


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:
 lowerCamelCase						 =	lowerCamelCase						 =	lowerCamelCase						 =	lowerCamelCase						 =	None




class A  (       unittest.TestCase					):






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




             _lowerCamelCase	:		List[str]          =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	:		Dict          =codebook_size if codebook_size is not None else {'height': 112, 'width': 112}
             _lowerCamelCase	:		List[str]          =codebook_crop_size if codebook_crop_size is not None else {'height': 112, 'width': 112}

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

             _lowerCamelCase	:		List[str]          =input_size_patches
             _lowerCamelCase	:		Any          =total_mask_patches
             _lowerCamelCase	:		str          =mask_group_max_patches
             _lowerCamelCase	:		Optional[Any]          =mask_group_min_patches
             _lowerCamelCase	:		Any          =mask_group_min_aspect_ratio
             _lowerCamelCase	:		List[str]          =mask_group_max_aspect_ratio

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






      def 	lowerCamelCase	( self						:					Dict			) ->						Tuple:
             """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 	lowerCamelCase	( self						:					List[str]			) ->						Optional[int]:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					int			) ->						List[Any]:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					List[str]			) ->						List[str]:
             """simple docstring"""




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




@require_torch
@require_vision
class A  (       UpperCamelCase_   ,							unittest.TestCase					):
      UpperCamelCase__							:  Union[str, Any]				=FlavaImageProcessor if is_vision_available() else None
      UpperCamelCase__							:  Optional[int]				=None






      def 	lowerCamelCase	( self						:					Dict			) ->						int:
             """simple docstring"""




             _lowerCamelCase	:		List[Any]          =FlavaImageProcessingTester(self			)






      @property
      def 	lowerCamelCase	( self						:					Optional[int]			) ->						List[str]:
             """simple docstring"""




             return self.image_processor_tester.prepare_image_processor_dict()






      def 	lowerCamelCase	( self						:					int			) ->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase	:		Any          =self.image_processing_class(**self.image_processor_dict			)
             self.assertTrue(hasattr(lowercase_      ,       'image_mean'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'image_std'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_normalize'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_resize'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'resample'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'crop_size'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_center_crop'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_rescale'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'rescale_factor'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'masking_generator'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'codebook_do_resize'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'codebook_size'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'codebook_resample'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'codebook_do_center_crop'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'codebook_crop_size'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'codebook_do_map_pixels'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'codebook_do_normalize'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'codebook_image_mean'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'codebook_image_std'			)			)






      def 	lowerCamelCase	( self						:					int			) ->						Dict:
             """simple docstring"""




             _lowerCamelCase	:		List[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	:		Optional[Any]          =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 	lowerCamelCase	( self						:					Tuple			) ->						int:
             """simple docstring"""




             pass






      def 	lowerCamelCase	( self						:					List[str]			) ->						Any:
             """simple docstring"""




             _lowerCamelCase	:		Any          =self.image_processing_class(**self.image_processor_dict			)
             # create random PIL images
             _lowerCamelCase	:		int          =prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=lowercase_			)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_      ,       PIL.Image.Image			)

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

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

             _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)      ,       )

             # Test batched
             _lowerCamelCase	:		List[Any]          =image_processing(lowercase_      ,       return_tensors='pt'			)
             _lowerCamelCase     ,					_lowerCamelCase	:		Optional[Any]          =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 	lowerCamelCase	( self						:					Optional[Any]      ,       lowercase_						:					Any      ,       lowercase_						:					str			) ->						List[Any]:
             """simple docstring"""




             _lowerCamelCase	:		List[Any]          =self.image_processing_class(**self.image_processor_dict			)
             # create random tensors
             _lowerCamelCase	:		Tuple          =prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=lowercase_      ,       **lowercase_			)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_      ,       lowercase_			)

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

             _lowerCamelCase     ,					_lowerCamelCase	:		int          =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	:		Tuple          =image_processing(lowercase_      ,       return_image_mask=lowercase_      ,       return_tensors='pt'			)

             _lowerCamelCase     ,					_lowerCamelCase	:		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	:		Optional[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	:		Optional[int]          =image_processing(lowercase_      ,       return_tensors='pt'			).pixel_values

             _lowerCamelCase     ,					_lowerCamelCase	:		Optional[int]          =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(lowercase_      ,       return_image_mask=lowercase_      ,       return_tensors='pt'			)

             _lowerCamelCase     ,					_lowerCamelCase	:		Optional[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	:		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,
                 )      ,       )






      def 	lowerCamelCase	( self						:					str			) ->						List[str]:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					Dict			) ->						int:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					str			) ->						List[Any]:
             """simple docstring"""




             random.seed(1234			)
             _lowerCamelCase	:		Dict          =self.image_processing_class(**self.image_processor_dict			)
             _lowerCamelCase	:		int          =prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=lowercase_      ,       torchify=lowercase_			)

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






      def 	lowerCamelCase	( self						:					Dict			) ->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase	:		int          =self.image_processing_class(**self.image_processor_dict			)
             # create random PIL images
             _lowerCamelCase	:		Tuple          =prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=lowercase_			)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_      ,       PIL.Image.Image			)

             # Test not batched input
             _lowerCamelCase	:		Tuple          =image_processing(image_inputs[0]      ,       return_codebook_pixels=lowercase_      ,       return_tensors='pt'			)
             _lowerCamelCase     ,					_lowerCamelCase	:		str          =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	:		Dict          =image_processing(lowercase_      ,       return_codebook_pixels=lowercase_      ,       return_tensors='pt'			)
             _lowerCamelCase     ,					_lowerCamelCase	:		Optional[Any]          =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 A  (       UpperCamelCase_					):






      @abstractmethod
      def __lt__( self						:					T      ,       lowercase_						:					T			) ->						bool:
             """simple docstring"""




             pass






      @abstractmethod
      def __gt__( self						:					T      ,       lowercase_						:					T			) ->						bool:
             """simple docstring"""




             pass






      @abstractmethod
      def __eq__( self						:					T      ,       lowercase_						:					object			) ->						bool:
             """simple docstring"""




             pass




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




class A  (       Generic[T]					):






      def __init__( self						:					Optional[Any]			) ->						None:
             """simple docstring"""




             _lowerCamelCase	:		list[T]          =[]
             _lowerCamelCase	:		int          =0






      def __repr__( self						:					int			) ->						str:
             """simple docstring"""




             return str(self.h			)






      def 	lowerCamelCase	( self						:					str      ,       lowercase_						:					int			) ->						int | None:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					Union[str, Any]      ,       lowercase_						:					int			) ->						int | None:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					List[str]      ,       lowercase_						:					int			) ->						int | None:
             """simple docstring"""




             _lowerCamelCase	:		Optional[int]          =2 * parent_idx + 2
             if right_child_index < self.heap_size:
                    return right_child_index
             return None






      def 	lowerCamelCase	( self						:					Any      ,       lowercase_						:					int			) ->						None:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					int      ,       lowercase_						:					Iterable[T]			) ->						None:
             """simple docstring"""




             _lowerCamelCase	:		List[Any]          =list(lowercase_			)
             _lowerCamelCase	:		Dict          =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(lowercase_			)






      def 	lowerCamelCase	( self						:					Dict			) ->						T:
             """simple docstring"""




             if self.heap_size >= 2:
                    _lowerCamelCase	:		Optional[int]          =self.h[0]
                    _lowerCamelCase	:		Optional[int]          =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 	lowerCamelCase	( self						:					Tuple      ,       lowercase_						:					T			) ->						None:
             """simple docstring"""




             self.h.append(lowercase_			)
             _lowerCamelCase	:		List[Any]          =(self.heap_size - 1) // 2
             self.heap_size += 1
             while idx >= 0:
                    self.max_heapify(lowercase_			)
                    _lowerCamelCase	:		Tuple          =(idx - 1) // 2






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						None:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =self.heap_size
             for j in range(size - 1      ,       0      ,       -1			):
                    _lowerCamelCase     ,					_lowerCamelCase	:		Optional[Any]          =self.h[j], self.h[0]
                    self.heap_size -= 1
                    self.max_heapify(0			)
             _lowerCamelCase	:		Union[str, Any]          =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}""")

  lowerCamelCase						 =	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""")


