

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   ,	A__   ,	A__=7   ,	A__=3   ,	A__=30   ,	A__=400   ,	A__=True   ,	A__=None   ,	A__=0.9   ,	A__=None   ,	A__=True   ,	A__=[0.5, 0.5, 0.5]   ,	A__=[0.5, 0.5, 0.5]   ,	):
						A__							:   Any					       =		size if size is not None else {"""shortest_edge""": 30}
						A__							:   Tuple					       =		crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
						A__							:   Optional[Any]					       =		parent
						A__							:   Any					       =		batch_size
						A__							:   Any					       =		num_channels
						A__							:   Union[str, Any]					       =		min_resolution
						A__							:   List[str]					       =		max_resolution
						A__							:   Dict					       =		do_resize_and_center_crop
						A__							:   int					       =		size
						A__							:   Optional[Any]					       =		crop_pct
						A__							:   Optional[int]					       =		crop_size
						A__							:   Tuple					       =		do_normalize
						A__							:   Optional[Any]					       =		image_mean
						A__							:   int					       =		image_std






					def   __A      (			self      ):
						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						(__snake_case		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[Any]  							=	PoolFormerImageProcessor if is_vision_available() else None






					def   __A      (			self      ):
						A__							:   Any					       =		PoolFormerImageProcessingTester(self      )






					@property
					def   __A      (			self      ):
						return self.image_processor_tester.prepare_image_processor_dict()






					def   __A      (			self      ):
						A__							:   Any					       =		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   __A      (			self      ):
						A__							:   Any					       =		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__							:   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   __A      (			self      ):
						pass






					def   __A      (			self      ):
						# Initialize image_processing
						A__							:   Dict					       =		self.image_processing_class(**self.image_processor_dict      )
						# create random PIL images
						A__							:   List[str]					       =		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__							:   Tuple					       =		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__							:   List[Any]					       =		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   __A      (			self      ):
						# Initialize image_processing
						A__							:   int					       =		self.image_processing_class(**self.image_processor_dict      )
						# create random numpy tensors
						A__							:   Optional[Any]					       =		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__							:   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
						A__							:   Optional[int]					       =		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   __A      (			self      ):
						# Initialize image_processing
						A__							:   str					       =		self.image_processing_class(**self.image_processor_dict      )
						# create random PyTorch tensors
						A__							:   List[Any]					       =		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__							:   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
						A__							:   Optional[int]					       =		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"""],
						    )   ,	)






from __future__ import annotations

def 						UpperCamelCase (lowercase_:			list[int]   ,				lowercase_:			list[int]   ,				lowercase_:			int )					->				tuple[float, list[float]]:
	A__							:   Tuple					       =		list(range(len(lowercase_ ) ) )
	A__							:   Union[str, Any]					       =		[v / w for v, w in zip(lowercase_   ,				lowercase_ )]
	index.sort(key=lambda lowercase_ : ratio[i]   ,				reverse=lowercase_ )

	A__							:   float					       =		0
	A__							:   list[float]					       =		[0] * len(lowercase_ )
	for i in index:
		if weight[i] <= capacity:
			A__							:   Optional[int]					       =		1
			max_value += value[i]
			capacity -= weight[i]
		else:
			A__							:   Union[str, Any]					       =		capacity / weight[i]
			max_value += value[i] * capacity / weight[i]
			break

	return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()



