


'''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:
						__magic_name__   :   List[Any]  							=None






class 		UpperCamelCase_		(					unittest.TestCase   ):





						"""simple docstring"""




						def __init__( self				:					Any	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					str=7	,				_lowerCamelCase				:					str=3	,				_lowerCamelCase				:					Tuple=30	,				_lowerCamelCase				:					List[str]=4_00	,				_lowerCamelCase				:					List[str]=True	,				_lowerCamelCase				:					Optional[Any]=None	,				_lowerCamelCase				:					Tuple=True	,				_lowerCamelCase				:					Tuple=None	,				_lowerCamelCase				:					Any=None	,				_lowerCamelCase				:					int=True	,				_lowerCamelCase				:					List[str]=1 / 2_55	,				_lowerCamelCase				:					List[Any]=True	,				_lowerCamelCase				:					Optional[int]=FLAVA_IMAGE_MEAN	,				_lowerCamelCase				:					Tuple=FLAVA_IMAGE_STD	,				_lowerCamelCase				:					List[Any]=14	,				_lowerCamelCase				:					List[str]=75	,				_lowerCamelCase				:					Tuple=None	,				_lowerCamelCase				:					Optional[Any]=16	,				_lowerCamelCase				:					Optional[Any]=0.3	,				_lowerCamelCase				:					Optional[Any]=None	,				_lowerCamelCase				:					Tuple=True	,				_lowerCamelCase				:					Dict=None	,				_lowerCamelCase				:					Optional[int]=None	,				_lowerCamelCase				:					Optional[Any]=True	,				_lowerCamelCase				:					List[Any]=None	,				_lowerCamelCase				:					Optional[Any]=True	,				_lowerCamelCase				:					Any=True	,				_lowerCamelCase				:					Any=FLAVA_CODEBOOK_MEAN	,				_lowerCamelCase				:					str=FLAVA_CODEBOOK_STD	,				)							->					int:
										__magic_name__	  = size if size is not None else {"height": 2_24, "width": 2_24}
										__magic_name__	  = crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
										__magic_name__	  = codebook_size if codebook_size is not None else {"height": 1_12, "width": 1_12}
										__magic_name__	  = codebook_crop_size if codebook_crop_size is not None else {"height": 1_12, "width": 1_12}

										__magic_name__	  = parent
										__magic_name__	  = batch_size
										__magic_name__	  = num_channels
										__magic_name__	  = do_resize
										__magic_name__	  = do_rescale
										__magic_name__	  = rescale_factor
										__magic_name__	  = min_resolution
										__magic_name__	  = max_resolution
										__magic_name__	  = size
										__magic_name__	  = resample if resample is not None else PILImageResampling.BICUBIC
										__magic_name__	  = do_normalize
										__magic_name__	  = image_mean
										__magic_name__	  = image_std
										__magic_name__	  = do_center_crop
										__magic_name__	  = crop_size

										__magic_name__	  = input_size_patches
										__magic_name__	  = total_mask_patches
										__magic_name__	  = mask_group_max_patches
										__magic_name__	  = mask_group_min_patches
										__magic_name__	  = mask_group_min_aspect_ratio
										__magic_name__	  = mask_group_max_aspect_ratio

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






@require_torch
@require_vision
class 		UpperCamelCase_		(					A   ,		unittest.TestCase   ):





						"""simple docstring"""




						UpperCAmelCase__	:       int         =     FlavaImageProcessor if is_vision_available() else None
						UpperCAmelCase__	:       Any         =     None
						def 					__A					( self				:					Union[str, Any]  )							->					int:
										__magic_name__	  = FlavaImageProcessingTester(self  )
						@property
						def 					__A					( self				:					Optional[int]  )							->					str:
										return self.image_processor_tester.prepare_image_processor_dict()
						def 					__A					( self				:					Union[str, Any]  )							->					Any:
										__magic_name__	  = 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				:					int  )							->					List[str]:
										__magic_name__	  = self.image_processing_class.from_dict(self.image_processor_dict  )
										self.assertEqual(image_processor.size	,				{"height": 2_24, "width": 2_24}  )
										self.assertEqual(image_processor.crop_size	,				{"height": 2_24, "width": 2_24}  )
										self.assertEqual(image_processor.codebook_size	,				{"height": 1_12, "width": 1_12}  )
										self.assertEqual(image_processor.codebook_crop_size	,				{"height": 1_12, "width": 1_12}  )

										__magic_name__	  = 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				:					List[Any]  )							->					int:
										pass
						def 					__A					( self				:					Optional[Any]  )							->					Any:
										# Initialize image_processing
										__magic_name__	  = self.image_processing_class(**self.image_processor_dict  )
										# create random PIL images
										__magic_name__	  = 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
										__magic_name__	  = image_processing(image_inputs[0]	,				return_tensors="pt"  )

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

										__magic_name__  ,      __magic_name__	  = 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
										__magic_name__	  = image_processing(_lowerCamelCase	,				return_tensors="pt"  )
										__magic_name__  ,      __magic_name__	  = 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				:					List[Any]	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					List[str]  )							->					Optional[int]:
										# Initialize image_processing
										__magic_name__	  = self.image_processing_class(**self.image_processor_dict  )
										# create random tensors
										__magic_name__	  = prepare_image_inputs(self.image_processor_tester	,				equal_resolution=_lowerCamelCase	,				**_lowerCamelCase  )
										for image in image_inputs:
														self.assertIsInstance(_lowerCamelCase	,				_lowerCamelCase  )

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

										__magic_name__  ,      __magic_name__	  = 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)	,				)

										__magic_name__	  = image_processing(_lowerCamelCase	,				return_image_mask=_lowerCamelCase	,				return_tensors="pt"  )

										__magic_name__  ,      __magic_name__	  = 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,
										    )	,				)

										__magic_name__  ,      __magic_name__	  = 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
										__magic_name__	  = image_processing(_lowerCamelCase	,				return_tensors="pt"  ).pixel_values

										__magic_name__  ,      __magic_name__	  = 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
										__magic_name__	  = image_processing(_lowerCamelCase	,				return_image_mask=_lowerCamelCase	,				return_tensors="pt"  )

										__magic_name__  ,      __magic_name__	  = 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,
										    )	,				)

										__magic_name__  ,      __magic_name__	  = 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				:					int  )							->					str:
										self._test_call_framework(np.ndarray	,				prepare_kwargs={"numpify": True}  )
						def 					__A					( self				:					Any  )							->					List[Any]:
										self._test_call_framework(torch.Tensor	,				prepare_kwargs={"torchify": True}  )
						def 					__A					( self				:					Dict  )							->					Any:
										# Initialize image_processing
										random.seed(12_34  )
										__magic_name__	  = self.image_processing_class(**self.image_processor_dict  )
										__magic_name__	  = prepare_image_inputs(self.image_processor_tester	,				equal_resolution=_lowerCamelCase	,				torchify=_lowerCamelCase  )

										# Test not batched input
										__magic_name__	  = 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				:					Any  )							->					Optional[Any]:
										# Initialize image_processing
										__magic_name__	  = self.image_processing_class(**self.image_processor_dict  )
										# create random PIL images
										__magic_name__	  = 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
										__magic_name__	  = image_processing(image_inputs[0]	,				return_codebook_pixels=_lowerCamelCase	,				return_tensors="pt"  )
										__magic_name__  ,      __magic_name__	  = 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
										__magic_name__	  = image_processing(_lowerCamelCase	,				return_codebook_pixels=_lowerCamelCase	,				return_tensors="pt"  )
										__magic_name__  ,      __magic_name__	  = 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




def      __snake_case       (       lowerCamelCase_ :				list[list[int]]	,  lowerCamelCase_ :				list[list[int]]						):
				'''simple docstring'''




				assert len(lowerCamelCase_						) == len(lowerCamelCase_						)

				__magic_name__	  = len(lowerCamelCase_						)
				__magic_name__	  = list(range(lowerCamelCase_						)						)
				__magic_name__	  = [-1] * n  # who the donor has donated to
				__magic_name__	  = [-1] * n  # who the recipient has received from
				__magic_name__	  = [0] * n

				while unmatched_donors:
								__magic_name__	  = unmatched_donors[0]
								__magic_name__	  = donor_pref[donor]
								__magic_name__	  = donor_preference[num_donations[donor]]
								num_donations[donor] += 1
								__magic_name__	  = recipient_pref[recipient]
								__magic_name__	  = rec_record[recipient]

								if prev_donor != -1:
												if rec_preference.index(lowerCamelCase_						) > rec_preference.index(lowerCamelCase_						):
																__magic_name__	  = donor
																__magic_name__	  = recipient
																unmatched_donors.append(lowerCamelCase_						)
																unmatched_donors.remove(lowerCamelCase_						)
								else:
												__magic_name__	  = donor
												__magic_name__	  = recipient
												unmatched_donors.remove(lowerCamelCase_						)
				return donor_record



