





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 	=      None




class 	_UpperCAmelCase						( unittest.TestCase       ):




					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     ,   )    ->	Optional[int]:
							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 						snake_case_		(    self							)    ->	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 						snake_case_		(    self							)    ->	Tuple:
							return (self.size["height"], self.size["width"])




					def 						snake_case_		(    self							)    ->	int:
							return (
							    (self.input_size_patches, self.input_size_patches)
							    if not isinstance(self.input_size_patches     ,   __lowerCamelCase							)
							    else self.input_size_patches
							)




					def 						snake_case_		(    self							)    ->	Tuple:
							return (self.codebook_size["height"], self.codebook_size["width"])




@require_torch
@require_vision
class 	_UpperCAmelCase						( A_			,							unittest.TestCase       ):
					__A        = FlavaImageProcessor if is_vision_available() else None
					__A        = None




					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							UpperCamelCase__								=						FlavaImageProcessingTester(self							)




					@property
					def 						snake_case_		(    self							)    ->	Tuple:
							return self.image_processor_tester.prepare_image_processor_dict()




					def 						snake_case_		(    self							)    ->	Optional[Any]:
							UpperCamelCase__								=						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 						snake_case_		(    self							)    ->	Union[str, Any]:
							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 						snake_case_		(    self							)    ->	Optional[Any]:
							pass




					def 						snake_case_		(    self							)    ->	int:
							UpperCamelCase__								=						self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							UpperCamelCase__								=						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
							UpperCamelCase__								=						image_processing(image_inputs[0]     ,   return_tensors="""pt"""							)

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

							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(__lowerCamelCase     ,   return_tensors="""pt"""							)
							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 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	Any:
							UpperCamelCase__								=						self.image_processing_class(**self.image_processor_dict							)
							# create random tensors
							UpperCamelCase__								=						prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__lowerCamelCase     ,   **__lowerCamelCase							)
							for image in image_inputs:
									self.assertIsInstance(__lowerCamelCase     ,   __lowerCamelCase							)

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

							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(__lowerCamelCase     ,   return_image_mask=__lowerCamelCase     ,   return_tensors="""pt"""							)

							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__								=						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(__lowerCamelCase     ,   return_tensors="""pt"""							).pixel_values

							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(__lowerCamelCase     ,   return_image_mask=__lowerCamelCase     ,   return_tensors="""pt"""							)

							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__								=						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 						snake_case_		(    self							)    ->	Union[str, Any]:
							self._test_call_framework(np.ndarray     ,   prepare_kwargs={"""numpify""": True}							)




					def 						snake_case_		(    self							)    ->	List[Any]:
							self._test_call_framework(torch.Tensor     ,   prepare_kwargs={"""torchify""": True}							)




					def 						snake_case_		(    self							)    ->	str:
							random.seed(1_234							)
							UpperCamelCase__								=						self.image_processing_class(**self.image_processor_dict							)
							UpperCamelCase__								=						prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__lowerCamelCase     ,   torchify=__lowerCamelCase							)

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




					def 						snake_case_		(    self							)    ->	Any:
							UpperCamelCase__								=						self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							UpperCamelCase__								=						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
							UpperCamelCase__								=						image_processing(image_inputs[0]     ,   return_codebook_pixels=__lowerCamelCase     ,   return_tensors="""pt"""							)
							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(__lowerCamelCase     ,   return_codebook_pixels=__lowerCamelCase     ,   return_tensors="""pt"""							)
							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,
							    )     ,   )






"""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()



