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 _a       (	unittest.TestCase ):

	'''simple docstring'''



	def __init__(       self							,			__UpperCAmelCase							,			__UpperCAmelCase=7							,			__UpperCAmelCase=3							,			__UpperCAmelCase=30							,			__UpperCAmelCase=400							,			__UpperCAmelCase=True							,			__UpperCAmelCase=None							,			__UpperCAmelCase=True							,			__UpperCAmelCase=None							,			__UpperCAmelCase=None							,			__UpperCAmelCase=True							,			__UpperCAmelCase=1 / 255							,			__UpperCAmelCase=True							,			__UpperCAmelCase=FLAVA_IMAGE_MEAN							,			__UpperCAmelCase=FLAVA_IMAGE_STD							,			__UpperCAmelCase=14							,			__UpperCAmelCase=75							,			__UpperCAmelCase=None							,			__UpperCAmelCase=16							,			__UpperCAmelCase=0.3							,			__UpperCAmelCase=None							,			__UpperCAmelCase=True							,			__UpperCAmelCase=None							,			__UpperCAmelCase=None							,			__UpperCAmelCase=True							,			__UpperCAmelCase=None							,			__UpperCAmelCase=True							,			__UpperCAmelCase=True							,			__UpperCAmelCase=FLAVA_CODEBOOK_MEAN							,			__UpperCAmelCase=FLAVA_CODEBOOK_STD							,			):
			__A							:   int	  =				size if size is not None else {"height": 224, "width": 224}
			__A							:   Any	  =				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							:   Optional[Any]	  =				codebook_crop_size if codebook_crop_size is not None else {"height": 112, "width": 112}

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

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

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

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

	def 					__UpperCAmelCase(       self   ):
			return (
			    (self.input_size_patches, self.input_size_patches)
			    if not isinstance(self.input_size_patches							,			__UpperCAmelCase   )
			    else self.input_size_patches
			)







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


@require_torch
@require_vision
class _a       (	lowerCAmelCase__   ,							unittest.TestCase ):

	'''simple docstring'''



	lowerCamelCase_   :      Dict       =	FlavaImageProcessor if is_vision_available() else None
	lowerCamelCase_   :      Optional[Any]       =	None

	def 					__UpperCAmelCase(       self   ):
			__A							:   int	  =				FlavaImageProcessingTester(self   )

	@property
	def 					__UpperCAmelCase(       self   ):
			return self.image_processor_tester.prepare_image_processor_dict()

	def 					__UpperCAmelCase(       self   ):
			__A							:   Tuple	  =				self.image_processing_class(**self.image_processor_dict   )
			self.assertTrue(hasattr(__UpperCAmelCase							,			"image_mean"   )   )
			self.assertTrue(hasattr(__UpperCAmelCase							,			"image_std"   )   )
			self.assertTrue(hasattr(__UpperCAmelCase							,			"do_normalize"   )   )
			self.assertTrue(hasattr(__UpperCAmelCase							,			"do_resize"   )   )
			self.assertTrue(hasattr(__UpperCAmelCase							,			"resample"   )   )
			self.assertTrue(hasattr(__UpperCAmelCase							,			"crop_size"   )   )
			self.assertTrue(hasattr(__UpperCAmelCase							,			"do_center_crop"   )   )
			self.assertTrue(hasattr(__UpperCAmelCase							,			"do_rescale"   )   )
			self.assertTrue(hasattr(__UpperCAmelCase							,			"rescale_factor"   )   )
			self.assertTrue(hasattr(__UpperCAmelCase							,			"masking_generator"   )   )
			self.assertTrue(hasattr(__UpperCAmelCase							,			"codebook_do_resize"   )   )
			self.assertTrue(hasattr(__UpperCAmelCase							,			"codebook_size"   )   )
			self.assertTrue(hasattr(__UpperCAmelCase							,			"codebook_resample"   )   )
			self.assertTrue(hasattr(__UpperCAmelCase							,			"codebook_do_center_crop"   )   )
			self.assertTrue(hasattr(__UpperCAmelCase							,			"codebook_crop_size"   )   )
			self.assertTrue(hasattr(__UpperCAmelCase							,			"codebook_do_map_pixels"   )   )
			self.assertTrue(hasattr(__UpperCAmelCase							,			"codebook_do_normalize"   )   )
			self.assertTrue(hasattr(__UpperCAmelCase							,			"codebook_image_mean"   )   )
			self.assertTrue(hasattr(__UpperCAmelCase							,			"codebook_image_std"   )   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   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}   )

			__A							:   Dict	  =				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   ):
			pass

	def 					__UpperCAmelCase(       self   ):
			# Initialize image_processing
			__A							:   int	  =				self.image_processing_class(**self.image_processor_dict   )
			# create random PIL images
			__A							:   Tuple	  =				prepare_image_inputs(self.image_processor_tester							,			equal_resolution=__UpperCAmelCase   )
			for image in image_inputs:
					self.assertIsInstance(__UpperCAmelCase							,			PIL.Image.Image   )

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

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

			__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							:   List[Any]	  =				image_processing(__UpperCAmelCase							,			return_tensors="pt"   )
			__A							:   List[str]	  =				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							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			# Initialize image_processing
			__A							:   Tuple	  =				self.image_processing_class(**self.image_processor_dict   )
			# create random tensors
			__A							:   Union[str, Any]	  =				prepare_image_inputs(self.image_processor_tester							,			equal_resolution=__UpperCAmelCase							,			**__UpperCAmelCase   )
			for image in image_inputs:
					self.assertIsInstance(__UpperCAmelCase							,			__UpperCAmelCase   )

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

			__A							:   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)							,			)

			__A							:   Optional[Any]	  =				image_processing(__UpperCAmelCase							,			return_image_mask=__UpperCAmelCase							,			return_tensors="pt"   )

			__A							:   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							:   List[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
			__A							:   int	  =				image_processing(__UpperCAmelCase							,			return_tensors="pt"   ).pixel_values

			__A							:   Union[str, Any]	  =				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							:   int	  =				image_processing(__UpperCAmelCase							,			return_image_mask=__UpperCAmelCase							,			return_tensors="pt"   )

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

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

	def 					__UpperCAmelCase(       self   ):
			# Initialize image_processing
			random.seed(1_234   )
			__A							:   Dict	  =				self.image_processing_class(**self.image_processor_dict   )
			__A							:   Tuple	  =				prepare_image_inputs(self.image_processor_tester							,			equal_resolution=__UpperCAmelCase							,			torchify=__UpperCAmelCase   )

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







	def 					__UpperCAmelCase(       self   ):
			# Initialize image_processing
			__A							:   Dict	  =				self.image_processing_class(**self.image_processor_dict   )
			# create random PIL images
			__A							:   Dict	  =				prepare_image_inputs(self.image_processor_tester							,			equal_resolution=__UpperCAmelCase   )
			for image in image_inputs:
					self.assertIsInstance(__UpperCAmelCase							,			PIL.Image.Image   )

			# Test not batched input
			__A							:   List[Any]	  =				image_processing(image_inputs[0]							,			return_codebook_pixels=__UpperCAmelCase							,			return_tensors="pt"   )
			__A							:   Optional[int]	  =				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							:   Union[str, Any]	  =				image_processing(__UpperCAmelCase							,			return_codebook_pixels=__UpperCAmelCase							,			return_tensors="pt"   )
			__A							:   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,
			    )							,			)






def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase    )							->	Tuple:
		__A							:   Union[str, Any]	  =				index
		__A							:   int	  =				2 * index + 1
		__A							:   Dict	  =				2 * index + 2
		if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
				__A							:   Dict	  =				left_index

		if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
				__A							:   int	  =				right_index

		if largest != index:
				__A  ,       __A							:   Dict	  =				unsorted[index], unsorted[largest]
				heapify(_lowercase ,     _lowercase ,     _lowercase    )



def    lowerCamelCase_   (  _lowercase    )							->	Tuple:
		__A							:   Dict	  =				len(_lowercase    )
		for i in range(n // 2 - 1 ,     -1 ,     -1    ):
				heapify(_lowercase ,     _lowercase ,     _lowercase    )
		for i in range(n - 1 ,     0 ,     -1    ):
				__A  ,       __A							:   Dict	  =				unsorted[i], unsorted[0]
				heapify(_lowercase ,     0 ,     _lowercase    )
		return unsorted


if __name__ == "__main__":
					UpperCamelCase      			=     input('Enter numbers separated by a comma:\n').strip()
					UpperCamelCase      			=     [int(item) for item in user_input.split(',')]
					print(heap_sort(unsorted))





