"""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 							__snake_case(					unittest.TestCase	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase					,       __lowerCamelCase=13					,       __lowerCamelCase=3					,       __lowerCamelCase=18					,       __lowerCamelCase=30					,       __lowerCamelCase=400					,       __lowerCamelCase=True					,       __lowerCamelCase=None					,       __lowerCamelCase=True					,       __lowerCamelCase=[0.5, 0.5, 0.5]					,       __lowerCamelCase=[0.5, 0.5, 0.5]					,       ):




         '''simple docstring'''



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

    def 						_a							(  self	):




         '''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 							__snake_case(					A_				,	unittest.TestCase	):


    '''simple docstring'''





    _UpperCAmelCase       =			EfficientNetImageProcessor if is_vision_available() else None
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Optional[Any]								=		EfficientNetImageProcessorTester(self	)
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         return self.image_processor_tester.prepare_image_processor_dict()
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Dict								=		self.image_processing_class(**self.image_processor_dict	)
         self.assertTrue(hasattr(__lowerCamelCase					,       'image_mean'	)	)
         self.assertTrue(hasattr(__lowerCamelCase					,       'image_std'	)	)
         self.assertTrue(hasattr(__lowerCamelCase					,       'do_normalize'	)	)
         self.assertTrue(hasattr(__lowerCamelCase					,       'do_resize'	)	)
         self.assertTrue(hasattr(__lowerCamelCase					,       'size'	)	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Union[str, Any]								=		self.image_processing_class.from_dict(self.image_processor_dict	)
         self.assertEqual(image_processor.size					,       {'height': 18, 'width': 18}	)

         __A					:					int								=		self.image_processing_class.from_dict(self.image_processor_dict					,       size=42	)
         self.assertEqual(image_processor.size					,       {'height': 42, 'width': 42}	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					List[Any]								=		self.image_processing_class(**self.image_processor_dict	)
         # create random PIL images
         __A					:					Dict								=		prepare_image_inputs(self.image_processor_tester					,       equal_resolution=__lowerCamelCase	)
         for image in image_inputs:
              self.assertIsInstance(__lowerCamelCase					,       Image.Image	)

         # Test not batched input
         __A					:					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.size['height'],
                 self.image_processor_tester.size['width'],
             )					,       )

         # Test batched
         __A					:					Optional[int]								=		image_processing(__lowerCamelCase					,       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	):




         '''simple docstring'''



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

         # Test not batched input
         __A					:					List[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.size['height'],
                 self.image_processor_tester.size['width'],
             )					,       )

         # Test batched
         __A					:					Union[str, Any]								=		image_processing(__lowerCamelCase					,       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	):




         '''simple docstring'''



         __A					:					Union[str, Any]								=		self.image_processing_class(**self.image_processor_dict	)
         # create random PyTorch tensors
         __A					:					List[str]								=		prepare_image_inputs(self.image_processor_tester					,       equal_resolution=__lowerCamelCase					,       torchify=__lowerCamelCase	)
         for image in image_inputs:
              self.assertIsInstance(__lowerCamelCase					,       torch.Tensor	)

         # Test not batched input
         __A					:					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.size['height'],
                 self.image_processor_tester.size['width'],
             )					,       )

         # Test batched
         __A					:					List[str]								=		image_processing(__lowerCamelCase					,       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'],
             )					,       )




"""simple docstring"""







from __future__ import annotations

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

lowerCamelCase      : List[str]       =TypeVar('''T''', bound=bool)






class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					T								=		value
         __A					:					RandomizedHeapNode[T] | None								=		None
         __A					:					RandomizedHeapNode[T] | None								=		None
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         return self._value

    @staticmethod
    def 						_a							(  __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         if not roota:
              return roota

         if not roota:
              return roota

         if roota.value > roota.value:
              __A						,		__A					:					List[str]								=		roota, roota

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

         __A					:					Dict								=		RandomizedHeapNode.merge(roota.left					,       __lowerCamelCase	)

         return roota







class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase = ()	):




         '''simple docstring'''



         __A					:					RandomizedHeapNode[T] | None								=		None

         if data:
              for item in data:
                   self.insert(__lowerCamelCase	)
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					List[str]								=		RandomizedHeapNode.merge(self._root					,       RandomizedHeapNode(__lowerCamelCase	)	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					int								=		self.top()

         if self._root is None:
              return None

         __A					:					Dict								=		RandomizedHeapNode.merge(self._root.left					,       self._root.right	)

         return result
    def 						_a							(  self	):




         '''simple docstring'''



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




         '''simple docstring'''



         __A					:					Dict								=		None
    def 						_a							(  self	):




         '''simple docstring'''



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

         return result

    def __bool__(  self	):




         '''simple docstring'''



         return self._root is not None


if __name__ == "__main__":
       import doctest

       doctest.testmod()



