




"""simple docstring"""



from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

SCREAMING_SNAKE_CASE							=			TypeVar("T", bound=bool)




class       UpperCAmelCase_    (		Generic[T]				):




    def __init__(       self		:			str      ,					snake_case_		:			T ) ->				None:




     '''simple docstring'''

     A__			=							value
     A__			=							None
     A__			=							None




    @property
    def 				__magic_name__				(       self		:			int ) ->				T:




     '''simple docstring'''

     return self._value




    @staticmethod
    def 				__magic_name__				(       snake_case_		:			SkewNode[T] | None      ,					snake_case_		:			SkewNode[T] | None ) ->				SkewNode[T] | None:




     '''simple docstring'''

     if not roota:
      return roota

     if not roota:
      return roota

     if roota.value > roota.value:
      A__, A__			=							roota, roota

     A__			=							roota
     A__			=							roota.right
     A__			=							roota.left
     A__			=							SkewNode.merge(snake_case_      ,					snake_case_ )

     return result






class       UpperCAmelCase_    (		Generic[T]				):




    def __init__(       self		:			Dict      ,					snake_case_		:			Iterable[T] | None = () ) ->				None:




     '''simple docstring'''

     A__			=							None
     if data:
      for item in data:
       self.insert(snake_case_ )




    def __bool__(       self		:			int ) ->				bool:




     '''simple docstring'''

     return self._root is not None




    def __iter__(       self		:			List[Any] ) ->				Iterator[T]:




     '''simple docstring'''

     A__			=							[]
     while self:
      result.append(self.pop() )

     # Pushing items back to the heap not to clear it.
     for item in result:
      self.insert(snake_case_ )

     return iter(snake_case_ )




    def 				__magic_name__				(       self		:			int      ,					snake_case_		:			T ) ->				None:




     '''simple docstring'''

     A__			=							SkewNode.merge(self._root      ,					SkewNode(snake_case_ ) )




    def 				__magic_name__				(       self		:			int ) ->				T | None:




     '''simple docstring'''

     A__			=							self.top()
     A__			=							(
         SkewNode.merge(self._root.left      ,					self._root.right ) if self._root else None
     )

     return result




    def 				__magic_name__				(       self		:			List[str] ) ->				T:




     '''simple docstring'''

     if not self._root:
      raise IndexError("Can't get top element for the empty heap." )
     return self._root.value




    def 				__magic_name__				(       self		:			Any ) ->				None:




     '''simple docstring'''

     A__			=							None


if __name__ == "__main__":
      import doctest

      doctest.testmod()











"""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 ViTImageProcessor




class       UpperCAmelCase_    (		unittest.TestCase				):




    def __init__(       self		:			Union[str, Any]      ,					snake_case_		:			Tuple      ,					snake_case_		:			Dict=7      ,					snake_case_		:			List[Any]=3      ,					snake_case_		:			Optional[int]=18      ,					snake_case_		:			Tuple=30      ,					snake_case_		:			Dict=400      ,					snake_case_		:			List[str]=True      ,					snake_case_		:			Optional[int]=None      ,					snake_case_		:			List[str]=True      ,					snake_case_		:			Optional[Any]=[0.5, 0.5, 0.5]      ,					snake_case_		:			Optional[int]=[0.5, 0.5, 0.5]      ,					) ->				List[Any]:




     '''simple docstring'''

     A__			=							size if size is not None else {"height": 18, "width": 18}
     A__			=							parent
     A__			=							batch_size
     A__			=							num_channels
     A__			=							image_size
     A__			=							min_resolution
     A__			=							max_resolution
     A__			=							do_resize
     A__			=							size
     A__			=							do_normalize
     A__			=							image_mean
     A__			=							image_std




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




     '''simple docstring'''

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






@require_torch
@require_vision
class       UpperCAmelCase_    (		A_,  unittest.TestCase				):
    lowercase__		   = ViTImageProcessor if is_vision_available() else None




    def 				__magic_name__				(       self		:			int ) ->				Any:




     '''simple docstring'''

     A__			=							ViTImageProcessingTester(self )




    @property
    def 				__magic_name__				(       self		:			Optional[Any] ) ->				str:




     '''simple docstring'''

     return self.image_processor_tester.prepare_image_processor_dict()




    def 				__magic_name__				(       self		:			Tuple ) ->				str:




     '''simple docstring'''

     A__			=							self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(snake_case_      ,					"image_mean" ) )
     self.assertTrue(hasattr(snake_case_      ,					"image_std" ) )
     self.assertTrue(hasattr(snake_case_      ,					"do_normalize" ) )
     self.assertTrue(hasattr(snake_case_      ,					"do_resize" ) )
     self.assertTrue(hasattr(snake_case_      ,					"size" ) )




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




     '''simple docstring'''

     A__			=							self.image_processing_class.from_dict(self.image_processor_dict )
     self.assertEqual(image_processor.size      ,					{"height": 18, "width": 18} )

     A__			=							self.image_processing_class.from_dict(self.image_processor_dict      ,					size=42 )
     self.assertEqual(image_processor.size      ,					{"height": 42, "width": 42} )




    def 				__magic_name__				(       self		:			Any ) ->				Optional[Any]:




     '''simple docstring'''

     pass




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




     '''simple docstring'''

     A__			=							self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     A__			=							prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=snake_case_ )
     for image in image_inputs:
      self.assertIsInstance(snake_case_      ,					Image.Image )

     # Test not batched input
     A__			=							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.size["height"],
             self.image_processor_tester.size["width"],
         )      ,					)

     # Test batched
     A__			=							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.size["height"],
             self.image_processor_tester.size["width"],
         )      ,					)




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




     '''simple docstring'''

     A__			=							self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     A__			=							prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=snake_case_      ,					numpify=snake_case_ )
     for image in image_inputs:
      self.assertIsInstance(snake_case_      ,					np.ndarray )

     # Test not batched input
     A__			=							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.size["height"],
             self.image_processor_tester.size["width"],
         )      ,					)

     # Test batched
     A__			=							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.size["height"],
             self.image_processor_tester.size["width"],
         )      ,					)




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




     '''simple docstring'''

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

     # Test not batched input
     A__			=							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.size["height"],
             self.image_processor_tester.size["width"],
         )      ,					)

     # Test batched
     A__			=							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.size["height"],
             self.image_processor_tester.size["width"],
         )      ,					)





