
"""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 TvltImageProcessor


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_=10						,					lowerCamelCase_=10						,					lowerCamelCase_=False						,					lowerCamelCase_=False			)  ->      Optional[int]:


					'''simple docstring'''
					__a:    List[Any]		     =       []
					for i in range(image_processor_tester.num_frames			):
										video.append(np.random.randint(2_55						,					size=(image_processor_tester.num_channels, width, height)						,					dtype=np.uinta			)			)

					if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										__a:    Dict		     =       [Image.fromarray(np.moveaxis(lowerCamelCase_						,					0						,					-1			)			) for frame in video]

					if torchify:
										__a:    Tuple		     =       [torch.from_numpy(lowerCamelCase_			) for frame in video]

					return video


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_=False						,					lowerCamelCase_=False						,					lowerCamelCase_=False			)  ->      List[Any]:


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

					__a:    Union[str, Any]		     =       []
					for i in range(image_processor_tester.batch_size			):
										if equal_resolution:
															__a:    List[str]		     =       image_processor_tester.max_resolution
										else:
															__a:    Union[str, Any]		     =       np.random.choice(
															    np.arange(image_processor_tester.min_resolution						,					image_processor_tester.max_resolution			)						,					2			)
															__a:    List[Any]		     =       prepare_video(
															    image_processor_tester=lowerCamelCase_						,					width=lowerCamelCase_						,					height=lowerCamelCase_						,					numpify=lowerCamelCase_						,					torchify=lowerCamelCase_						,					)
										video_inputs.append(lowerCamelCase_			)

					return video_inputs




class  _snake_case	(    unittest.TestCase							):






							def __init__(       self    :      List[Any]    ,			snake_case__    :      str    ,			snake_case__    :      int=7    ,			snake_case__    :      List[str]=3    ,			snake_case__    :      Union[str, Any]=4    ,			snake_case__    :      Tuple=1_8    ,			snake_case__    :      List[str]=3_0    ,			snake_case__    :      List[Any]=4_0_0    ,			snake_case__    :      Tuple=True    ,			snake_case__    :      Optional[int]=None    ,			snake_case__    :      Tuple=True    ,			snake_case__    :      Dict=[0.5, 0.5, 0.5]    ,			snake_case__    :      List[Any]=[0.5, 0.5, 0.5]    ,			snake_case__    :      Union[str, Any]=True    ,			snake_case__    :      Tuple=None    ,			):






												"""simple docstring"""



												__a:    Dict		     =       size if size is not None else {"""shortest_edge""": 1_8}
												__a:    List[Any]		     =       crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}

												__a:    Tuple		     =       parent
												__a:    List[Any]		     =       batch_size
												__a:    Dict		     =       num_channels
												__a:    List[Any]		     =       num_frames
												__a:    int		     =       image_size
												__a:    List[str]		     =       min_resolution
												__a:    Dict		     =       max_resolution
												__a:    Any		     =       do_resize
												__a:    Tuple		     =       size
												__a:    Union[str, Any]		     =       do_normalize
												__a:    Tuple		     =       image_mean
												__a:    Tuple		     =       image_std
												__a:    Union[str, Any]		     =       do_center_crop
												__a:    Any		     =       crop_size






							def 				__lowercase	(       self    :      List[str]     ):






												"""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,
												    "do_center_crop": self.do_center_crop,
												    "crop_size": self.crop_size,
												}






@require_torch
@require_vision
class  _snake_case	(    _a			,					unittest.TestCase							):
							SCREAMING_SNAKE_CASE		  =    TvltImageProcessor if is_vision_available() else None






							def 				__lowercase	(       self    :      Tuple     ):






												"""simple docstring"""



												__a:    Any		     =       TvltImageProcessorTester(self     )






							@property
							def 				__lowercase	(       self    :      Optional[int]     ):






												"""simple docstring"""



												return self.image_processor_tester.prepare_image_processor_dict()






							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												__a:    Optional[Any]		     =       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    ,			"""do_center_crop"""     )     )
												self.assertTrue(hasattr(__lowerCamelCase    ,			"""size"""     )     )






							def 				__lowercase	(       self    :      Optional[int]     ):






												"""simple docstring"""



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

												# Test not batched input
												__a:    int		     =       image_processor(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
												__a:    Optional[int]		     =       image_processor(__lowerCamelCase    ,			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 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												__a:    Any		     =       self.image_processing_class(**self.image_processor_dict     )
												# create random numpy tensors
												__a:    List[str]		     =       prepare_video_inputs(self.image_processor_tester    ,			equal_resolution=__lowerCamelCase    ,			numpify=__lowerCamelCase     )
												for video in video_inputs:
																	self.assertIsInstance(__lowerCamelCase    ,			__lowerCamelCase     )
																	self.assertIsInstance(video[0]    ,			np.ndarray     )

												# Test not batched input
												__a:    Optional[Any]		     =       image_processor(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
												__a:    Any		     =       image_processor(__lowerCamelCase    ,			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 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



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

												# Test not batched input
												__a:    List[Any]		     =       image_processor(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
												__a:    Dict		     =       image_processor(__lowerCamelCase    ,			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"""],
												    )    ,			)








from __future__ import annotations

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

class        _SCREAMING_SNAKE_CASE			(     _a ):





	@abstractmethod
	def __lt__(						self							:   T					,    __lowerCamelCase							:   T      ):
		pass





	@abstractmethod
	def __gt__(						self							:   T					,    __lowerCamelCase							:   T      ):
		pass





	@abstractmethod
	def __eq__(						self							:   T					,    __lowerCamelCase							:   object      ):
		pass
UpperCAmelCase_					:		str           = TypeVar('''T''', bound=Comparable)

class        _SCREAMING_SNAKE_CASE			(     Generic[T] ):





	def __init__(						self							:   Dict      ):
		UpperCamelCase	:list[T]		=      []
		UpperCamelCase	:int		=      0





	def __repr__(						self							:   List[str]      ):
		return str(self.h      )





	def 		_A					(						self							:   Tuple					,    __lowerCamelCase							:   int      ):
		if child_idx > 0:
			return (child_idx - 1) // 2
		return None





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   int      ):
		UpperCamelCase	:Optional[int]		=      2 * parent_idx + 1
		if left_child_index < self.heap_size:
			return left_child_index
		return None





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   int      ):
		UpperCamelCase	:Optional[Any]		=      2 * parent_idx + 2
		if right_child_index < self.heap_size:
			return right_child_index
		return None





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





	def 		_A					(						self							:   Any					,    __lowerCamelCase							:   Iterable[T]      ):
		UpperCamelCase	:Optional[Any]		=      list(__lowerCamelCase      )
		UpperCamelCase	: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(__lowerCamelCase      )





	def 		_A					(						self							:   Optional[int]      ):
		if self.heap_size >= 2:
			UpperCamelCase	:List[str]		=      self.h[0]
			UpperCamelCase	:str		=      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 		_A					(						self							:   Optional[Any]					,    __lowerCamelCase							:   T      ):
		self.h.append(__lowerCamelCase      )
		UpperCamelCase	:Any		=      (self.heap_size - 1) // 2
		self.heap_size += 1
		while idx >= 0:
			self.max_heapify(__lowerCamelCase      )
			UpperCamelCase	:List[str]		=      (idx - 1) // 2





	def 		_A					(						self							:   int      ):
		UpperCamelCase	:int		=      self.heap_size
		for j in range(size - 1					,    0					,    -1      ):
			UpperCamelCase	, UpperCamelCase	:Any		=      self.h[j], self.h[0]
			self.heap_size -= 1
			self.max_heapify(0      )
		UpperCamelCase	: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],
	    [1_03, 9, 1, 7, 11, 15, 25, 2_01, 2_09, 1_07, 5],
	    [-45, -2, -5],
	]:
		print(F'''unsorted array: {unsorted}''')

		UpperCAmelCase_					:		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(1_00)
		print(F'''after new value 100 inserted: {heap}''')

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