



'''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 SwinaSRImageProcessor
				from transformers.image_transforms import get_image_size






class 			_SCREAMING_SNAKE_CASE				(unittest.TestCase      ):



			def __init__(     self		:						Union[str, Any]    ,			__UpperCamelCase		:						int    ,			__UpperCamelCase		:						Tuple=7    ,			__UpperCamelCase		:						Dict=3    ,			__UpperCamelCase		:						Union[str, Any]=18    ,			__UpperCamelCase		:						str=30    ,			__UpperCamelCase		:						Union[str, Any]=400    ,			__UpperCamelCase		:						Tuple=True    ,			__UpperCamelCase		:						Optional[int]=1 / 255    ,			__UpperCamelCase		:						int=True    ,			__UpperCamelCase		:						Dict=8    ,			)      ->      Dict:
							"""simple docstring"""





							snake_case__      :   Optional[Any]      =    parent
							snake_case__      :   str      =    batch_size
							snake_case__      :   List[str]      =    num_channels
							snake_case__      :   Optional[int]      =    image_size
							snake_case__      :   Dict      =    min_resolution
							snake_case__      :   int      =    max_resolution
							snake_case__      :   Tuple      =    do_rescale
							snake_case__      :   str      =    rescale_factor
							snake_case__      :   List[str]      =    do_pad
							snake_case__      :   str      =    pad_size



			def 				lowerCAmelCase (     self		:						Dict			)      ->      Optional[int]:
							"""simple docstring"""





							return {
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							    "pad_size": self.pad_size,
							}







			def 				lowerCAmelCase (     self		:						str    ,			__UpperCamelCase		:						Optional[int]=False    ,			__UpperCamelCase		:						int=False    ,			__UpperCamelCase		:						Union[str, Any]=False			)      ->      Optional[int]:
							"""simple docstring"""





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

							if equal_resolution:
											snake_case__      :   int      =    []
											for i in range(self.batch_size			):
															image_inputs.append(
															    np.random.randint(
															        255    ,			size=(self.num_channels, self.max_resolution, self.max_resolution)    ,			dtype=np.uinta			)			)
							else:
											snake_case__      :   Dict      =    []
											for i in range(self.batch_size			):
															snake_case__  ,      snake_case__      :   Optional[Any]      =    np.random.choice(np.arange(self.min_resolution    ,			self.max_resolution			)    ,			2			)
															image_inputs.append(np.random.randint(255    ,			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__      :   Tuple      =    [Image.fromarray(np.moveaxis(__UpperCamelCase    ,			0    ,			-1			)			) for x in image_inputs]

							if torchify:
											snake_case__      :   str      =    [torch.from_numpy(__UpperCamelCase			) for x in image_inputs]

							return image_inputs






@require_torch
@require_vision
class 			_SCREAMING_SNAKE_CASE				(lowercase__,	unittest.TestCase      ):
			A__								=  SwinaSRImageProcessor if is_vision_available() else None



			def 				lowerCAmelCase (     self		:						Union[str, Any]			)      ->      Optional[Any]:
							"""simple docstring"""





							snake_case__      :   List[str]      =    SwinaSRImageProcessingTester(self			)



			@property
			def 				lowerCAmelCase (     self		:						Dict			)      ->      str:
							"""simple docstring"""





							return self.image_processor_tester.prepare_image_processor_dict()



			def 				lowerCAmelCase (     self		:						Tuple			)      ->      str:
							"""simple docstring"""





							snake_case__      :   List[Any]      =    self.image_processing_class(**self.image_processor_dict			)
							self.assertTrue(hasattr(__UpperCamelCase    ,			'''do_rescale'''			)			)
							self.assertTrue(hasattr(__UpperCamelCase    ,			'''rescale_factor'''			)			)
							self.assertTrue(hasattr(__UpperCamelCase    ,			'''do_pad'''			)			)
							self.assertTrue(hasattr(__UpperCamelCase    ,			'''pad_size'''			)			)



			def 				lowerCAmelCase (     self		:						Union[str, Any]			)      ->      List[str]:
							"""simple docstring"""





							pass



			def 				lowerCAmelCase (     self		:						List[Any]    ,			__UpperCamelCase		:						Optional[int]			)      ->      Any:
							"""simple docstring"""





							snake_case__  ,      snake_case__      :   Any      =    get_image_size(__UpperCamelCase			)
							snake_case__      :   int      =    self.image_processor_tester.pad_size

							snake_case__      :   Any      =    (old_height // size + 1) * size - old_height
							snake_case__      :   List[str]      =    (old_width // size + 1) * size - old_width
							return old_height + pad_height, old_width + pad_width



			def 				lowerCAmelCase (     self		:						List[Any]			)      ->      int:
							"""simple docstring"""





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

							# Test not batched input
							snake_case__      :   Union[str, Any]      =    image_processor(image_inputs[0]    ,			return_tensors='''pt'''			).pixel_values
							snake_case__  ,      snake_case__      :   Tuple      =    self.calculate_expected_size(np.array(image_inputs[0]			)			)
							self.assertEqual(
							    encoded_images.shape    ,			(
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )    ,			)



			def 				lowerCAmelCase (     self		:						List[Any]			)      ->      int:
							"""simple docstring"""





							snake_case__      :   Tuple      =    self.image_processing_class(**self.image_processor_dict			)
							# create random numpy tensors
							snake_case__      :   List[str]      =    self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase    ,			numpify=__UpperCamelCase			)
							for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase    ,			np.ndarray			)

							# Test not batched input
							snake_case__      :   List[Any]      =    image_processor(image_inputs[0]    ,			return_tensors='''pt'''			).pixel_values
							snake_case__  ,      snake_case__      :   Dict      =    self.calculate_expected_size(image_inputs[0]			)
							self.assertEqual(
							    encoded_images.shape    ,			(
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )    ,			)







			def 				lowerCAmelCase (     self		:						str			)      ->      int:
							"""simple docstring"""





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

							# Test not batched input
							snake_case__      :   Optional[Any]      =    image_processor(image_inputs[0]    ,			return_tensors='''pt'''			).pixel_values
							snake_case__  ,      snake_case__      :   Tuple      =    self.calculate_expected_size(image_inputs[0]			)
							self.assertEqual(
							    encoded_images.shape    ,			(
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )    ,			)








'''simple docstring'''







from __future__ import annotations

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

a__    :   Dict								=			TypeVar('T')
a__    :   Optional[Any]								=			TypeVar('U')
class    lowercase_	(       Generic[T, U]  ):


    def __init__(	self    ,   a    ,   a       ):
          UpperCamelCase__      =							key
          UpperCamelCase__      =							val
          UpperCamelCase__      =							0
          UpperCamelCase__      =							None
          UpperCamelCase__      =							None


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


    def __init__(	self       ):
          UpperCamelCase__      =							DoubleLinkedListNode(a    ,   a       )
          UpperCamelCase__      =							DoubleLinkedListNode(a    ,   a       )
          UpperCamelCase__   ,      UpperCamelCase__      =							self.rear, self.head


    def __repr__(	self       ):
          UpperCamelCase__      =							["DoubleLinkedList"]
          UpperCamelCase__      =							self.head
          while node.next is not None:
                rep.append(str(a       )       )
                UpperCamelCase__      =							node.next
          rep.append(str(self.rear       )       )
          return ",\n    ".join(a       )


    def   __a				(	self    ,   a       ):
          UpperCamelCase__      =							self.rear.prev

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

          UpperCamelCase__      =							node
          UpperCamelCase__      =							previous
          UpperCamelCase__      =							node
          UpperCamelCase__      =							self.rear
          node.freq += 1
          self._position_node(a       )


    def   __a				(	self    ,   a       ):

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

                UpperCamelCase__      =							previous_node.prev
                UpperCamelCase__      =							node.prev
                UpperCamelCase__      =							previous_node
                UpperCamelCase__      =							node


    def   __a				(	self    ,   a       ):

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

          UpperCamelCase__      =							node.next
          UpperCamelCase__      =							node.prev
          UpperCamelCase__      =							None
          UpperCamelCase__      =							None
          return node

class    lowercase_	(       Generic[T, U]  ):
    __UpperCAmelCase       =      {}


    def __init__(	self    ,   a       ):
          UpperCamelCase__      =							DoubleLinkedList()
          UpperCamelCase__      =							capacity
          UpperCamelCase__      =							0
          UpperCamelCase__      =							0
          UpperCamelCase__      =							0
          UpperCamelCase__      =							{}


    def __repr__(	self       ):
          return (
              f'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
              f'''capacity={self.capacity}, current_size={self.num_keys})'''
          )


    def __contains__(	self    ,   a       ):
          return key in self.cache


    def   __a				(	self    ,   a       ):

          if key in self.cache:
                self.hits += 1
                UpperCamelCase__      =							self.cache[key]
                UpperCamelCase__      =							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(a       )
                return node.val
          self.miss += 1
          return None


    def   __a				(	self    ,   a    ,   a       ):

          if key not in self.cache:
                if self.num_keys >= self.capacity:
                      # delete first node when over capacity
                      UpperCamelCase__      =							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(a       ) is not None
                      # first_node guaranteed to be in list

                      del self.cache[first_node.key]
                      self.num_keys -= 1
                UpperCamelCase__      =							DoubleLinkedListNode(a    ,   a       )
                self.list.add(self.cache[key]       )
                self.num_keys += 1

          else:
                UpperCamelCase__      =							self.list.remove(self.cache[key]       )
                assert node is not None  # node guaranteed to be in list
                UpperCamelCase__      =							value
                self.list.add(a       )


    @classmethod
    def   __a				(	cls    ,   a = 1_28       ):

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

                      UpperCamelCase__      =							cls.decorator_function_to_instance_map[func].get(args[0]       )
                      if result is None:
                            UpperCamelCase__      =							func(*a       )
                            cls.decorator_function_to_instance_map[func].put(args[0]    ,   a       )
                      return result

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

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

                return cache_decorator_wrapper

          return cache_decorator_inner


if __name__ == "__main__":
  import doctest

  doctest.testmod()





