

def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					_enforce_args(UpperCAmelCase__		,					UpperCAmelCase__      )
					if n == 0:
										return 0
					__lowerCAmelCase					=							float('-inf'      )
					for i in range(1		,					n + 1      ):
										__lowerCAmelCase					=							max(
										    UpperCAmelCase__		,					prices[i - 1] + naive_cut_rod_recursive(n - i		,					UpperCAmelCase__      )      )

					return max_revue


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					_enforce_args(UpperCAmelCase__		,					UpperCAmelCase__      )
					__lowerCAmelCase					=							[float('-inf'      ) for _ in range(n + 1      )]
					return _top_down_cut_rod_recursive(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					if max_rev[n] >= 0:
										return max_rev[n]
					elif n == 0:
										return 0
					else:
										__lowerCAmelCase					=							float('-inf'      )
										for i in range(1		,					n + 1      ):
															__lowerCAmelCase					=							max(
															    UpperCAmelCase__		,					prices[i - 1] + _top_down_cut_rod_recursive(n - i		,					UpperCAmelCase__		,					UpperCAmelCase__      )		,					)

										__lowerCAmelCase					=							max_revenue

					return max_rev[n]


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					_enforce_args(UpperCAmelCase__		,					UpperCAmelCase__      )

					# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
					# length 0.
					__lowerCAmelCase					=							[float('-inf'      ) for _ in range(n + 1      )]
					__lowerCAmelCase					=							0

					for i in range(1		,					n + 1      ):
										__lowerCAmelCase					=							max_rev[i]
										for j in range(1		,					i + 1      ):
															__lowerCAmelCase					=							max(UpperCAmelCase__		,					prices[j - 1] + max_rev[i - j]      )

										__lowerCAmelCase					=							max_revenue_i

					return max_rev[n]


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					if n < 0:
										__lowerCAmelCase					=							F"""n must be greater than or equal to 0. Got n = {n}"""
										raise ValueError(UpperCAmelCase__      )

					if n > len(UpperCAmelCase__      ):
										__lowerCAmelCase					=							(
										    'Each integral piece of rod must have a corresponding price. '
										    F"""Got n = {n} but length of prices = {len(UpperCAmelCase__      )}"""
										)
										raise ValueError(UpperCAmelCase__      )


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							[6, 10, 12, 15, 20, 23]
					__lowerCAmelCase					=							len(UpperCAmelCase__      )

					# the best revenue comes from cutting the rod into 6 pieces, each
					# of length 1 resulting in a revenue of 6 * 6 = 36.
					__lowerCAmelCase					=							36

					__lowerCAmelCase					=							top_down_cut_rod(UpperCAmelCase__		,					UpperCAmelCase__      )
					__lowerCAmelCase					=							bottom_up_cut_rod(UpperCAmelCase__		,					UpperCAmelCase__      )
					__lowerCAmelCase					=							naive_cut_rod_recursive(UpperCAmelCase__		,					UpperCAmelCase__      )

					assert expected_max_revenue == max_rev_top_down
					assert max_rev_top_down == max_rev_bottom_up
					assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
		main()









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 ViTImageProcessor





class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=1_3					,						_A=3					,						_A=2_2_4					,						_A=3_0					,						_A=4_0_0					,						_A=True					,						_A=None					,						_A=True					,						_A=[0.5, 0.5, 0.5]					,						_A=[0.5, 0.5, 0.5]					,						):
												__lowerCAmelCase					=							size if size is not None else {'height': 1_8, 'width': 1_8}
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							num_channels
												__lowerCAmelCase					=							image_size
												__lowerCAmelCase					=							min_resolution
												__lowerCAmelCase					=							max_resolution
												__lowerCAmelCase					=							do_resize
												__lowerCAmelCase					=							size
												__lowerCAmelCase					=							do_normalize
												__lowerCAmelCase					=							image_mean
												__lowerCAmelCase					=							image_std




							def        A__  ( 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,
												}





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


							"""simple docstring"""





							__UpperCAmelCase       =ViTImageProcessor if is_vision_available() else None




							def        A__  ( self							):
												__lowerCAmelCase					=							EfficientFormerImageProcessorTester(self							)




							@property
							def        A__  ( self							):
												return self.image_proc_tester.prepare_image_processor_dict()




							def        A__  ( self							):
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												self.assertTrue(hasattr(_A					,						'image_mean'							)							)
												self.assertTrue(hasattr(_A					,						'image_std'							)							)
												self.assertTrue(hasattr(_A					,						'do_normalize'							)							)
												self.assertTrue(hasattr(_A					,						'do_resize'							)							)
												self.assertTrue(hasattr(_A					,						'size'							)							)




							def        A__  ( self							):
												pass




							def        A__  ( self							):
												# Initialize image_processor
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random PIL images
												__lowerCAmelCase					=							prepare_image_inputs(self.image_proc_tester					,						equal_resolution=_A							)
												for image in image_inputs:
																	self.assertIsInstance(_A					,						Image.Image							)

												# Test not batched input
												__lowerCAmelCase					=							image_processor(image_inputs[0]					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processor(_A					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)




							def        A__  ( self							):
												# Initialize image_processor
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random numpy tensors
												__lowerCAmelCase					=							prepare_image_inputs(self.image_proc_tester					,						equal_resolution=_A					,						numpify=_A							)
												for image in image_inputs:
																	self.assertIsInstance(_A					,						np.ndarray							)

												# Test not batched input
												__lowerCAmelCase					=							image_processor(image_inputs[0]					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processor(_A					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)




							def        A__  ( self							):
												# Initialize image_processor
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random PyTorch tensors
												__lowerCAmelCase					=							prepare_image_inputs(self.image_proc_tester					,						equal_resolution=_A					,						torchify=_A							)
												for image in image_inputs:
																	self.assertIsInstance(_A					,						torch.Tensor							)

												# Test not batched input
												__lowerCAmelCase					=							image_processor(image_inputs[0]					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processor(_A					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)






