



'''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, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import MobileViTImageProcessor






class 						_UpperCamelCase  (							unittest.TestCase    ):


			'''simple docstring'''

			def __init__(							self     : int							,     lowerCAmelCase__     : List[Any]							,     lowerCAmelCase__     : Any=7							,     lowerCAmelCase__     : Tuple=3							,     lowerCAmelCase__     : Any=1_8							,     lowerCAmelCase__     : Optional[Any]=3_0							,     lowerCAmelCase__     : Optional[int]=4_0_0							,     lowerCAmelCase__     : List[Any]=True							,     lowerCAmelCase__     : Union[str, Any]=None							,     lowerCAmelCase__     : Dict=True							,     lowerCAmelCase__     : Dict=None							,     lowerCAmelCase__     : Optional[Any]=True							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[str]           =		size if size is not None else {"""shortest_edge""": 2_0}
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}
					__SCREAMING_SNAKE_CASE       :		List[str]           =		parent
					__SCREAMING_SNAKE_CASE       :		List[str]           =		batch_size
					__SCREAMING_SNAKE_CASE       :		Any           =		num_channels
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		image_size
					__SCREAMING_SNAKE_CASE       :		List[str]           =		min_resolution
					__SCREAMING_SNAKE_CASE       :		Any           =		max_resolution
					__SCREAMING_SNAKE_CASE       :		Any           =		do_resize
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		size
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		do_center_crop
					__SCREAMING_SNAKE_CASE       :		Dict           =		crop_size
					__SCREAMING_SNAKE_CASE       :		int           =		do_flip_channel_order



			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					    "do_flip_channel_order": self.do_flip_channel_order,
					}






@require_torch
@require_vision
class 						_UpperCamelCase  (							lowerCamelCase__    ,					unittest.TestCase    ):


			'''simple docstring'''

			_A      :		List[str]			    =		MobileViTImageProcessor if is_vision_available() else None

			def UpperCamelCase__					(							self     : Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		MobileViTImageProcessingTester(self						)

			@property
			def UpperCamelCase__					(							self     : Tuple						):





					"""simple docstring"""






					return self.image_processor_tester.prepare_image_processor_dict()

			def UpperCamelCase__					(							self     : int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.image_processing_class(**self.image_processor_dict						)
					self.assertTrue(hasattr(lowerCAmelCase__							,     """do_resize"""						)						)
					self.assertTrue(hasattr(lowerCAmelCase__							,     """size"""						)						)
					self.assertTrue(hasattr(lowerCAmelCase__							,     """do_center_crop"""						)						)
					self.assertTrue(hasattr(lowerCAmelCase__							,     """center_crop"""						)						)
					self.assertTrue(hasattr(lowerCAmelCase__							,     """do_flip_channel_order"""						)						)

			def UpperCamelCase__					(							self     : List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[str]           =		self.image_processing_class.from_dict(self.image_processor_dict						)
					self.assertEqual(image_processor.size							,     {"""shortest_edge""": 2_0}						)
					self.assertEqual(image_processor.crop_size							,     {"""height""": 1_8, """width""": 1_8}						)

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.image_processing_class.from_dict(self.image_processor_dict							,     size=4_2							,     crop_size=8_4						)
					self.assertEqual(image_processor.size							,     {"""shortest_edge""": 4_2}						)
					self.assertEqual(image_processor.crop_size							,     {"""height""": 8_4, """width""": 8_4}						)

			def UpperCamelCase__					(							self     : str						):





					"""simple docstring"""






					pass

			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		self.image_processing_class(**self.image_processor_dict						)
					# create random PIL images
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase__						)
					for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase__							,     Image.Image						)

					# Test not batched input
					__SCREAMING_SNAKE_CASE       :		List[str]           =		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
					__SCREAMING_SNAKE_CASE       :		List[str]           =		image_processing(lowerCAmelCase__							,     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"""






					__SCREAMING_SNAKE_CASE       :		List[str]           =		self.image_processing_class(**self.image_processor_dict						)
					# create random numpy tensors
					__SCREAMING_SNAKE_CASE       :		str           =		prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase__							,     numpify=lowerCAmelCase__						)
					for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase__							,     np.ndarray						)

					# Test not batched input
					__SCREAMING_SNAKE_CASE       :		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
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		image_processing(lowerCAmelCase__							,     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"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.image_processing_class(**self.image_processor_dict						)
					# create random PyTorch tensors
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase__							,     torchify=lowerCAmelCase__						)
					for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase__							,     torch.Tensor						)

					# Test not batched input
					__SCREAMING_SNAKE_CASE       :		Dict           =		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
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		image_processing(lowerCAmelCase__							,     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'''




from __future__ import annotations


def 				lowerCAmelCase_   (	_lowerCamelCase:   list[int]					):
		if not nums:
				return 0
		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nums[0]
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		0
		for num in nums[1:]:
				__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Any           =		(
				    max_excluding + num,
				    max(_lowerCamelCase       ,      _lowerCamelCase					),
				)
		return max(_lowerCamelCase       ,      _lowerCamelCase					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()