

'''simple docstring'''


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


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import BlipImageProcessor



class        lowercase    (	unittest.TestCase			):


							def __init__(      self     ,      _snake_case     ,      _snake_case=7     ,      _snake_case=3     ,      _snake_case=18     ,      _snake_case=30     ,      _snake_case=400     ,      _snake_case=True     ,      _snake_case=None     ,      _snake_case=True     ,      _snake_case=False     ,      _snake_case=[0.48_145_466, 0.4_578_275, 0.40_821_073]     ,      _snake_case=[0.26_862_954, 0.26_130_258, 0.27_577_711]     ,      _snake_case=True     ,      )				->					Dict:
								UpperCAmelCase_		:   Optional[Any]													=       size if size is not None else {'height': 20, 'width': 20}
								UpperCAmelCase_		:   Any													=       parent
								UpperCAmelCase_		:   Tuple													=       batch_size
								UpperCAmelCase_		:   Optional[int]													=       num_channels
								UpperCAmelCase_		:   str													=       image_size
								UpperCAmelCase_		:   Tuple													=       min_resolution
								UpperCAmelCase_		:   Dict													=       max_resolution
								UpperCAmelCase_		:   Dict													=       do_resize
								UpperCAmelCase_		:   Dict													=       size
								UpperCAmelCase_		:   Optional[Any]													=       do_normalize
								UpperCAmelCase_		:   Tuple													=       image_mean
								UpperCAmelCase_		:   List[str]													=       image_std
								UpperCAmelCase_		:   Optional[Any]													=       do_pad
								UpperCAmelCase_		:   Dict													=       do_convert_rgb


							def     _snake_case				(      self)				->					Any:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_convert_rgb": self.do_convert_rgb,
								    "do_pad": self.do_pad,
								}





							def     _snake_case				(      self     ,      _snake_case=False     ,      _snake_case=False     ,      _snake_case=False)				->					Any:
								assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

								if equal_resolution:
									UpperCAmelCase_		:   Dict													=       []
									for i in range(self.batch_size):
										image_inputs.append(
										    np.random.randint(
										        255     ,      size=(self.num_channels, self.max_resolution, self.max_resolution)     ,      dtype=np.uinta))
								else:
									UpperCAmelCase_		:   Tuple													=       []
									for i in range(self.batch_size):
										UpperCAmelCase_						,     UpperCAmelCase_		:   Optional[int]													=       np.random.choice(np.arange(self.min_resolution     ,      self.max_resolution)     ,      2)
										image_inputs.append(np.random.randint(255     ,      size=(self.num_channels, width, height)     ,      dtype=np.uinta))

								if not numpify and not torchify:
									# PIL expects the channel dimension as last dimension
									UpperCAmelCase_		:   str													=       [Image.fromarray(np.moveaxis(_snake_case     ,      0     ,      -1)) for x in image_inputs]

								if torchify:
									UpperCAmelCase_		:   Dict													=       [torch.from_numpy(_snake_case) for x in image_inputs]

								return image_inputs



@require_torch
@require_vision
class        lowercase    (	a_,       unittest.TestCase			):
							_lowerCamelCase		:   Tuple= BlipImageProcessor if is_vision_available() else None


							def     _snake_case				(      self)				->					Tuple:
								UpperCAmelCase_		:   List[Any]													=       BlipImageProcessingTester(self)


							@property
							def     _snake_case				(      self)				->					int:
								return self.image_processor_tester.prepare_image_processor_dict()


							def     _snake_case				(      self)				->					int:
								UpperCAmelCase_		:   Optional[Any]													=       self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_snake_case     ,      'do_resize'))
								self.assertTrue(hasattr(_snake_case     ,      'size'))
								self.assertTrue(hasattr(_snake_case     ,      'do_normalize'))
								self.assertTrue(hasattr(_snake_case     ,      'image_mean'))
								self.assertTrue(hasattr(_snake_case     ,      'image_std'))
								self.assertTrue(hasattr(_snake_case     ,      'do_convert_rgb'))


							def     _snake_case				(      self)				->					int:
								pass


							def     _snake_case				(      self)				->					Optional[Any]:
								# Initialize image_processor
								UpperCAmelCase_		:   Optional[Any]													=       self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								UpperCAmelCase_		:   Dict													=       self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case)
								for image in image_inputs:
									self.assertIsInstance(_snake_case     ,      Image.Image)

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

								# Test batched
								UpperCAmelCase_		:   str													=       image_processor(_snake_case     ,      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.size['height'],
								        self.image_processor_tester.size['width'],
								    )     ,      )


							def     _snake_case				(      self)				->					int:
								# Initialize image_processor
								UpperCAmelCase_		:   Tuple													=       self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								UpperCAmelCase_		:   Optional[int]													=       self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case     ,      numpify=_snake_case)
								for image in image_inputs:
									self.assertIsInstance(_snake_case     ,      np.ndarray)

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

								# Test batched
								UpperCAmelCase_		:   str													=       image_processor(_snake_case     ,      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.size['height'],
								        self.image_processor_tester.size['width'],
								    )     ,      )





							def     _snake_case				(      self)				->					Tuple:
								# Initialize image_processor
								UpperCAmelCase_		:   Union[str, Any]													=       self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								UpperCAmelCase_		:   List[str]													=       self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case     ,      torchify=_snake_case)
								for image in image_inputs:
									self.assertIsInstance(_snake_case     ,      torch.Tensor)

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

								# Test batched
								UpperCAmelCase_		:   Union[str, Any]													=       image_processor(_snake_case     ,      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.size['height'],
								        self.image_processor_tester.size['width'],
								    )     ,      )



@require_torch
@require_vision
class        lowercase    (	a_,       unittest.TestCase			):
							_lowerCamelCase		:   int= BlipImageProcessor if is_vision_available() else None


							def     _snake_case				(      self)				->					Dict:
								UpperCAmelCase_		:   int													=       BlipImageProcessingTester(self     ,      num_channels=4)
								UpperCAmelCase_		:   Union[str, Any]													=       3


							@property
							def     _snake_case				(      self)				->					int:
								return self.image_processor_tester.prepare_image_processor_dict()


							def     _snake_case				(      self)				->					Any:
								UpperCAmelCase_		:   Optional[Any]													=       self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_snake_case     ,      'do_resize'))
								self.assertTrue(hasattr(_snake_case     ,      'size'))
								self.assertTrue(hasattr(_snake_case     ,      'do_normalize'))
								self.assertTrue(hasattr(_snake_case     ,      'image_mean'))
								self.assertTrue(hasattr(_snake_case     ,      'image_std'))
								self.assertTrue(hasattr(_snake_case     ,      'do_convert_rgb'))


							def     _snake_case				(      self)				->					str:
								pass





							def     _snake_case				(      self)				->					Optional[int]:
								# Initialize image_processor
								UpperCAmelCase_		:   Optional[int]													=       self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								UpperCAmelCase_		:   str													=       self.image_processor_tester.prepare_inputs(equal_resolution=_snake_case)
								for image in image_inputs:
									self.assertIsInstance(_snake_case     ,      Image.Image)

								# Test not batched input
								UpperCAmelCase_		:   List[Any]													=       image_processor(image_inputs[0]     ,      return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape     ,      (
								        1,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )     ,      )

								# Test batched
								UpperCAmelCase_		:   Optional[int]													=       image_processor(_snake_case     ,      return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_images.shape     ,      (
								        self.image_processor_tester.batch_size,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.size['height'],
								        self.image_processor_tester.size['width'],
								    )     ,      )









'''simple docstring'''


from __future__ import annotations







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     int:

	# Base Case
	if counter == 0 or capacity == 0:
		return 0

	# If weight of the nth item is more than Knapsack of capacity,
	#   then this item cannot be included in the optimal solution,
	# else return the maximum of two cases:
	#   (1) nth item included
	#   (2) not included
	if weights[counter - 1] > capacity:
		return knapsack(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,counter - 1	)
	else:
		UpperCAmelCase_		:   int													=       capacity - weights[counter - 1]
		UpperCAmelCase_		:   Optional[int]													=       values[counter - 1] + knapsack(
		    UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,counter - 1	)
		UpperCAmelCase_		:   Any													=       knapsack(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,counter - 1	)
		return max(UpperCamelCase ,UpperCamelCase	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()






