
from __future__ import annotations






def   lowerCamelCase__							(							__lowerCAmelCase    :    list[int]							):




			"""simple docstring"""


			if not nums:
						return 0
			lowerCAmelCase_         =      nums[0]
			lowerCAmelCase_         =      0
			for num in nums[1:]:
						lowerCAmelCase_      ,    lowerCAmelCase_         =      (
						    max_excluding + num,
						    max(__lowerCAmelCase       ,				__lowerCAmelCase							),
						)
			return max(__lowerCAmelCase       ,				__lowerCAmelCase							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()





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 ChineseCLIPImageProcessor





class       _lowerCAmelCase   (      unittest.TestCase			):




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase=7							,							_UpperCamelCase=3							,							_UpperCamelCase=18							,							_UpperCamelCase=30							,							_UpperCamelCase=400							,							_UpperCamelCase=True							,							_UpperCamelCase=None							,							_UpperCamelCase=True							,							_UpperCamelCase=None							,							_UpperCamelCase=True							,							_UpperCamelCase=[0.48145466, 0.4578275, 0.40821073]							,							_UpperCamelCase=[0.26862954, 0.26130258, 0.27577711]							,							_UpperCamelCase=True							,							)							-> Dict:
							lowerCAmelCase_         =      size if size is not None else {"height": 224, "width": 224}
							lowerCAmelCase_         =      crop_size if crop_size is not None else {"height": 18, "width": 18}
							lowerCAmelCase_         =      parent
							lowerCAmelCase_         =      batch_size
							lowerCAmelCase_         =      num_channels
							lowerCAmelCase_         =      image_size
							lowerCAmelCase_         =      min_resolution
							lowerCAmelCase_         =      max_resolution
							lowerCAmelCase_         =      do_resize
							lowerCAmelCase_         =      size
							lowerCAmelCase_         =      do_center_crop
							lowerCAmelCase_         =      crop_size
							lowerCAmelCase_         =      do_normalize
							lowerCAmelCase_         =      image_mean
							lowerCAmelCase_         =      image_std
							lowerCAmelCase_         =      do_convert_rgb




				def 	__a		(   self	)							-> List[Any]:
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_convert_rgb": self.do_convert_rgb,
							}




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

							if equal_resolution:
										lowerCAmelCase_         =      []
										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:
										lowerCAmelCase_         =      []
										for i in range(self.batch_size	):
													lowerCAmelCase_      ,    lowerCAmelCase_         =      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
										lowerCAmelCase_         =      [Image.fromarray(np.moveaxis(_UpperCamelCase							,							0							,							-1	)	) for x in image_inputs]

							if torchify:
										lowerCAmelCase_         =      [torch.from_numpy(_UpperCamelCase	) for x in image_inputs]

							return image_inputs





@require_torch
@require_vision
class       _lowerCAmelCase   (      __a   , unittest.TestCase			):
				_lowercase     						=ChineseCLIPImageProcessor if is_vision_available() else None




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      ChineseCLIPImageProcessingTester(self							,							do_center_crop=_UpperCamelCase	)




				@property
				def 	__a		(   self	)							-> Tuple:
							return self.image_processor_tester.prepare_image_processor_dict()




				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_resize"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"size"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_center_crop"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"center_crop"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_normalize"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"image_mean"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"image_std"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_convert_rgb"	)	)




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      self.image_processing_class.from_dict(self.image_processor_dict	)
							self.assertEqual(image_processor.size							,							{"height": 224, "width": 224}	)
							self.assertEqual(image_processor.crop_size							,							{"height": 18, "width": 18}	)

							lowerCAmelCase_         =      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	)							-> str:
							pass




				def 	__a		(   self	)							-> Any:
							# Initialize image_processing
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							# create random PIL images
							lowerCAmelCase_         =      self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCamelCase	)
							for image in image_inputs:
										self.assertIsInstance(_UpperCamelCase							,							Image.Image	)

							# Test not batched input
							lowerCAmelCase_         =      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
							lowerCAmelCase_         =      image_processing(_UpperCamelCase							,							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	)							-> str:
							# Initialize image_processing
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							# create random numpy tensors
							lowerCAmelCase_         =      self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCamelCase							,							numpify=_UpperCamelCase	)
							for image in image_inputs:
										self.assertIsInstance(_UpperCamelCase							,							np.ndarray	)

							# Test not batched input
							lowerCAmelCase_         =      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
							lowerCAmelCase_         =      image_processing(_UpperCamelCase							,							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	)							-> Union[str, Any]:
							# Initialize image_processing
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							# create random PyTorch tensors
							lowerCAmelCase_         =      self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCamelCase							,							torchify=_UpperCamelCase	)
							for image in image_inputs:
										self.assertIsInstance(_UpperCamelCase							,							torch.Tensor	)

							# Test not batched input
							lowerCAmelCase_         =      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
							lowerCAmelCase_         =      image_processing(_UpperCamelCase							,							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"],
							    )							,							)





@require_torch
@require_vision
class       _lowerCAmelCase   (      __a   , unittest.TestCase			):
				_lowercase     						=ChineseCLIPImageProcessor if is_vision_available() else None




				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      ChineseCLIPImageProcessingTester(self							,							num_channels=4							,							do_center_crop=_UpperCamelCase	)
							lowerCAmelCase_         =      3




				@property
				def 	__a		(   self	)							-> List[str]:
							return self.image_processor_tester.prepare_image_processor_dict()




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_resize"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"size"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_center_crop"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"center_crop"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_normalize"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"image_mean"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"image_std"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_convert_rgb"	)	)




				def 	__a		(   self	)							-> int:
							pass




				def 	__a		(   self	)							-> str:
							# Initialize image_processing
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							# create random PIL images
							lowerCAmelCase_         =      self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCamelCase	)
							for image in image_inputs:
										self.assertIsInstance(_UpperCamelCase							,							Image.Image	)

							# Test not batched input
							lowerCAmelCase_         =      image_processing(image_inputs[0]							,							return_tensors="pt"	).pixel_values
							self.assertEqual(
							    encoded_images.shape							,							(
							        1,
							        self.expected_encoded_image_num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,							)

							# Test batched
							lowerCAmelCase_         =      image_processing(_UpperCamelCase							,							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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,							)



