





'''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


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ChineseCLIPImageProcessor







class 			lowerCamelCase					(						unittest.TestCase  ):


     def __init__(					self	:  Any       ,     __snake_case	:  Tuple       ,     __snake_case	:  Optional[int]=7       ,     __snake_case	:  Tuple=3       ,     __snake_case	:  Dict=18       ,     __snake_case	:  List[Any]=30       ,     __snake_case	:  List[str]=4_00       ,     __snake_case	:  int=True       ,     __snake_case	:  str=None       ,     __snake_case	:  int=True       ,     __snake_case	:  Any=None       ,     __snake_case	:  Optional[int]=True       ,     __snake_case	:  Optional[Any]=[0.48_145_466, 0.4_578_275, 0.40_821_073]       ,     __snake_case	:  int=[0.26_862_954, 0.26_130_258, 0.27_577_711]       ,     __snake_case	:  List[Any]=True       ,     ):





           '''simple docstring'''





           _snake_case:   str  					=		size if size is not None else {'height': 2_24, 'width': 2_24}
           _snake_case:   str  					=		crop_size if crop_size is not None else {'height': 18, 'width': 18}
           _snake_case:   Dict  					=		parent
           _snake_case:   List[str]  					=		batch_size
           _snake_case:   Optional[int]  					=		num_channels
           _snake_case:   Tuple  					=		image_size
           _snake_case:   Optional[Any]  					=		min_resolution
           _snake_case:   str  					=		max_resolution
           _snake_case:   Dict  					=		do_resize
           _snake_case:   str  					=		size
           _snake_case:   int  					=		do_center_crop
           _snake_case:   int  					=		crop_size
           _snake_case:   str  					=		do_normalize
           _snake_case:   str  					=		image_mean
           _snake_case:   Optional[Any]  					=		image_std
           _snake_case:   Optional[int]  					=		do_convert_rgb


     def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





           '''simple docstring'''





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




     def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     __snake_case	:  Tuple=False       ,     __snake_case	:  List[Any]=False       ,     __snake_case	:  List[Any]=False       ):





           '''simple docstring'''





           assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

           if equal_resolution:
                 _snake_case:   str  					=		[]
                 for i in range(self.batch_size       ):
                       image_inputs.append(
                           np.random.randint(
                               2_55       ,     size=(self.num_channels, self.max_resolution, self.max_resolution)       ,     dtype=np.uinta       )       )
           else:
                 _snake_case:   Optional[Any]  					=		[]
                 for i in range(self.batch_size       ):
                       _snake_case:   Union[str, Any]  					=		np.random.choice(np.arange(self.min_resolution       ,     self.max_resolution       )       ,     2       )
                       image_inputs.append(np.random.randint(2_55       ,     size=(self.num_channels, width, height)       ,     dtype=np.uinta       )       )

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

           if torchify:
                 _snake_case:   List[Any]  					=		[torch.from_numpy(__snake_case       ) for x in image_inputs]

           return image_inputs







@require_torch
@require_vision
class 			lowerCamelCase					(						__UpperCAmelCase     ,							unittest.TestCase  ):
     _SCREAMING_SNAKE_CASE							   =    ChineseCLIPImageProcessor if is_vision_available() else None


     def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





           '''simple docstring'''





           _snake_case:   Optional[int]  					=		ChineseCLIPImageProcessingTester(self       ,     do_center_crop=__snake_case       )


     @property
     def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





           '''simple docstring'''





           return self.image_processor_tester.prepare_image_processor_dict()


     def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





           '''simple docstring'''





           _snake_case:   List[Any]  					=		self.image_processing_class(**self.image_processor_dict       )
           self.assertTrue(hasattr(__snake_case       ,     'do_resize'       )       )
           self.assertTrue(hasattr(__snake_case       ,     'size'       )       )
           self.assertTrue(hasattr(__snake_case       ,     'do_center_crop'       )       )
           self.assertTrue(hasattr(__snake_case       ,     'center_crop'       )       )
           self.assertTrue(hasattr(__snake_case       ,     'do_normalize'       )       )
           self.assertTrue(hasattr(__snake_case       ,     'image_mean'       )       )
           self.assertTrue(hasattr(__snake_case       ,     'image_std'       )       )
           self.assertTrue(hasattr(__snake_case       ,     'do_convert_rgb'       )       )


     def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





           '''simple docstring'''





           _snake_case:   int  					=		self.image_processing_class.from_dict(self.image_processor_dict       )
           self.assertEqual(image_processor.size       ,     {'height': 2_24, 'width': 2_24}       )
           self.assertEqual(image_processor.crop_size       ,     {'height': 18, 'width': 18}       )

           _snake_case:   Union[str, 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	:  Any       ):





           '''simple docstring'''





           pass


     def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





           '''simple docstring'''





           _snake_case:   Tuple  					=		self.image_processing_class(**self.image_processor_dict       )
           # create random PIL images
           _snake_case:   Any  					=		self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case       )
           for image in image_inputs:
                 self.assertIsInstance(__snake_case       ,     Image.Image       )

           # Test not batched input
           _snake_case:   str  					=		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
           _snake_case:   Union[str, Any]  					=		image_processing(__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.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )       ,     )


     def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





           '''simple docstring'''





           _snake_case:   Any  					=		self.image_processing_class(**self.image_processor_dict       )
           # create random numpy tensors
           _snake_case:   Optional[int]  					=		self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case       ,     numpify=__snake_case       )
           for image in image_inputs:
                 self.assertIsInstance(__snake_case       ,     np.ndarray       )

           # Test not batched input
           _snake_case:   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
           _snake_case:   Union[str, Any]  					=		image_processing(__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.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )       ,     )




     def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





           '''simple docstring'''





           _snake_case:   Optional[Any]  					=		self.image_processing_class(**self.image_processor_dict       )
           # create random PyTorch tensors
           _snake_case:   Union[str, Any]  					=		self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case       ,     torchify=__snake_case       )
           for image in image_inputs:
                 self.assertIsInstance(__snake_case       ,     torch.Tensor       )

           # Test not batched input
           _snake_case:   Dict  					=		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
           _snake_case:   str  					=		image_processing(__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.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )       ,     )







@require_torch
@require_vision
class 			lowerCamelCase					(						__UpperCAmelCase     ,							unittest.TestCase  ):
     _SCREAMING_SNAKE_CASE							   =    ChineseCLIPImageProcessor if is_vision_available() else None


     def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





           '''simple docstring'''





           _snake_case:   str  					=		ChineseCLIPImageProcessingTester(self       ,     num_channels=4       ,     do_center_crop=__snake_case       )
           _snake_case:   Optional[int]  					=		3


     @property
     def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





           '''simple docstring'''





           return self.image_processor_tester.prepare_image_processor_dict()


     def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





           '''simple docstring'''





           _snake_case:   List[Any]  					=		self.image_processing_class(**self.image_processor_dict       )
           self.assertTrue(hasattr(__snake_case       ,     'do_resize'       )       )
           self.assertTrue(hasattr(__snake_case       ,     'size'       )       )
           self.assertTrue(hasattr(__snake_case       ,     'do_center_crop'       )       )
           self.assertTrue(hasattr(__snake_case       ,     'center_crop'       )       )
           self.assertTrue(hasattr(__snake_case       ,     'do_normalize'       )       )
           self.assertTrue(hasattr(__snake_case       ,     'image_mean'       )       )
           self.assertTrue(hasattr(__snake_case       ,     'image_std'       )       )
           self.assertTrue(hasattr(__snake_case       ,     'do_convert_rgb'       )       )


     def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





           '''simple docstring'''





           pass




     def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





           '''simple docstring'''





           _snake_case:   List[str]  					=		self.image_processing_class(**self.image_processor_dict       )
           # create random PIL images
           _snake_case:   str  					=		self.image_processor_tester.prepare_inputs(equal_resolution=__snake_case       )
           for image in image_inputs:
                 self.assertIsInstance(__snake_case       ,     Image.Image       )

           # Test not batched input
           _snake_case:   Union[str, Any]  					=		image_processing(image_inputs[0]       ,     return_tensors='pt'       ).pixel_values
           self.assertEqual(
               encoded_images.shape       ,     (
                   1,
                   self.expected_encoded_image_num_channels,
                   self.image_processor_tester.crop_size['height'],
                   self.image_processor_tester.crop_size['width'],
               )       ,     )

           # Test batched
           _snake_case:   Union[str, Any]  					=		image_processing(__snake_case       ,     return_tensors='pt'       ).pixel_values
           self.assertEqual(
               encoded_images.shape       ,     (
                   self.image_processor_tester.batch_size,
                   self.expected_encoded_image_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

A   : str             =			TypeVar('T')







class 			lowerCamelCase					(						Generic[T]  ):
					_SCREAMING_SNAKE_CASE							   =    42  # Cache store of keys
					_SCREAMING_SNAKE_CASE							   =    42  # References of the keys in cache
					_SCREAMING_SNAKE_CASE							   =    10  # Maximum capacity of cache


					def __init__(					self	:  List[Any]       ,     __snake_case	:  int       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		deque()
											_snake_case:   Union[str, Any]  					=		set()
											if not n:
																	_snake_case:   Optional[int]  					=		sys.maxsize
											elif n < 0:
																	raise ValueError('n should be an integer greater than 0.'       )
											else:
																	_snake_case:   Tuple  					=		n


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ,     __snake_case	:  T       ):





											'''simple docstring'''





											if x not in self.key_reference:
																	if len(self.dq_store       ) == LRUCache._MAX_CAPACITY:
																							_snake_case:   int  					=		self.dq_store.pop()
																							self.key_reference.remove(__snake_case       )
											else:
																	self.dq_store.remove(__snake_case       )

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											for k in self.dq_store:
																	print(__snake_case       )




					def __repr__(					self	:  List[Any]       ):





											'''simple docstring'''





											return f'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store       )}'''


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		A   : 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]"





