




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 		UpperCAmelCase   (				unittest.TestCase			):







						'''simple docstring'''





						def __init__(						self							:     Optional[Any]       ,	__lowercase							:     Dict       ,	__lowercase							:     Any=7       ,	__lowercase							:     Dict=3       ,	__lowercase							:     Tuple=30       ,	__lowercase							:     Any=4_00       ,	__lowercase							:     List[str]=True       ,	__lowercase							:     Optional[int]=None       ,	__lowercase							:     Optional[Any]=0.9       ,	__lowercase							:     Any=None       ,	__lowercase							:     Optional[Any]=True       ,	__lowercase							:     Dict=[0.5, 0.5, 0.5]       ,	__lowercase							:     Union[str, Any]=[0.5, 0.5, 0.5]       ,	):






							"""simple docstring"""






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






						def        snake_case__						(						self							:     Optional[Any]						):






							"""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 		UpperCAmelCase   (				UpperCAmelCase__       ,   unittest.TestCase			):







						'''simple docstring'''





						lowerCAmelCase_     		=   PoolFormerImageProcessor if is_vision_available() else None






						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






							snake_case_		    =     PoolFormerImageProcessingTester(self						)






						@property
						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							return self.image_processor_tester.prepare_image_processor_dict()






						def        snake_case__						(						self							:     int						):






							"""simple docstring"""






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






						def        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							snake_case_		    =     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}						)

							snake_case_		    =     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							:     Any						):






							"""simple docstring"""






							pass






						def        snake_case__						(						self							:     Union[str, Any]						):






							"""simple docstring"""






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

							# Test not batched input
							snake_case_		    =     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
							snake_case_		    =     image_processing(__lowercase       ,	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							:     Union[str, Any]						):






							"""simple docstring"""






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

							# Test not batched input
							snake_case_		    =     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
							snake_case_		    =     image_processing(__lowercase       ,	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]						):






							"""simple docstring"""






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

							# Test not batched input
							snake_case_		    =     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
							snake_case_		    =     image_processing(__lowercase       ,	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 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





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

	return max_revue


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	_enforce_args(_A     ,	_A							)
	snake_case_		    =     [float("-inf"							) for _ in range(n + 1							)]
	return _top_down_cut_rod_recursive(_A     ,	_A     ,	_A							)


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A							):







	'''simple docstring'''





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

		snake_case_		    =     max_revenue

	return max_rev[n]


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	_enforce_args(_A     ,	_A							)

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

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

		snake_case_		    =     max_revenue_i

	return max_rev[n]


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	if n < 0:
		snake_case_		    =     f"n must be greater than or equal to 0. Got n = {n}"
		raise ValueError(_A							)

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


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     [6, 10, 12, 15, 20, 23]
	snake_case_		    =     len(_A							)

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

	snake_case_		    =     top_down_cut_rod(_A     ,	_A							)
	snake_case_		    =     bottom_up_cut_rod(_A     ,	_A							)
	snake_case_		    =     naive_cut_rod_recursive(_A     ,	_A							)

	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()
