"""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:
       lowerCamelCase      : Dict       =None






class 							__snake_case(					unittest.TestCase	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase					,       __lowerCamelCase=7					,       __lowerCamelCase=3					,       __lowerCamelCase=30					,       __lowerCamelCase=400					,       __lowerCamelCase=True					,       __lowerCamelCase=None					,       __lowerCamelCase=True					,       __lowerCamelCase=None					,       __lowerCamelCase=None					,       __lowerCamelCase=True					,       __lowerCamelCase=1 / 255					,       __lowerCamelCase=True					,       __lowerCamelCase=FLAVA_IMAGE_MEAN					,       __lowerCamelCase=FLAVA_IMAGE_STD					,       __lowerCamelCase=14					,       __lowerCamelCase=75					,       __lowerCamelCase=None					,       __lowerCamelCase=16					,       __lowerCamelCase=0.3					,       __lowerCamelCase=None					,       __lowerCamelCase=True					,       __lowerCamelCase=None					,       __lowerCamelCase=None					,       __lowerCamelCase=True					,       __lowerCamelCase=None					,       __lowerCamelCase=True					,       __lowerCamelCase=True					,       __lowerCamelCase=FLAVA_CODEBOOK_MEAN					,       __lowerCamelCase=FLAVA_CODEBOOK_STD					,       ):




         '''simple docstring'''



         __A					:					List[Any]								=		size if size is not None else {'height': 224, 'width': 224}
         __A					:					List[str]								=		crop_size if crop_size is not None else {'height': 224, 'width': 224}
         __A					:					Any								=		codebook_size if codebook_size is not None else {'height': 112, 'width': 112}
         __A					:					Tuple								=		codebook_crop_size if codebook_crop_size is not None else {'height': 112, 'width': 112}

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

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

         __A					:					str								=		codebook_do_resize
         __A					:					Optional[Any]								=		codebook_size
         __A					:					str								=		codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
         __A					:					List[str]								=		codebook_do_center_crop
         __A					:					Optional[int]								=		codebook_crop_size
         __A					:					Optional[Any]								=		codebook_do_map_pixels
         __A					:					Tuple								=		codebook_do_normalize
         __A					:					Any								=		codebook_image_mean
         __A					:					str								=		codebook_image_std
    def 						_a							(  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,
             "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 						_a							(  self	):




         '''simple docstring'''



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




         '''simple docstring'''



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

    def 						_a							(  self	):




         '''simple docstring'''



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







@require_torch
@require_vision
class 							__snake_case(					A_				,	unittest.TestCase	):


    '''simple docstring'''





    _UpperCAmelCase       =			FlavaImageProcessor if is_vision_available() else None
    _UpperCAmelCase       =			None
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Optional[int]								=		FlavaImageProcessingTester(self	)
    @property
    def 						_a							(  self	):




         '''simple docstring'''



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




         '''simple docstring'''



         __A					:					List[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					,       'resample'	)	)
         self.assertTrue(hasattr(__lowerCamelCase					,       'crop_size'	)	)
         self.assertTrue(hasattr(__lowerCamelCase					,       'do_center_crop'	)	)
         self.assertTrue(hasattr(__lowerCamelCase					,       'do_rescale'	)	)
         self.assertTrue(hasattr(__lowerCamelCase					,       'rescale_factor'	)	)
         self.assertTrue(hasattr(__lowerCamelCase					,       'masking_generator'	)	)
         self.assertTrue(hasattr(__lowerCamelCase					,       'codebook_do_resize'	)	)
         self.assertTrue(hasattr(__lowerCamelCase					,       'codebook_size'	)	)
         self.assertTrue(hasattr(__lowerCamelCase					,       'codebook_resample'	)	)
         self.assertTrue(hasattr(__lowerCamelCase					,       'codebook_do_center_crop'	)	)
         self.assertTrue(hasattr(__lowerCamelCase					,       'codebook_crop_size'	)	)
         self.assertTrue(hasattr(__lowerCamelCase					,       'codebook_do_map_pixels'	)	)
         self.assertTrue(hasattr(__lowerCamelCase					,       'codebook_do_normalize'	)	)
         self.assertTrue(hasattr(__lowerCamelCase					,       'codebook_image_mean'	)	)
         self.assertTrue(hasattr(__lowerCamelCase					,       'codebook_image_std'	)	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Optional[int]								=		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}	)

         __A					:					List[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 						_a							(  self	):




         '''simple docstring'''



         pass
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Dict								=		self.image_processing_class(**self.image_processor_dict	)
         # create random PIL images
         __A					:					List[Any]								=		prepare_image_inputs(self.image_processor_tester					,       equal_resolution=__lowerCamelCase	)
         for image in image_inputs:
              self.assertIsInstance(__lowerCamelCase					,       PIL.Image.Image	)

         # Test not batched input
         __A					:					Union[str, Any]								=		image_processing(image_inputs[0]					,       return_tensors='pt'	)

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

         __A						,		__A					:					Tuple								=		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
         __A					:					Dict								=		image_processing(__lowerCamelCase					,       return_tensors='pt'	)
         __A						,		__A					:					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 						_a							(  self					,       __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					Dict								=		self.image_processing_class(**self.image_processor_dict	)
         # create random tensors
         __A					:					Optional[Any]								=		prepare_image_inputs(self.image_processor_tester					,       equal_resolution=__lowerCamelCase					,       **__lowerCamelCase	)
         for image in image_inputs:
              self.assertIsInstance(__lowerCamelCase					,       __lowerCamelCase	)

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

         __A						,		__A					:					List[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)					,       )

         __A					:					Union[str, Any]								=		image_processing(__lowerCamelCase					,       return_image_mask=__lowerCamelCase					,       return_tensors='pt'	)

         __A						,		__A					:					Tuple								=		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,
             )					,       )

         __A						,		__A					:					Optional[int]								=		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
         __A					:					Optional[int]								=		image_processing(__lowerCamelCase					,       return_tensors='pt'	).pixel_values

         __A						,		__A					:					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
         __A					:					Optional[int]								=		image_processing(__lowerCamelCase					,       return_image_mask=__lowerCamelCase					,       return_tensors='pt'	)

         __A						,		__A					:					Optional[int]								=		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,
             )					,       )

         __A						,		__A					:					Dict								=		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 						_a							(  self	):




         '''simple docstring'''



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




         '''simple docstring'''



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




         '''simple docstring'''



         random.seed(1234	)
         __A					:					List[str]								=		self.image_processing_class(**self.image_processor_dict	)
         __A					:					str								=		prepare_image_inputs(self.image_processor_tester					,       equal_resolution=__lowerCamelCase					,       torchify=__lowerCamelCase	)

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

    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					List[Any]								=		self.image_processing_class(**self.image_processor_dict	)
         # create random PIL images
         __A					:					List[Any]								=		prepare_image_inputs(self.image_processor_tester					,       equal_resolution=__lowerCamelCase	)
         for image in image_inputs:
              self.assertIsInstance(__lowerCamelCase					,       PIL.Image.Image	)

         # Test not batched input
         __A					:					str								=		image_processing(image_inputs[0]					,       return_codebook_pixels=__lowerCamelCase					,       return_tensors='pt'	)
         __A						,		__A					:					List[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
         __A					:					Tuple								=		image_processing(__lowerCamelCase					,       return_codebook_pixels=__lowerCamelCase					,       return_tensors='pt'	)
         __A						,		__A					:					Union[str, 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,
             )					,       )




"""simple docstring"""







from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

lowerCamelCase      : List[str]       =TypeVar('''T''', bound=bool)






class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					T								=		value
         __A					:					RandomizedHeapNode[T] | None								=		None
         __A					:					RandomizedHeapNode[T] | None								=		None
    @property
    def 						_a							(  self	):




         '''simple docstring'''



         return self._value

    @staticmethod
    def 						_a							(  __lowerCamelCase					,       __lowerCamelCase	):




         '''simple docstring'''



         if not roota:
              return roota

         if not roota:
              return roota

         if roota.value > roota.value:
              __A						,		__A					:					List[str]								=		roota, roota

         if random.choice([True, False]	):
              __A						,		__A					:					Dict								=		roota.right, roota.left

         __A					:					Dict								=		RandomizedHeapNode.merge(roota.left					,       __lowerCamelCase	)

         return roota







class 							__snake_case(					Generic[T]	):


    '''simple docstring'''





    def __init__(  self					,       __lowerCamelCase = ()	):




         '''simple docstring'''



         __A					:					RandomizedHeapNode[T] | None								=		None

         if data:
              for item in data:
                   self.insert(__lowerCamelCase	)
    def 						_a							(  self					,       __lowerCamelCase	):




         '''simple docstring'''



         __A					:					List[str]								=		RandomizedHeapNode.merge(self._root					,       RandomizedHeapNode(__lowerCamelCase	)	)
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					int								=		self.top()

         if self._root is None:
              return None

         __A					:					Dict								=		RandomizedHeapNode.merge(self._root.left					,       self._root.right	)

         return result
    def 						_a							(  self	):




         '''simple docstring'''



         if not self._root:
              raise IndexError('Can\'t get top element for the empty heap.'	)
         return self._root.value
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Dict								=		None
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Tuple								=		[]
         while self:
              result.append(self.pop()	)

         return result

    def __bool__(  self	):




         '''simple docstring'''



         return self._root is not None


if __name__ == "__main__":
       import doctest

       doctest.testmod()



