
from __future__ import annotations

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

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







class 	lowerCamelCase						(    Generic[T]    ):





     def __init__(self :       int							,       _A :       T  )		->		None:
         snake_case						=   value
         snake_case						=   None
         snake_case						=   None





     @property
     def 						UpperCAmelCase(self :       Optional[int]  )		->		T:
         return self._value





     @staticmethod
     def 						UpperCAmelCase(_A :       RandomizedHeapNode[T] | None							,       _A :       RandomizedHeapNode[T] | None  )		->		RandomizedHeapNode[T] | None:







         if not roota:
             return roota

         if not roota:
             return roota

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

         if random.choice([True, False]  ):
             snake_case	,				snake_case						=   roota.right, roota.left

         snake_case						=   RandomizedHeapNode.merge(roota.left							,       _A  )

         return roota







class 	lowerCamelCase						(    Generic[T]    ):





     def __init__(self :       str							,       _A :       Iterable[T] | None = ()  )		->		None:
         snake_case						=   None

         if data:
             for item in data:
                 self.insert(_A  )





     def 						UpperCAmelCase(self :       Tuple							,       _A :       T  )		->		None:
         snake_case						=   RandomizedHeapNode.merge(self._root							,       RandomizedHeapNode(_A  )  )





     def 						UpperCAmelCase(self :       Any  )		->		T | None:
         snake_case						=   self.top()

         if self._root is None:
             return None

         snake_case						=   RandomizedHeapNode.merge(self._root.left							,       self._root.right  )

         return result





     def 						UpperCAmelCase(self :       Union[str, Any]  )		->		T:







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





     def 						UpperCAmelCase(self :       List[Any]  )		->		None:
         snake_case						=   None





     def 						UpperCAmelCase(self :       Optional[Any]  )		->		list[Any]:
         snake_case						=   []
         while self:
             result.append(self.pop()  )

         return result





     def __bool__(self :       Optional[int]  )		->		bool:
         return self._root is not 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 EfficientNetImageProcessor
class  _SCREAMING_SNAKE_CASE				(		unittest.TestCase						):


			'''simple docstring'''





			def __init__( self				:    List[str]			,   lowercase				:    Optional[Any]			,   lowercase				:    int=1_3			,   lowercase				:    List[Any]=3			,   lowercase				:    int=1_8			,   lowercase				:    List[Any]=3_0			,   lowercase				:    Optional[int]=4_0_0			,   lowercase				:    Any=True			,   lowercase				:    int=None			,   lowercase				:    Any=True			,   lowercase				:    Optional[int]=[0.5, 0.5, 0.5]			,   lowercase				:    List[Any]=[0.5, 0.5, 0.5]			,   )				->     List[Any]:

									'''simple docstring'''







									UpperCamelCase__       =			size if size is not None else {"""height""": 1_8, """width""": 1_8}
									UpperCamelCase__       =			parent
									UpperCamelCase__       =			batch_size
									UpperCamelCase__       =			num_channels
									UpperCamelCase__       =			image_size
									UpperCamelCase__       =			min_resolution
									UpperCamelCase__       =			max_resolution
									UpperCamelCase__       =			do_resize
									UpperCamelCase__       =			size
									UpperCamelCase__       =			do_normalize
									UpperCamelCase__       =			image_mean
									UpperCamelCase__       =			image_std







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

									'''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  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE							,unittest.TestCase						):


			'''simple docstring'''





			__a				:				List[Any]         =       EfficientNetImageProcessor if is_vision_available() else None






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

									'''simple docstring'''







									UpperCamelCase__       =			EfficientNetImageProcessorTester(self      )






			@property
			def 					A   ( self				:    Optional[int]      )				->     Optional[Any]:

									'''simple docstring'''







									return self.image_processor_tester.prepare_image_processor_dict()






			def 					A   ( self				:    Dict      )				->     str:

									'''simple docstring'''







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






			def 					A   ( self				:    List[str]      )				->     Optional[int]:

									'''simple docstring'''







									UpperCamelCase__       =			self.image_processing_class.from_dict(self.image_processor_dict      )
									self.assertEqual(image_processor.size			,   {"""height""": 1_8, """width""": 1_8}      )

									UpperCamelCase__       =			self.image_processing_class.from_dict(self.image_processor_dict			,   size=4_2      )
									self.assertEqual(image_processor.size			,   {"""height""": 4_2, """width""": 4_2}      )






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

									'''simple docstring'''







									UpperCamelCase__       =			self.image_processing_class(**self.image_processor_dict      )
									# create random PIL images
									UpperCamelCase__       =			prepare_image_inputs(self.image_processor_tester			,   equal_resolution=lowercase      )
									for image in image_inputs:
															self.assertIsInstance(lowercase			,   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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )			,   )

									# Test batched
									UpperCamelCase__       =			image_processing(lowercase			,   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 					A   ( self				:    Dict      )				->     Optional[int]:

									'''simple docstring'''







									UpperCamelCase__       =			self.image_processing_class(**self.image_processor_dict      )
									# create random numpy tensors
									UpperCamelCase__       =			prepare_image_inputs(self.image_processor_tester			,   equal_resolution=lowercase			,   numpify=lowercase      )
									for image in image_inputs:
															self.assertIsInstance(lowercase			,   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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )			,   )

									# Test batched
									UpperCamelCase__       =			image_processing(lowercase			,   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 					A   ( self				:    Optional[int]      )				->     List[str]:

									'''simple docstring'''







									UpperCamelCase__       =			self.image_processing_class(**self.image_processor_dict      )
									# create random PyTorch tensors
									UpperCamelCase__       =			prepare_image_inputs(self.image_processor_tester			,   equal_resolution=lowercase			,   torchify=lowercase      )
									for image in image_inputs:
															self.assertIsInstance(lowercase			,   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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )			,   )

									# Test batched
									UpperCamelCase__       =			image_processing(lowercase			,   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"""],
									    )			,   )
