'''  )  )
    self.assertTrue(hasattr(UpperCAmelCase_							,	'''do_center_crop'''  )  )
    self.assertTrue(hasattr(UpperCAmelCase_							,	'''size'''  )  )


  def   UpperCamelCase__      (				self    :					int  )     ->						Any:
    _UpperCamelCase       			=self.image_processing_class.from_dict(self.image_processor_dict  )
    self.assertEqual(image_processor.size							,	{'''shortest_edge''': 18}  )
    self.assertEqual(image_processor.crop_size							,	{'''height''': 18, '''width''': 18}  )

    _UpperCamelCase       			=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   UpperCamelCase__      (				self    :					str  )     ->						int:
    pass


  def   UpperCamelCase__      (				self    :					Dict  )     ->						Tuple:
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random PIL images
    _UpperCamelCase       			=prepare_image_inputs(self.image_processor_tester							,	equal_resolution=UpperCAmelCase_  )
    for image in image_inputs:
      self.assertIsInstance(UpperCAmelCase_							,	Image.Image  )

    # Test not batched input
    _UpperCamelCase       			=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
    _UpperCamelCase       			=image_processing(UpperCAmelCase_							,	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   UpperCamelCase__      (				self    :					Dict  )     ->						Optional[int]:
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random numpy tensors
    _UpperCamelCase       			=prepare_image_inputs(self.image_processor_tester							,	equal_resolution=UpperCAmelCase_							,	numpify=UpperCAmelCase_  )
    for image in image_inputs:
      self.assertIsInstance(UpperCAmelCase_							,	np.ndarray  )

    # Test not batched input
    _UpperCamelCase       			=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
    _UpperCamelCase       			=image_processing(UpperCAmelCase_							,	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   UpperCamelCase__      (				self    :					Tuple  )     ->						str:
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random PyTorch tensors
    _UpperCamelCase       			=prepare_image_inputs(self.image_processor_tester							,	equal_resolution=UpperCAmelCase_							,	torchify=UpperCAmelCase_  )
    for image in image_inputs:
      self.assertIsInstance(UpperCAmelCase_							,	torch.Tensor  )

    # Test not batched input
    _UpperCamelCase       			=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
    _UpperCamelCase       			=image_processing(UpperCAmelCase_							,	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'''],
        )							,	)













'''simple docstring'''






from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

__lowerCamelCase						: Optional[int]		=   TypeVar('T')







class 		UpperCAmelCase   (				Generic[T]):


  """simple docstring"""







  lowerCAmelCase_					=				42  # Cache store of keys
  lowerCAmelCase_					=				42  # References of the keys in cache
  lowerCAmelCase_					=				10  # Maximum capacity of cache


  def __init__(				self    :					Union[str, Any]							,	UpperCamelCase__    :					int  )     ->						None:
    _UpperCamelCase       			=deque()
    _UpperCamelCase       			=set()
    if not n:
      _UpperCamelCase       			=sys.maxsize
    elif n < 0:
      raise ValueError('''n should be an integer greater than 0.'''  )
    else:
      _UpperCamelCase       			=n


  def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					T  )     ->						None:
    if x not in self.key_reference:
      if len(self.dq_store  ) == LRUCache._MAX_CAPACITY:
        _UpperCamelCase       			=self.dq_store.pop()
        self.key_reference.remove(UpperCamelCase__  )
    else:
      self.dq_store.remove(UpperCamelCase__  )

    self.dq_store.appendleft(UpperCamelCase__  )
    self.key_reference.add(UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						None:
    for k in self.dq_store:
      print(UpperCamelCase__  )




  def __repr__(				self    :					str  )     ->						str:
    return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store  )}'''


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      __lowerCamelCase						: LRUCache[str | int]		=   LRUCache(4)
      lru_cache.refer('A')
      lru_cache.refer(2)
      lru_cache.refer(3)
      lru_cache.refer('A')
      lru_cache.refer(4)
      lru_cache.refer(5)
      lru_cache.display()

      print(lru_cache)
      assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"






