






"""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 ConvNextImageProcessor


class _lowerCAmelCase	(						unittest.TestCase					):







						def __init__(						self      ,							lowerCAmelCase_      ,							lowerCAmelCase_=7      ,							lowerCAmelCase_=3      ,							lowerCAmelCase_=1_8      ,							lowerCAmelCase_=3_0      ,							lowerCAmelCase_=4_0_0      ,							lowerCAmelCase_=True      ,							lowerCAmelCase_=None      ,							lowerCAmelCase_=0.875      ,							lowerCAmelCase_=True      ,							lowerCAmelCase_=[0.5, 0.5, 0.5]      ,							lowerCAmelCase_=[0.5, 0.5, 0.5]      ,							)		->       Optional[int]:
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						size if size is not None else {'shortest_edge': 2_0}
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						parent
										_SCREAMING_SNAKE_CASE      :		Dict							=						batch_size
										_SCREAMING_SNAKE_CASE      :		Any							=						num_channels
										_SCREAMING_SNAKE_CASE      :		Any							=						image_size
										_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						min_resolution
										_SCREAMING_SNAKE_CASE      :		Tuple							=						max_resolution
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						do_resize
										_SCREAMING_SNAKE_CASE      :		Optional[int]							=						size
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						crop_pct
										_SCREAMING_SNAKE_CASE      :		int							=						do_normalize
										_SCREAMING_SNAKE_CASE      :		Any							=						image_mean
										_SCREAMING_SNAKE_CASE      :		Tuple							=						image_std







						def  A			(						self       )		->       str:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "crop_pct": self.crop_pct,
										}







@require_torch
@require_vision
class _lowerCAmelCase	(						__UpperCAmelCase      ,     unittest.TestCase					):
						SCREAMING_SNAKE_CASE_:				Optional[int]   				=							ConvNextImageProcessor if is_vision_available() else None







						def  A			(						self       )		->       Union[str, Any]:
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						ConvNextImageProcessingTester(self       )







						@property
						def  A			(						self       )		->       Union[str, Any]:
										return self.image_processor_tester.prepare_image_processor_dict()







						def  A			(						self       )		->       Optional[Any]:
										_SCREAMING_SNAKE_CASE      :		Dict							=						self.image_processing_class(**self.image_processor_dict       )
										self.assertTrue(hasattr(lowerCAmelCase_      ,							'do_resize'       )       )
										self.assertTrue(hasattr(lowerCAmelCase_      ,							'size'       )       )
										self.assertTrue(hasattr(lowerCAmelCase_      ,							'crop_pct'       )       )
										self.assertTrue(hasattr(lowerCAmelCase_      ,							'do_normalize'       )       )
										self.assertTrue(hasattr(lowerCAmelCase_      ,							'image_mean'       )       )
										self.assertTrue(hasattr(lowerCAmelCase_      ,							'image_std'       )       )







						def  A			(						self       )		->       Tuple:
										_SCREAMING_SNAKE_CASE      :		List[str]							=						self.image_processing_class.from_dict(self.image_processor_dict       )
										self.assertEqual(image_processor.size      ,							{'shortest_edge': 2_0}       )

										_SCREAMING_SNAKE_CASE      :		str							=						self.image_processing_class.from_dict(self.image_processor_dict      ,							size=4_2       )
										self.assertEqual(image_processor.size      ,							{'shortest_edge': 4_2}       )







						def  A			(						self       )		->       int:
										pass







						def  A			(						self       )		->       int:
										# Initialize image_processing
										_SCREAMING_SNAKE_CASE      :		List[str]							=						self.image_processing_class(**self.image_processor_dict       )
										# create random PIL images
										_SCREAMING_SNAKE_CASE      :		List[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      :		Optional[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.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )      ,							)

										# Test batched
										_SCREAMING_SNAKE_CASE      :		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.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )      ,							)







						def  A			(						self       )		->       Optional[Any]:
										# Initialize image_processing
										_SCREAMING_SNAKE_CASE      :		List[Any]							=						self.image_processing_class(**self.image_processor_dict       )
										# create random numpy tensors
										_SCREAMING_SNAKE_CASE      :		List[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      :		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.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )      ,							)

										# Test batched
										_SCREAMING_SNAKE_CASE      :		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.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )      ,							)







						def  A			(						self       )		->       List[str]:
										# Initialize image_processing
										_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      :		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.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )      ,							)

										# Test batched
										_SCREAMING_SNAKE_CASE      :		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.size['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    )      ,							)









"""simple docstring"""
def 							lowercase__		(				lowerCamelCase					):
				return -num if num < 0 else num





def 							lowercase__		(				lowerCamelCase					):
				if len(lowerCamelCase					) == 0:
								raise ValueError('abs_min() arg is an empty sequence'					)
				_SCREAMING_SNAKE_CASE      :		List[Any]							=						x[0]
				for i in x:
								if abs_val(lowerCamelCase					) < abs_val(lowerCamelCase					):
												_SCREAMING_SNAKE_CASE      :		List[Any]							=						i
				return j





def 							lowercase__		(				lowerCamelCase					):
				if len(lowerCamelCase					) == 0:
								raise ValueError('abs_max() arg is an empty sequence'					)
				_SCREAMING_SNAKE_CASE      :		Tuple							=						x[0]
				for i in x:
								if abs(lowerCamelCase					) > abs(lowerCamelCase					):
												_SCREAMING_SNAKE_CASE      :		Optional[Any]							=						i
				return j





def 							lowercase__		(				lowerCamelCase					):
				if len(lowerCamelCase					) == 0:
								raise ValueError('abs_max_sort() arg is an empty sequence'					)
				return sorted(lowerCamelCase,	key=lowerCamelCase					)[-1]





def 							lowercase__		(				):
				assert abs_val(0					) == 0
				assert abs_val(34					) == 34
				assert abs_val(-100_000_000_000					) == 100_000_000_000

				_SCREAMING_SNAKE_CASE      :		Tuple							=						[-3, -1, 2, -11]
				assert abs_max(lowerCamelCase					) == -11
				assert abs_max_sort(lowerCamelCase					) == -11
				assert abs_min(lowerCamelCase					) == -1


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			test_abs_val()
			print(abs_val(-34))  # --> 34

