

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     a__  (    unittest.TestCase	):
			"""simple docstring"""







			def __init__(			self					,					lowercase					,					lowercase=7					,					lowercase=3					,					lowercase=30					,					lowercase=400					,					lowercase=True					,					lowercase=None					,					lowercase=0.9					,					lowercase=None					,					lowercase=True					,					lowercase=[0.5, 0.5, 0.5]					,					lowercase=[0.5, 0.5, 0.5]					,					)     ->  Optional[Any]:
							'''simple docstring'''






							A__						=	size if size is not None else {"shortest_edge": 30}
							A__						=	crop_size if crop_size is not None else {"height": 30, "width": 30}
							A__						=	parent
							A__						=	batch_size
							A__						=	num_channels
							A__						=	min_resolution
							A__						=	max_resolution
							A__						=	do_resize_and_center_crop
							A__						=	size
							A__						=	crop_pct
							A__						=	crop_size
							A__						=	do_normalize
							A__						=	image_mean
							A__						=	image_std


			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''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     a__  (    __SCREAMING_SNAKE_CASE							,		unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				PoolFormerImageProcessor if is_vision_available() else None


			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							A__						=	PoolFormerImageProcessingTester(self				)


			@property
			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							return self.image_processor_tester.prepare_image_processor_dict()


			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(_a					,					"do_resize_and_center_crop"				)				)
							self.assertTrue(hasattr(_a					,					"size"				)				)
							self.assertTrue(hasattr(_a					,					"crop_pct"				)				)
							self.assertTrue(hasattr(_a					,					"do_normalize"				)				)
							self.assertTrue(hasattr(_a					,					"image_mean"				)				)
							self.assertTrue(hasattr(_a					,					"image_std"				)				)


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							A__						=	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}				)

							A__						=	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    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							A__						=	prepare_image_inputs(self.image_processor_tester					,					equal_resolution=_a				)
							for image in image_inputs:
											self.assertIsInstance(_a					,					Image.Image				)

							# Test not batched input
							A__						=	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
							A__						=	image_processing(_a					,					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    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							A__						=	prepare_image_inputs(self.image_processor_tester					,					equal_resolution=_a					,					numpify=_a				)
							for image in image_inputs:
											self.assertIsInstance(_a					,					np.ndarray				)

							# Test not batched input
							A__						=	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
							A__						=	image_processing(_a					,					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    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							A__						=	self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							A__						=	prepare_image_inputs(self.image_processor_tester					,					equal_resolution=_a					,					torchify=_a				)
							for image in image_inputs:
											self.assertIsInstance(_a					,					torch.Tensor				)

							# Test not batched input
							A__						=	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
							A__						=	image_processing(_a					,					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        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      List[Any]:
		_enforce_args(lowerCAmelCase__    ,					lowerCAmelCase__			)
		if n == 0:
				return 0
		__a           =			float('''-inf'''			)
		for i in range(1    ,					n + 1			):
				__a           =			max(
				    lowerCAmelCase__    ,					prices[i - 1] + naive_cut_rod_recursive(n - i    ,					lowerCAmelCase__			)			)

		return max_revue

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      List[str]:
		_enforce_args(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			[float('''-inf'''			) for _ in range(n + 1			)]
		return _top_down_cut_rod_recursive(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list    ,					lowerCAmelCase__ :     list			) ->      Union[str, Any]:
		if max_rev[n] >= 0:
				return max_rev[n]
		elif n == 0:
				return 0
		else:
				__a           =			float('''-inf'''			)
				for i in range(1    ,					n + 1			):
						__a           =			max(
						    lowerCAmelCase__    ,					prices[i - 1] + _top_down_cut_rod_recursive(n - i    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)    ,					)

				__a           =			max_revenue

		return max_rev[n]

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      Dict:
		_enforce_args(lowerCAmelCase__    ,					lowerCAmelCase__			)

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

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

				__a           =			max_revenue_i

		return max_rev[n]

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     list			) ->      str:
		if n < 0:
				__a           =			f'''n must be greater than or equal to 0. Got n = {n}'''
				raise ValueError(lowerCAmelCase__			)

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

def        lowercase							(		) ->      int:
		__a           =			[6, 10, 12, 15, 20, 23]
		__a           =			len(lowerCAmelCase__			)

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

		__a           =			top_down_cut_rod(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			bottom_up_cut_rod(lowerCAmelCase__    ,					lowerCAmelCase__			)
		__a           =			naive_cut_rod_recursive(lowerCAmelCase__    ,					lowerCAmelCase__			)

		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()






