




import random
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():
				import PIL

				from transformers import FlavaImageProcessor
				from transformers.image_utils import PILImageResampling
				from transformers.models.flava.image_processing_flava import (
				    FLAVA_CODEBOOK_MEAN,
				    FLAVA_CODEBOOK_STD,
				    FLAVA_IMAGE_MEAN,
				    FLAVA_IMAGE_STD,
				)
else:
				lowercase_     :    Dict      =			None






class    __lowerCAmelCase			(	unittest.TestCase			):







			def __init__(	self   :      Any   ,   snake_case__   :      Optional[int]   ,   snake_case__   :      List[str]=7   ,   snake_case__   :      str=3   ,   snake_case__   :      Optional[int]=30   ,   snake_case__   :      Optional[Any]=400   ,   snake_case__   :      List[str]=True   ,   snake_case__   :      Union[str, Any]=None   ,   snake_case__   :      Any=True   ,   snake_case__   :      List[Any]=None   ,   snake_case__   :      str=None   ,   snake_case__   :      str=True   ,   snake_case__   :      Optional[Any]=1 / 255   ,   snake_case__   :      Optional[int]=True   ,   snake_case__   :      Tuple=FLAVA_IMAGE_MEAN   ,   snake_case__   :      str=FLAVA_IMAGE_STD   ,   snake_case__   :      str=14   ,   snake_case__   :      List[Any]=75   ,   snake_case__   :      str=None   ,   snake_case__   :      Tuple=16   ,   snake_case__   :      Any=0.3   ,   snake_case__   :      List[Any]=None   ,   snake_case__   :      Any=True   ,   snake_case__   :      Tuple=None   ,   snake_case__   :      List[Any]=None   ,   snake_case__   :      Optional[int]=True   ,   snake_case__   :      Union[str, Any]=None   ,   snake_case__   :      Optional[int]=True   ,   snake_case__   :      Tuple=True   ,   snake_case__   :      Tuple=FLAVA_CODEBOOK_MEAN   ,   snake_case__   :      Optional[int]=FLAVA_CODEBOOK_STD   ,   ):




									"""simple docstring"""


									_UpperCAmelCase													=					size if size is not None else {"height": 224, "width": 224}
									_UpperCAmelCase													=					crop_size if crop_size is not None else {"height": 224, "width": 224}
									_UpperCAmelCase													=					codebook_size if codebook_size is not None else {"height": 112, "width": 112}
									_UpperCAmelCase													=					codebook_crop_size if codebook_crop_size is not None else {"height": 112, "width": 112}

									_UpperCAmelCase													=					parent
									_UpperCAmelCase													=					batch_size
									_UpperCAmelCase													=					num_channels
									_UpperCAmelCase													=					do_resize
									_UpperCAmelCase													=					do_rescale
									_UpperCAmelCase													=					rescale_factor
									_UpperCAmelCase													=					min_resolution
									_UpperCAmelCase													=					max_resolution
									_UpperCAmelCase													=					size
									_UpperCAmelCase													=					resample if resample is not None else PILImageResampling.BICUBIC
									_UpperCAmelCase													=					do_normalize
									_UpperCAmelCase													=					image_mean
									_UpperCAmelCase													=					image_std
									_UpperCAmelCase													=					do_center_crop
									_UpperCAmelCase													=					crop_size

									_UpperCAmelCase													=					input_size_patches
									_UpperCAmelCase													=					total_mask_patches
									_UpperCAmelCase													=					mask_group_max_patches
									_UpperCAmelCase													=					mask_group_min_patches
									_UpperCAmelCase													=					mask_group_min_aspect_ratio
									_UpperCAmelCase													=					mask_group_max_aspect_ratio

									_UpperCAmelCase													=					codebook_do_resize
									_UpperCAmelCase													=					codebook_size
									_UpperCAmelCase													=					codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
									_UpperCAmelCase													=					codebook_do_center_crop
									_UpperCAmelCase													=					codebook_crop_size
									_UpperCAmelCase													=					codebook_do_map_pixels
									_UpperCAmelCase													=					codebook_do_normalize
									_UpperCAmelCase													=					codebook_image_mean
									_UpperCAmelCase													=					codebook_image_std







			def UpperCamelCase      (	self   :      Union[str, 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,
									    "resample": self.resample,
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "input_size_patches": self.input_size_patches,
									    "total_mask_patches": self.total_mask_patches,
									    "mask_group_max_patches": self.mask_group_max_patches,
									    "mask_group_min_patches": self.mask_group_min_patches,
									    "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
									    "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
									    "codebook_do_resize": self.codebook_do_resize,
									    "codebook_size": self.codebook_size,
									    "codebook_resample": self.codebook_resample,
									    "codebook_do_center_crop": self.codebook_do_center_crop,
									    "codebook_crop_size": self.codebook_crop_size,
									    "codebook_do_map_pixels": self.codebook_do_map_pixels,
									    "codebook_do_normalize": self.codebook_do_normalize,
									    "codebook_image_mean": self.codebook_image_mean,
									    "codebook_image_std": self.codebook_image_std,
									}







			def UpperCamelCase      (	self   :      Tuple				):




									"""simple docstring"""


									return (self.size["height"], self.size["width"])







			def UpperCamelCase      (	self   :      str				):




									"""simple docstring"""


									return (
									    (self.input_size_patches, self.input_size_patches)
									    if not isinstance(self.input_size_patches   ,   snake_case__				)
									    else self.input_size_patches
									)







			def UpperCamelCase      (	self   :      Tuple				):




									"""simple docstring"""


									return (self.codebook_size["height"], self.codebook_size["width"])






@require_torch
@require_vision
class    __lowerCAmelCase			(	UpperCAmelCase__ ,		unittest.TestCase			):
			snake_case_				:			Dict								=					FlavaImageProcessor if is_vision_available() else None
			snake_case_				:			Optional[int]								=					None







			def UpperCamelCase      (	self   :      Any				):




									"""simple docstring"""


									_UpperCAmelCase													=					FlavaImageProcessingTester(self				)







			@property
			def UpperCamelCase      (	self   :      int				):




									"""simple docstring"""


									return self.image_processor_tester.prepare_image_processor_dict()







			def UpperCamelCase      (	self   :      List[Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					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__   ,   "resample"				)				)
									self.assertTrue(hasattr(snake_case__   ,   "crop_size"				)				)
									self.assertTrue(hasattr(snake_case__   ,   "do_center_crop"				)				)
									self.assertTrue(hasattr(snake_case__   ,   "do_rescale"				)				)
									self.assertTrue(hasattr(snake_case__   ,   "rescale_factor"				)				)
									self.assertTrue(hasattr(snake_case__   ,   "masking_generator"				)				)
									self.assertTrue(hasattr(snake_case__   ,   "codebook_do_resize"				)				)
									self.assertTrue(hasattr(snake_case__   ,   "codebook_size"				)				)
									self.assertTrue(hasattr(snake_case__   ,   "codebook_resample"				)				)
									self.assertTrue(hasattr(snake_case__   ,   "codebook_do_center_crop"				)				)
									self.assertTrue(hasattr(snake_case__   ,   "codebook_crop_size"				)				)
									self.assertTrue(hasattr(snake_case__   ,   "codebook_do_map_pixels"				)				)
									self.assertTrue(hasattr(snake_case__   ,   "codebook_do_normalize"				)				)
									self.assertTrue(hasattr(snake_case__   ,   "codebook_image_mean"				)				)
									self.assertTrue(hasattr(snake_case__   ,   "codebook_image_std"				)				)







			def UpperCamelCase      (	self   :      Union[str, Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.image_processing_class.from_dict(self.image_processor_dict				)
									self.assertEqual(image_processor.size   ,   {"height": 224, "width": 224}				)
									self.assertEqual(image_processor.crop_size   ,   {"height": 224, "width": 224}				)
									self.assertEqual(image_processor.codebook_size   ,   {"height": 112, "width": 112}				)
									self.assertEqual(image_processor.codebook_crop_size   ,   {"height": 112, "width": 112}				)

									_UpperCAmelCase													=					self.image_processing_class.from_dict(
									    self.image_processor_dict   ,   size=42   ,   crop_size=84   ,   codebook_size=33   ,   codebook_crop_size=66				)
									self.assertEqual(image_processor.size   ,   {"height": 42, "width": 42}				)
									self.assertEqual(image_processor.crop_size   ,   {"height": 84, "width": 84}				)
									self.assertEqual(image_processor.codebook_size   ,   {"height": 33, "width": 33}				)
									self.assertEqual(image_processor.codebook_crop_size   ,   {"height": 66, "width": 66}				)







			def UpperCamelCase      (	self   :      Dict				):




									"""simple docstring"""


									pass







			def UpperCamelCase      (	self   :      List[Any]				):




									"""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=snake_case__				)
									for image in image_inputs:
															self.assertIsInstance(snake_case__   ,   PIL.Image.Image				)

									# Test not batched input
									_UpperCAmelCase													=					image_processing(image_inputs[0]   ,   return_tensors="pt"				)

									# Test no bool masked pos
									self.assertFalse("bool_masked_pos" in encoded_images				)

									_UpperCAmelCase							,			_UpperCAmelCase													=					self.image_processor_tester.get_expected_image_size()

									self.assertEqual(
									    encoded_images.pixel_values.shape   ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,   )

									# Test batched
									_UpperCAmelCase													=					image_processing(snake_case__   ,   return_tensors="pt"				)
									_UpperCAmelCase							,			_UpperCAmelCase													=					self.image_processor_tester.get_expected_image_size()

									# Test no bool masked pos
									self.assertFalse("bool_masked_pos" in encoded_images				)

									self.assertEqual(
									    encoded_images.pixel_values.shape   ,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )   ,   )







			def UpperCamelCase      (	self   :      Tuple   ,   snake_case__   :      str   ,   snake_case__   :      Optional[Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.image_processing_class(**self.image_processor_dict				)
									# create random tensors
									_UpperCAmelCase													=					prepare_image_inputs(self.image_processor_tester   ,   equal_resolution=snake_case__   ,   **snake_case__				)
									for image in image_inputs:
															self.assertIsInstance(snake_case__   ,   snake_case__				)

									# Test not batched input
									_UpperCAmelCase													=					image_processing(image_inputs[0]   ,   return_tensors="pt"				)

									_UpperCAmelCase							,			_UpperCAmelCase													=					self.image_processor_tester.get_expected_image_size()
									self.assertEqual(
									    encoded_images.pixel_values.shape   ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,   )

									_UpperCAmelCase													=					image_processing(snake_case__   ,   return_image_mask=snake_case__   ,   return_tensors="pt"				)

									_UpperCAmelCase							,			_UpperCAmelCase													=					self.image_processor_tester.get_expected_image_size()
									self.assertEqual(
									    encoded_images.pixel_values.shape   ,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )   ,   )

									_UpperCAmelCase							,			_UpperCAmelCase													=					self.image_processor_tester.get_expected_mask_size()
									self.assertEqual(
									    encoded_images.bool_masked_pos.shape   ,   (
									        self.image_processor_tester.batch_size,
									        expected_height,
									        expected_width,
									    )   ,   )

									# Test batched
									_UpperCAmelCase													=					image_processing(snake_case__   ,   return_tensors="pt"				).pixel_values

									_UpperCAmelCase							,			_UpperCAmelCase													=					self.image_processor_tester.get_expected_image_size()
									self.assertEqual(
									    encoded_images.shape   ,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )   ,   )

									# Test masking
									_UpperCAmelCase													=					image_processing(snake_case__   ,   return_image_mask=snake_case__   ,   return_tensors="pt"				)

									_UpperCAmelCase							,			_UpperCAmelCase													=					self.image_processor_tester.get_expected_image_size()
									self.assertEqual(
									    encoded_images.pixel_values.shape   ,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )   ,   )

									_UpperCAmelCase							,			_UpperCAmelCase													=					self.image_processor_tester.get_expected_mask_size()
									self.assertEqual(
									    encoded_images.bool_masked_pos.shape   ,   (
									        self.image_processor_tester.batch_size,
									        expected_height,
									        expected_width,
									    )   ,   )







			def UpperCamelCase      (	self   :      Dict				):




									"""simple docstring"""


									self._test_call_framework(np.ndarray   ,   prepare_kwargs={"numpify": True}				)







			def UpperCamelCase      (	self   :      Any				):




									"""simple docstring"""


									self._test_call_framework(torch.Tensor   ,   prepare_kwargs={"torchify": True}				)







			def UpperCamelCase      (	self   :      str				):




									"""simple docstring"""


									random.seed(1_234				)
									_UpperCAmelCase													=					self.image_processing_class(**self.image_processor_dict				)
									_UpperCAmelCase													=					prepare_image_inputs(self.image_processor_tester   ,   equal_resolution=snake_case__   ,   torchify=snake_case__				)

									# Test not batched input
									_UpperCAmelCase													=					image_processing(image_inputs[0]   ,   return_image_mask=snake_case__   ,   return_tensors="pt"				)
									self.assertEqual(encoded_images.bool_masked_pos.sum().item()   ,   75				)







			def UpperCamelCase      (	self   :      Any				):




									"""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=snake_case__				)
									for image in image_inputs:
															self.assertIsInstance(snake_case__   ,   PIL.Image.Image				)

									# Test not batched input
									_UpperCAmelCase													=					image_processing(image_inputs[0]   ,   return_codebook_pixels=snake_case__   ,   return_tensors="pt"				)
									_UpperCAmelCase							,			_UpperCAmelCase													=					self.image_processor_tester.get_expected_codebook_image_size()
									self.assertEqual(
									    encoded_images.codebook_pixel_values.shape   ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,   )

									# Test batched
									_UpperCAmelCase													=					image_processing(snake_case__   ,   return_codebook_pixels=snake_case__   ,   return_tensors="pt"				)
									_UpperCAmelCase							,			_UpperCAmelCase													=					self.image_processor_tester.get_expected_codebook_image_size()
									self.assertEqual(
									    encoded_images.codebook_pixel_values.shape   ,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )   ,   )










from __future__ import annotations

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






class    __lowerCAmelCase			(	UpperCAmelCase__			):







			@abstractmethod
			def __lt__(	self   :      T   ,   snake_case__   :      T				):




									"""simple docstring"""


									pass







			@abstractmethod
			def __gt__(	self   :      T   ,   snake_case__   :      T				):




									"""simple docstring"""


									pass







			@abstractmethod
			def __eq__(	self   :      T   ,   snake_case__   :      object				):




									"""simple docstring"""


									pass




lowercase_     :    Tuple      =			TypeVar('T', bound=Comparable)






class    __lowerCAmelCase			(	Generic[T]			):







			def __init__(	self   :      Optional[int]				):




									"""simple docstring"""


									_UpperCAmelCase													=					[]
									_UpperCAmelCase													=					0







			def __repr__(	self   :      Dict				):




									"""simple docstring"""


									return str(self.h				)







			def UpperCamelCase      (	self   :      Optional[Any]   ,   snake_case__   :      int				):




									"""simple docstring"""


									if child_idx > 0:
															return (child_idx - 1) // 2
									return None







			def UpperCamelCase      (	self   :      Tuple   ,   snake_case__   :      int				):




									"""simple docstring"""


									_UpperCAmelCase													=					2 * parent_idx + 1
									if left_child_index < self.heap_size:
															return left_child_index
									return None







			def UpperCamelCase      (	self   :      Union[str, Any]   ,   snake_case__   :      int				):




									"""simple docstring"""


									_UpperCAmelCase													=					2 * parent_idx + 2
									if right_child_index < self.heap_size:
															return right_child_index
									return None







			def UpperCamelCase      (	self   :      Dict   ,   snake_case__   :      int				):




									"""simple docstring"""


									if index < self.heap_size:
															_UpperCAmelCase													=					index
															_UpperCAmelCase													=					self.left_child_idx(snake_case__				)
															_UpperCAmelCase													=					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]:
																					_UpperCAmelCase													=					left_child
															if right_child is not None and self.h[right_child] > self.h[violation]:
																					_UpperCAmelCase													=					right_child
															# if violation indeed exists
															if violation != index:
																					# swap to fix the violation
																					_UpperCAmelCase							,			_UpperCAmelCase													=					self.h[index], self.h[violation]
																					# fix the subsequent violation recursively if any
																					self.max_heapify(snake_case__				)







			def UpperCamelCase      (	self   :      Tuple   ,   snake_case__   :      Iterable[T]				):




									"""simple docstring"""


									_UpperCAmelCase													=					list(snake_case__				)
									_UpperCAmelCase													=					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 UpperCamelCase      (	self   :      Tuple				):




									"""simple docstring"""


									if self.heap_size >= 2:
															_UpperCAmelCase													=					self.h[0]
															_UpperCAmelCase													=					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 UpperCamelCase      (	self   :      str   ,   snake_case__   :      T				):




									"""simple docstring"""


									self.h.append(snake_case__				)
									_UpperCAmelCase													=					(self.heap_size - 1) // 2
									self.heap_size += 1
									while idx >= 0:
															self.max_heapify(snake_case__				)
															_UpperCAmelCase													=					(idx - 1) // 2







			def UpperCamelCase      (	self   :      Any				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.heap_size
									for j in range(size - 1   ,   0   ,   -1				):
															_UpperCAmelCase							,			_UpperCAmelCase													=					self.h[j], self.h[0]
															self.heap_size -= 1
															self.max_heapify(0				)
									_UpperCAmelCase													=					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}""")

								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(1_00)
								print(f"""after new value 100 inserted: {heap}""")

								heap.heap_sort()
								print(f"""heap-sorted array: {heap}\n""")




