
from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar

class __snake_case	(   lowerCAmelCase  ):
    @abstractmethod
    def __lt__(						self	,snake_case   ):



           '''simple docstring'''







           pass
    @abstractmethod
    def __gt__(						self	,snake_case   ):



           '''simple docstring'''







           pass
    @abstractmethod
    def __eq__(						self	,snake_case   ):



           '''simple docstring'''







           pass






lowercase						:      Any							=							TypeVar("""T""", bound=Comparable)

class __snake_case	(   Generic[T]  ):
    def __init__(						self   ):



           '''simple docstring'''







           lowercase							:  list[T] 			=   []
           lowercase							:  int 			=   0
    def __repr__(						self   ):



           '''simple docstring'''







           return str(self.h   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           if child_idx > 0:
                  return (child_idx - 1) // 2
           return None
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           lowercase							:  Any 			=   2 * parent_idx + 1
           if left_child_index < self.heap_size:
                  return left_child_index
           return None
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           lowercase							:  Any 			=   2 * parent_idx + 2
           if right_child_index < self.heap_size:
                  return right_child_index
           return None
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           if index < self.heap_size:
                  lowercase							:  int 			=   index
                  lowercase							:  Dict 			=   self.left_child_idx(snake_case   )
                  lowercase							:  Optional[int] 			=   self.right_child_idx(snake_case   )
                  # check which child is larger than its parent
                  if left_child is not None and self.h[left_child] > self.h[violation]:
                         lowercase							:  Any 			=   left_child
                  if right_child is not None and self.h[right_child] > self.h[violation]:
                         lowercase							:  Tuple 			=   right_child
                  # if violation indeed exists
                  if violation != index:
                         # swap to fix the violation
                         lowercase			,    lowercase							:  int 			=   self.h[index], self.h[violation]
                         # fix the subsequent violation recursively if any
                         self.max_heapify(snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           lowercase							:  int 			=   list(snake_case   )
           lowercase							:  int 			=   len(self.h   )
           if self.heap_size > 1:
                  # max_heapify from right to left but exclude leaves (last level)
                  for i in range(self.heap_size // 2 - 1	,-1	,-1   ):
                         self.max_heapify(snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           if self.heap_size >= 2:
                  lowercase							:  Optional[int] 			=   self.h[0]
                  lowercase							:  Union[str, Any] 			=   self.h.pop(-1   )
                  self.heap_size -= 1
                  self.max_heapify(0   )
                  return me
           elif self.heap_size == 1:
                  self.heap_size -= 1
                  return self.h.pop(-1   )
           else:
                  raise Exception("""Empty heap"""   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           self.h.append(snake_case   )
           lowercase							:  Dict 			=   (self.heap_size - 1) // 2
           self.heap_size += 1
           while idx >= 0:
                  self.max_heapify(snake_case   )
                  lowercase							:  Optional[int] 			=   (idx - 1) // 2
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   self.heap_size
           for j in range(size - 1	,0	,-1   ):
                  lowercase			,    lowercase							:  Union[str, Any] 			=   self.h[j], self.h[0]
                  self.heap_size -= 1
                  self.max_heapify(0   )
           lowercase							:  List[str] 			=   size


if __name__ == "__main__":
  import doctest

  # run doc test
  doctest.testmod()

  # demo
  for unsorted in [
      [0],
      [2],
      [3, 5],
      [5, 3],
      [5, 5],
      [0, 0, 0, 0],
      [1, 1, 1, 1],
      [2, 2, 3, 5],
      [0, 2, 2, 3, 5],
      [2, 5, 3, 0, 2, 3, 0, 3],
      [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
      [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
      [-45, -2, -5],
  ]:
    print(F'''unsorted array: {unsorted}''')

    lowercase						:      Heap[int]							=							Heap()
    heap.build_max_heap(unsorted)
    print(F'''after build heap: {heap}''')

    print(F'''max value: {heap.extract_max()}''')
    print(F'''after max value removed: {heap}''')

    heap.insert(100)
    print(F'''after new value 100 inserted: {heap}''')

    heap.heap_sort()
    print(F'''heap-sorted array: {heap}\n''')


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_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VideoMAEImageProcessor

class __snake_case	(   unittest.TestCase  ):
    def __init__(						self	,snake_case	,snake_case=7	,snake_case=3	,snake_case=10	,snake_case=18	,snake_case=30	,snake_case=400	,snake_case=True	,snake_case=None	,snake_case=True	,snake_case=[0.5, 0.5, 0.5]	,snake_case=[0.5, 0.5, 0.5]	,snake_case=None	,):



           '''simple docstring'''







           lowercase							:  Dict 			=   size if size is not None else {"""shortest_edge""": 18}
           lowercase							:  Tuple 			=   crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

           lowercase							:  Optional[int] 			=   parent
           lowercase							:  Any 			=   batch_size
           lowercase							:  int 			=   num_channels
           lowercase							:  Dict 			=   num_frames
           lowercase							:  Any 			=   image_size
           lowercase							:  str 			=   min_resolution
           lowercase							:  Any 			=   max_resolution
           lowercase							:  Tuple 			=   do_resize
           lowercase							:  Optional[Any] 			=   size
           lowercase							:  str 			=   do_normalize
           lowercase							:  Optional[Any] 			=   image_mean
           lowercase							:  Optional[Any] 			=   image_std
           lowercase							:  Union[str, Any] 			=   crop_size
    def    _SCREAMING_SNAKE_CASE (						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,
               "crop_size": self.crop_size,
           }






@require_torch
@require_vision
class __snake_case	(   lowerCAmelCase      ,			unittest.TestCase  ):
    _a	:		Optional[Any]=       VideoMAEImageProcessor if is_vision_available() else None
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   VideoMAEImageProcessingTester(self   )
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:  Any 			=   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	,"""do_center_crop"""   )   )
           self.assertTrue(hasattr(snake_case	,"""size"""   )   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   self.image_processing_class.from_dict(self.image_processor_dict   )
           self.assertEqual(image_processor.size	,{"""shortest_edge""": 18}   )
           self.assertEqual(image_processor.crop_size	,{"""height""": 18, """width""": 18}   )

           lowercase							:  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   ):



           '''simple docstring'''







           pass
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random PIL videos
           lowercase							:  Optional[int] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,Image.Image   )

           # Test not batched input
           lowercase							:  Optional[Any] 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Union[str, Any] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   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   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random numpy tensors
           lowercase							:  Optional[Any] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case	,numpify=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,np.ndarray   )

           # Test not batched input
           lowercase							:  int 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Optional[int] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   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   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   self.image_processing_class(**self.image_processor_dict   )
           # create random PyTorch tensors
           lowercase							:  List[str] 			=   prepare_video_inputs(self.image_processor_tester	,equal_resolution=snake_case	,torchify=snake_case   )
           for video in video_inputs:
                  self.assertIsInstance(snake_case	,snake_case   )
                  self.assertIsInstance(video[0]	,torch.Tensor   )

           # Test not batched input
           lowercase							:  Optional[Any] 			=   image_processing(video_inputs[0]	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)

           # Test batched
           lowercase							:  Union[str, Any] 			=   image_processing(snake_case	,return_tensors="""pt"""   ).pixel_values
           self.assertEqual(
               encoded_videos.shape	,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )	,)
