



"""simple docstring"""






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():
		from PIL import Image

		from transformers import PoolFormerImageProcessor

class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					def __init__( self							:						int,						lowerCamelCase							:						Optional[Any],						lowerCamelCase							:						List[str]=7,						lowerCamelCase							:						Optional[Any]=3,						lowerCamelCase							:						Any=30,						lowerCamelCase							:						Dict=400,						lowerCamelCase							:						Optional[int]=True,						lowerCamelCase							:						int=None,						lowerCamelCase							:						int=0.9,						lowerCamelCase							:						Optional[int]=None,						lowerCamelCase							:						List[Any]=True,						lowerCamelCase							:						Union[str, Any]=[0.5, 0.5, 0.5],						lowerCamelCase							:						Union[str, Any]=[0.5, 0.5, 0.5],						)->      Any:
								lowerCamelCase__	:	str				=size if size is not None else {'''shortest_edge''': 30}
								lowerCamelCase__	:	List[str]				=crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
								lowerCamelCase__	:	List[Any]				=parent
								lowerCamelCase__	:	Any				=batch_size
								lowerCamelCase__	:	Union[str, Any]				=num_channels
								lowerCamelCase__	:	str				=min_resolution
								lowerCamelCase__	:	int				=max_resolution
								lowerCamelCase__	:	Any				=do_resize_and_center_crop
								lowerCamelCase__	:	Any				=size
								lowerCamelCase__	:	Any				=crop_pct
								lowerCamelCase__	:	Optional[Any]				=crop_size
								lowerCamelCase__	:	int				=do_normalize
								lowerCamelCase__	:	Union[str, Any]				=image_mean
								lowerCamelCase__	:	int				=image_std




					def 	snake_case ( self							:						Optional[Any]  )->      Any:
								return {
								    "size": self.size,
								    "do_resize_and_center_crop": self.do_resize_and_center_crop,
								    "crop_pct": self.crop_pct,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								}





@require_torch
@require_vision
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							PoolFormerImageProcessor if is_vision_available() else None



					def 	snake_case ( self							:						List[str]  )->      Dict:
								lowerCamelCase__	:	Optional[int]				=PoolFormerImageProcessingTester(self  )



					@property
					def 	snake_case ( self							:						Optional[Any]  )->      Any:
								return self.image_processor_tester.prepare_image_processor_dict()



					def 	snake_case ( self							:						Union[str, Any]  )->      Union[str, Any]:
								lowerCamelCase__	:	int				=self.image_processing_class(**self.image_processor_dict  )
								self.assertTrue(hasattr(lowerCamelCase,						'''do_resize_and_center_crop'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,						'''size'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,						'''crop_pct'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,						'''do_normalize'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,						'''image_mean'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,						'''image_std'''  )  )



					def 	snake_case ( self							:						Optional[int]  )->      str:
								lowerCamelCase__	:	Optional[int]				=self.image_processing_class.from_dict(self.image_processor_dict  )
								self.assertEqual(image_processor.size,						{'''shortest_edge''': 30}  )
								self.assertEqual(image_processor.crop_size,						{'''height''': 30, '''width''': 30}  )

								lowerCamelCase__	:	str				=self.image_processing_class.from_dict(self.image_processor_dict,						size=42,						crop_size=84  )
								self.assertEqual(image_processor.size,						{'''shortest_edge''': 42}  )
								self.assertEqual(image_processor.crop_size,						{'''height''': 84, '''width''': 84}  )



					def 	snake_case ( self							:						Tuple  )->      List[str]:
								pass



					def 	snake_case ( self							:						Dict  )->      Tuple:
								# Initialize image_processing
								lowerCamelCase__	:	int				=self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								lowerCamelCase__	:	Optional[Any]				=prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase  )
								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,						Image.Image  )

								# Test not batched input
								lowerCamelCase__	:	Optional[Any]				=image_processing(image_inputs[0],						return_tensors='''pt'''  ).pixel_values

								self.assertEqual(
								    encoded_images.shape,						(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)

								# Test batched
								lowerCamelCase__	:	Any				=image_processing(lowerCamelCase,						return_tensors='''pt'''  ).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)



					def 	snake_case ( self							:						List[str]  )->      Dict:
								# Initialize image_processing
								lowerCamelCase__	:	str				=self.image_processing_class(**self.image_processor_dict  )
								# create random numpy tensors
								lowerCamelCase__	:	Optional[int]				=prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase,						numpify=lowerCamelCase  )
								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,						np.ndarray  )

								# Test not batched input
								lowerCamelCase__	:	Dict				=image_processing(image_inputs[0],						return_tensors='''pt'''  ).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)

								# Test batched
								lowerCamelCase__	:	List[str]				=image_processing(lowerCamelCase,						return_tensors='''pt'''  ).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)




					def 	snake_case ( self							:						Optional[Any]  )->      List[Any]:
								# Initialize image_processing
								lowerCamelCase__	:	int				=self.image_processing_class(**self.image_processor_dict  )
								# create random PyTorch tensors
								lowerCamelCase__	:	List[Any]				=prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase,						torchify=lowerCamelCase  )
								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,						torch.Tensor  )

								# Test not batched input
								lowerCamelCase__	:	Optional[int]				=image_processing(image_inputs[0],						return_tensors='''pt'''  ).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)

								# Test batched
								lowerCamelCase__	:	Optional[int]				=image_processing(lowerCamelCase,						return_tensors='''pt'''  ).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					list							,	__lowerCamelCase       :					list							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					int							,	__lowerCamelCase       :					int			):
			"""simple docstring"""
			if index == number_of_items:
						return 0
			lowerCamelCase__	:	Optional[int]				=0
			lowerCamelCase__	:	Union[str, Any]				=0
			lowerCamelCase__	:	List[str]				=knapsack(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	index + 1			)
			if weights[index] <= max_weight:
						lowerCamelCase__	:	Dict				=values[index] + knapsack(
						    __lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	max_weight - weights[index]							,	index + 1			)
			return max(__lowerCamelCase							,	__lowerCamelCase			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




