


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 						lowerCamelCase_				(      unittest.TestCase ):







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=7       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=3_0       ,					__lowerCAmelCase=4_0_0       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=0.9       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=[0.5, 0.5, 0.5]       ,					__lowerCAmelCase=[0.5, 0.5, 0.5]       ,					):
								"""simple docstring"""
								__magic_name__		:int        =      size if size is not None else {'''shortest_edge''': 3_0}
								__magic_name__		:Optional[Any]        =      crop_size if crop_size is not None else {'''height''': 3_0, '''width''': 3_0}
								__magic_name__		:Union[str, Any]        =      parent
								__magic_name__		:Optional[int]        =      batch_size
								__magic_name__		:Union[str, Any]        =      num_channels
								__magic_name__		:List[str]        =      min_resolution
								__magic_name__		:Optional[Any]        =      max_resolution
								__magic_name__		:List[str]        =      do_resize_and_center_crop
								__magic_name__		:List[str]        =      size
								__magic_name__		:Optional[int]        =      crop_pct
								__magic_name__		:Tuple        =      crop_size
								__magic_name__		:Optional[int]        =      do_normalize
								__magic_name__		:int        =      image_mean
								__magic_name__		:int        =      image_std







				def 			A	(						self     ):
								"""simple docstring"""
								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 						lowerCamelCase_				(      lowerCamelCase			,				unittest.TestCase ):
				a__					       =  PoolFormerImageProcessor if is_vision_available() else None







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      PoolFormerImageProcessingTester(self     )







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return self.image_processor_tester.prepare_image_processor_dict()







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		: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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      self.image_processing_class.from_dict(self.image_processor_dict     )
								self.assertEqual(image_processor.size       ,					{'''shortest_edge''': 3_0}     )
								self.assertEqual(image_processor.crop_size       ,					{'''height''': 3_0, '''width''': 3_0}     )

								__magic_name__		:str        =      self.image_processing_class.from_dict(self.image_processor_dict       ,					size=4_2       ,					crop_size=8_4     )
								self.assertEqual(image_processor.size       ,					{'''shortest_edge''': 4_2}     )
								self.assertEqual(image_processor.crop_size       ,					{'''height''': 8_4, '''width''': 8_4}     )







				def 			A	(						self     ):
								"""simple docstring"""
								pass







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.image_processing_class(**self.image_processor_dict     )
								# create random PIL images
								__magic_name__		:Optional[int]        =      prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=__lowerCAmelCase     )
								for image in image_inputs:
												self.assertIsInstance(__lowerCAmelCase       ,					Image.Image     )

								# Test not batched input
								__magic_name__		: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
								__magic_name__		: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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      self.image_processing_class(**self.image_processor_dict     )
								# create random numpy tensors
								__magic_name__		:List[Any]        =      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
								__magic_name__		:List[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
								__magic_name__		:Union[str, 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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      self.image_processing_class(**self.image_processor_dict     )
								# create random PyTorch tensors
								__magic_name__		:int        =      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
								__magic_name__		: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
								__magic_name__		: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      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				global f  # a global dp table for knapsack
				if f[i][j] < 0:
								if j < wt[i - 1]:
												__magic_name__		:Dict        =      mf_knapsack(i - 1,  snake_case,  snake_case,  snake_case )
								else:
												__magic_name__		:Optional[Any]        =      max(
												    mf_knapsack(i - 1,  snake_case,  snake_case,  snake_case ),  mf_knapsack(i - 1,  snake_case,  snake_case,  j - wt[i - 1] ) + val[i - 1],  )
								__magic_name__		:List[Any]        =      val
				return f[i][j]

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:str        =      [[0] * (w + 1) for _ in range(n + 1 )]

				for i in range(1,  n + 1 ):
								for w_ in range(1,  w + 1 ):
												if wt[i - 1] <= w_:
																__magic_name__		:List[str]        =      max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]],  dp[i - 1][w_] )
												else:
																__magic_name__		:Optional[int]        =      dp[i - 1][w_]

				return dp[n][w_], dp

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				if not (isinstance(snake_case,  (list, tuple) ) and isinstance(snake_case,  (list, tuple) )):
								raise ValueError(
								    '''Both the weights and values vectors must be either lists or tuples''' )

				__magic_name__		:Dict        =      len(snake_case )
				if num_items != len(snake_case ):
								__magic_name__		:str        =      (
								    '''The number of weights must be the same as the number of values.\n'''
								    f'''But got {num_items} weights and {len(snake_case )} values'''
								)
								raise ValueError(snake_case )
				for i in range(snake_case ):
								if not isinstance(wt[i],  snake_case ):
												__magic_name__		:str        =      (
												    '''All weights must be integers but got weight of '''
												    f'''type {type(wt[i] )} at index {i}'''
												)
												raise TypeError(snake_case )

				__magic_name__     ,				__magic_name__		:Tuple        =      knapsack(snake_case,  snake_case,  snake_case,  snake_case )
				__magic_name__		:set        =      set()
				_construct_solution(snake_case,  snake_case,  snake_case,  snake_case,  snake_case )

				return optimal_val, example_optional_set

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				if i > 0 and j > 0:
								if dp[i - 1][j] == dp[i][j]:
												_construct_solution(snake_case,  snake_case,  i - 1,  snake_case,  snake_case )
								else:
												optimal_set.add(snake_case )
												_construct_solution(snake_case,  snake_case,  i - 1,  j - wt[i - 1],  snake_case )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		[3, 2, 4, 4]
					SCREAMING_SNAKE_CASE__      :			Any							=		[4, 3, 2, 3]
					SCREAMING_SNAKE_CASE__      :			List[str]							=		4
					SCREAMING_SNAKE_CASE__      :			Optional[int]							=		6
					SCREAMING_SNAKE_CASE__      :			str							=		[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      :			List[str]							=		knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      :			List[str]							=		knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)






