



'''simple docstring'''





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:
   _UpperCamelCase							       =		_UpperCamelCase							       =		_UpperCamelCase							       =		_UpperCamelCase							       =		None






class       lowerCamelCase_					(       unittest.TestCase  ):
  """simple docstring"""




  def __init__(				self   :			Tuple    ,			_a   :			str    ,			_a   :			str=7    ,			_a   :			Tuple=3    ,			_a   :			Union[str, Any]=30    ,			_a   :			List[str]=400    ,			_a   :			Tuple=True    ,			_a   :			Any=None    ,			_a   :			Optional[int]=True    ,			_a   :			Optional[int]=None    ,			_a   :			Tuple=None    ,			_a   :			Dict=True    ,			_a   :			str=1 / 255    ,			_a   :			List[str]=True    ,			_a   :			Union[str, Any]=FLAVA_IMAGE_MEAN    ,			_a   :			List[str]=FLAVA_IMAGE_STD    ,			_a   :			str=14    ,			_a   :			Dict=75    ,			_a   :			Union[str, Any]=None    ,			_a   :			List[Any]=16    ,			_a   :			Optional[int]=0.3    ,			_a   :			Dict=None    ,			_a   :			int=True    ,			_a   :			Dict=None    ,			_a   :			Union[str, Any]=None    ,			_a   :			str=True    ,			_a   :			str=None    ,			_a   :			Optional[int]=True    ,			_a   :			List[Any]=True    ,			_a   :			str=FLAVA_CODEBOOK_MEAN    ,			_a   :			Union[str, Any]=FLAVA_CODEBOOK_STD    ,			)      ->      Optional[int]:
    __lowerCamelCase : Optional[Any]					 =				size if size is not None else {'height': 224, 'width': 224}
    __lowerCamelCase : Optional[int]					 =				crop_size if crop_size is not None else {'height': 224, 'width': 224}
    __lowerCamelCase : int					 =				codebook_size if codebook_size is not None else {'height': 112, 'width': 112}
    __lowerCamelCase : Union[str, Any]					 =				codebook_crop_size if codebook_crop_size is not None else {'height': 112, 'width': 112}

    __lowerCamelCase : List[str]					 =				parent
    __lowerCamelCase : int					 =				batch_size
    __lowerCamelCase : List[str]					 =				num_channels
    __lowerCamelCase : Dict					 =				do_resize
    __lowerCamelCase : Union[str, Any]					 =				do_rescale
    __lowerCamelCase : Union[str, Any]					 =				rescale_factor
    __lowerCamelCase : Tuple					 =				min_resolution
    __lowerCamelCase : Tuple					 =				max_resolution
    __lowerCamelCase : int					 =				size
    __lowerCamelCase : List[str]					 =				resample if resample is not None else PILImageResampling.BICUBIC
    __lowerCamelCase : List[str]					 =				do_normalize
    __lowerCamelCase : Any					 =				image_mean
    __lowerCamelCase : Any					 =				image_std
    __lowerCamelCase : Tuple					 =				do_center_crop
    __lowerCamelCase : Optional[int]					 =				crop_size

    __lowerCamelCase : Union[str, Any]					 =				input_size_patches
    __lowerCamelCase : Union[str, Any]					 =				total_mask_patches
    __lowerCamelCase : Tuple					 =				mask_group_max_patches
    __lowerCamelCase : Optional[Any]					 =				mask_group_min_patches
    __lowerCamelCase : Optional[int]					 =				mask_group_min_aspect_ratio
    __lowerCamelCase : Optional[Any]					 =				mask_group_max_aspect_ratio

    __lowerCamelCase : Any					 =				codebook_do_resize
    __lowerCamelCase : Union[str, Any]					 =				codebook_size
    __lowerCamelCase : Tuple					 =				codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
    __lowerCamelCase : Dict					 =				codebook_do_center_crop
    __lowerCamelCase : List[Any]					 =				codebook_crop_size
    __lowerCamelCase : Optional[Any]					 =				codebook_do_map_pixels
    __lowerCamelCase : Optional[Any]					 =				codebook_do_normalize
    __lowerCamelCase : Optional[int]					 =				codebook_image_mean
    __lowerCamelCase : str					 =				codebook_image_std




  def 		_lowercase  (				self   :			Optional[Any]     )      ->      Tuple:
    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 		_lowercase  (				self   :			Optional[Any]     )      ->      List[str]:
    return (self.size["height"], self.size["width"])




  def 		_lowercase  (				self   :			Optional[int]     )      ->      Any:
    return (
        (self.input_size_patches, self.input_size_patches)
        if not isinstance(self.input_size_patches    ,			_a     )
        else self.input_size_patches
    )




  def 		_lowercase  (				self   :			Union[str, Any]     )      ->      Any:
    return (self.codebook_size["height"], self.codebook_size["width"])






@require_torch
@require_vision
class       lowerCamelCase_					(       SCREAMING_SNAKE_CASE_     ,     unittest.TestCase  ):
  """simple docstring"""




  a_							      =FlavaImageProcessor if is_vision_available() else None
  a_							      =None




  def 		_lowercase  (				self   :			str     )      ->      List[Any]:
    __lowerCamelCase : Any					 =				FlavaImageProcessingTester(self     )




  @property
  def 		_lowercase  (				self   :			Union[str, Any]     )      ->      int:
    return self.image_processor_tester.prepare_image_processor_dict()




  def 		_lowercase  (				self   :			List[Any]     )      ->      Optional[Any]:
    __lowerCamelCase : int					 =				self.image_processing_class(**self.image_processor_dict     )
    self.assertTrue(hasattr(_a    ,			'image_mean'     )     )
    self.assertTrue(hasattr(_a    ,			'image_std'     )     )
    self.assertTrue(hasattr(_a    ,			'do_normalize'     )     )
    self.assertTrue(hasattr(_a    ,			'do_resize'     )     )
    self.assertTrue(hasattr(_a    ,			'resample'     )     )
    self.assertTrue(hasattr(_a    ,			'crop_size'     )     )
    self.assertTrue(hasattr(_a    ,			'do_center_crop'     )     )
    self.assertTrue(hasattr(_a    ,			'do_rescale'     )     )
    self.assertTrue(hasattr(_a    ,			'rescale_factor'     )     )
    self.assertTrue(hasattr(_a    ,			'masking_generator'     )     )
    self.assertTrue(hasattr(_a    ,			'codebook_do_resize'     )     )
    self.assertTrue(hasattr(_a    ,			'codebook_size'     )     )
    self.assertTrue(hasattr(_a    ,			'codebook_resample'     )     )
    self.assertTrue(hasattr(_a    ,			'codebook_do_center_crop'     )     )
    self.assertTrue(hasattr(_a    ,			'codebook_crop_size'     )     )
    self.assertTrue(hasattr(_a    ,			'codebook_do_map_pixels'     )     )
    self.assertTrue(hasattr(_a    ,			'codebook_do_normalize'     )     )
    self.assertTrue(hasattr(_a    ,			'codebook_image_mean'     )     )
    self.assertTrue(hasattr(_a    ,			'codebook_image_std'     )     )




  def 		_lowercase  (				self   :			Dict     )      ->      Optional[Any]:
    __lowerCamelCase : Optional[Any]					 =				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}     )

    __lowerCamelCase : str					 =				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 		_lowercase  (				self   :			int     )      ->      Optional[Any]:
    pass




  def 		_lowercase  (				self   :			Union[str, Any]     )      ->      Optional[Any]:
    # Initialize image_processing
    __lowerCamelCase : Any					 =				self.image_processing_class(**self.image_processor_dict     )
    # create random PIL images
    __lowerCamelCase : Optional[int]					 =				prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=_a     )
    for image in image_inputs:
      self.assertIsInstance(_a    ,			PIL.Image.Image     )

    # Test not batched input
    __lowerCamelCase : Dict					 =				image_processing(image_inputs[0]    ,			return_tensors='pt'     )

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

    __lowerCamelCase ,__lowerCamelCase : str					 =				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
    __lowerCamelCase : List[str]					 =				image_processing(_a    ,			return_tensors='pt'     )
    __lowerCamelCase ,__lowerCamelCase : int					 =				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 		_lowercase  (				self   :			Union[str, Any]    ,			_a   :			Union[str, Any]    ,			_a   :			Dict     )      ->      Tuple:
    # Initialize image_processing
    __lowerCamelCase : str					 =				self.image_processing_class(**self.image_processor_dict     )
    # create random tensors
    __lowerCamelCase : int					 =				prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=_a    ,			**_a     )
    for image in image_inputs:
      self.assertIsInstance(_a    ,			_a     )

    # Test not batched input
    __lowerCamelCase : Optional[int]					 =				image_processing(image_inputs[0]    ,			return_tensors='pt'     )

    __lowerCamelCase ,__lowerCamelCase : Optional[Any]					 =				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)    ,			)

    __lowerCamelCase : List[Any]					 =				image_processing(_a    ,			return_image_mask=_a    ,			return_tensors='pt'     )

    __lowerCamelCase ,__lowerCamelCase : List[Any]					 =				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,
        )    ,			)

    __lowerCamelCase ,__lowerCamelCase : Union[str, Any]					 =				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
    __lowerCamelCase : int					 =				image_processing(_a    ,			return_tensors='pt'     ).pixel_values

    __lowerCamelCase ,__lowerCamelCase : List[str]					 =				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
    __lowerCamelCase : int					 =				image_processing(_a    ,			return_image_mask=_a    ,			return_tensors='pt'     )

    __lowerCamelCase ,__lowerCamelCase : Union[str, Any]					 =				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,
        )    ,			)

    __lowerCamelCase ,__lowerCamelCase : str					 =				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 		_lowercase  (				self   :			int     )      ->      int:
    self._test_call_framework(np.ndarray    ,			prepare_kwargs={'numpify': True}     )




  def 		_lowercase  (				self   :			Optional[Any]     )      ->      Any:
    self._test_call_framework(torch.Tensor    ,			prepare_kwargs={'torchify': True}     )




  def 		_lowercase  (				self   :			Union[str, Any]     )      ->      Optional[int]:
    # Initialize image_processing
    random.seed(1234     )
    __lowerCamelCase : List[str]					 =				self.image_processing_class(**self.image_processor_dict     )
    __lowerCamelCase : Union[str, Any]					 =				prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=_a    ,			torchify=_a     )

    # Test not batched input
    __lowerCamelCase : List[str]					 =				image_processing(image_inputs[0]    ,			return_image_mask=_a    ,			return_tensors='pt'     )
    self.assertEqual(encoded_images.bool_masked_pos.sum().item()    ,			75     )




  def 		_lowercase  (				self   :			Dict     )      ->      int:
    # Initialize image_processing
    __lowerCamelCase : Optional[int]					 =				self.image_processing_class(**self.image_processor_dict     )
    # create random PIL images
    __lowerCamelCase : Optional[Any]					 =				prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=_a     )
    for image in image_inputs:
      self.assertIsInstance(_a    ,			PIL.Image.Image     )

    # Test not batched input
    __lowerCamelCase : Tuple					 =				image_processing(image_inputs[0]    ,			return_codebook_pixels=_a    ,			return_tensors='pt'     )
    __lowerCamelCase ,__lowerCamelCase : Optional[Any]					 =				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
    __lowerCamelCase : Optional[Any]					 =				image_processing(_a    ,			return_codebook_pixels=_a    ,			return_tensors='pt'     )
    __lowerCamelCase ,__lowerCamelCase : Tuple					 =				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,
        )    ,			)











'''simple docstring'''





from __future__ import annotations

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






class       lowerCamelCase_					(       SCREAMING_SNAKE_CASE_  ):
  """simple docstring"""




  @abstractmethod
  def __lt__(				self   :			T    ,			_a   :			T     )      ->      bool:
    pass




  @abstractmethod
  def __gt__(				self   :			T    ,			_a   :			T     )      ->      bool:
    pass




  @abstractmethod
  def __eq__(				self   :			T    ,			_a   :			object     )      ->      bool:
    pass




_UpperCamelCase							       =		TypeVar('T', bound=Comparable)






class       lowerCamelCase_					(       Generic[T]  ):
  """simple docstring"""




  def __init__(				self   :			List[str]     )      ->      None:
    __lowerCamelCase : list[T]					 =				[]
    __lowerCamelCase : int					 =				0




  def __repr__(				self   :			int     )      ->      str:
    return str(self.h     )




  def 		_lowercase  (				self   :			str    ,			_a   :			int     )      ->      int | None:
    if child_idx > 0:
      return (child_idx - 1) // 2
    return None




  def 		_lowercase  (				self   :			Tuple    ,			_a   :			int     )      ->      int | None:
    __lowerCamelCase : Optional[Any]					 =				2 * parent_idx + 1
    if left_child_index < self.heap_size:
      return left_child_index
    return None




  def 		_lowercase  (				self   :			Tuple    ,			_a   :			int     )      ->      int | None:
    __lowerCamelCase : Any					 =				2 * parent_idx + 2
    if right_child_index < self.heap_size:
      return right_child_index
    return None




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




  def 		_lowercase  (				self   :			int    ,			_a   :			Iterable[T]     )      ->      None:
    __lowerCamelCase : Dict					 =				list(_a     )
    __lowerCamelCase : Any					 =				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(_a     )




  def 		_lowercase  (				self   :			int     )      ->      T:
    if self.heap_size >= 2:
      __lowerCamelCase : Any					 =				self.h[0]
      __lowerCamelCase : Dict					 =				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 		_lowercase  (				self   :			Optional[Any]    ,			_a   :			T     )      ->      None:
    self.h.append(_a     )
    __lowerCamelCase : List[Any]					 =				(self.heap_size - 1) // 2
    self.heap_size += 1
    while idx >= 0:
      self.max_heapify(_a     )
      __lowerCamelCase : Union[str, Any]					 =				(idx - 1) // 2




  def 		_lowercase  (				self   :			Tuple     )      ->      None:
    __lowerCamelCase : Optional[Any]					 =				self.heap_size
    for j in range(size - 1    ,			0    ,			-1     ):
      __lowerCamelCase ,__lowerCamelCase : List[str]					 =				self.h[j], self.h[0]
      self.heap_size -= 1
      self.max_heapify(0     )
    __lowerCamelCase : Tuple					 =				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}''')

      _UpperCamelCase							       =		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''')






