




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 	__magic_name__				(       unittest.TestCase							):






     def __init__(							self		,	_lowercase		,	_lowercase=7		,	_lowercase=3		,	_lowercase=18		,	_lowercase=30		,	_lowercase=400		,	_lowercase=True		,	_lowercase=None		,	_lowercase=0.875		,	_lowercase=True		,	_lowercase=[0.5, 0.5, 0.5]		,	_lowercase=[0.5, 0.5, 0.5]		,	)->    str:
         UpperCamelCase_      =	size if size is not None else {"shortest_edge": 20}
         UpperCamelCase_      =	parent
         UpperCamelCase_      =	batch_size
         UpperCamelCase_      =	num_channels
         UpperCamelCase_      =	image_size
         UpperCamelCase_      =	min_resolution
         UpperCamelCase_      =	max_resolution
         UpperCamelCase_      =	do_resize
         UpperCamelCase_      =	size
         UpperCamelCase_      =	crop_pct
         UpperCamelCase_      =	do_normalize
         UpperCamelCase_      =	image_mean
         UpperCamelCase_      =	image_std






     def   UpperCAmelCase_							(							self  )->    Dict:
         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 	__magic_name__				(       snake_case ,			unittest.TestCase							):
     UpperCamelCase_  :Any								=			ConvNextImageProcessor if is_vision_available() else None






     def   UpperCAmelCase_							(							self  )->    Optional[Any]:
         UpperCamelCase_      =	ConvNextImageProcessingTester(self  )






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






     def   UpperCAmelCase_							(							self  )->    Any:
         UpperCamelCase_      =	self.image_processing_class(**self.image_processor_dict  )
         self.assertTrue(hasattr(_lowercase		,	"do_resize"  )  )
         self.assertTrue(hasattr(_lowercase		,	"size"  )  )
         self.assertTrue(hasattr(_lowercase		,	"crop_pct"  )  )
         self.assertTrue(hasattr(_lowercase		,	"do_normalize"  )  )
         self.assertTrue(hasattr(_lowercase		,	"image_mean"  )  )
         self.assertTrue(hasattr(_lowercase		,	"image_std"  )  )






     def   UpperCAmelCase_							(							self  )->    List[Any]:
         UpperCamelCase_      =	self.image_processing_class.from_dict(self.image_processor_dict  )
         self.assertEqual(image_processor.size		,	{"shortest_edge": 20}  )

         UpperCamelCase_      =	self.image_processing_class.from_dict(self.image_processor_dict		,	size=42  )
         self.assertEqual(image_processor.size		,	{"shortest_edge": 42}  )






     def   UpperCAmelCase_							(							self  )->    Any:
         pass






     def   UpperCAmelCase_							(							self  )->    str:
         # Initialize image_processing
         UpperCamelCase_      =	self.image_processing_class(**self.image_processor_dict  )
         # create random PIL images
         UpperCamelCase_      =	prepare_image_inputs(self.image_processor_tester		,	equal_resolution=_lowercase  )
         for image in image_inputs:
             self.assertIsInstance(_lowercase		,	Image.Image  )

         # Test not batched input
         UpperCamelCase_      =	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
         UpperCamelCase_      =	image_processing(_lowercase		,	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   UpperCAmelCase_							(							self  )->    int:
         # Initialize image_processing
         UpperCamelCase_      =	self.image_processing_class(**self.image_processor_dict  )
         # create random numpy tensors
         UpperCamelCase_      =	prepare_image_inputs(self.image_processor_tester		,	equal_resolution=_lowercase		,	numpify=_lowercase  )
         for image in image_inputs:
             self.assertIsInstance(_lowercase		,	np.ndarray  )

         # Test not batched input
         UpperCamelCase_      =	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
         UpperCamelCase_      =	image_processing(_lowercase		,	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   UpperCAmelCase_							(							self  )->    List[Any]:
         # Initialize image_processing
         UpperCamelCase_      =	self.image_processing_class(**self.image_processor_dict  )
         # create random PyTorch tensors
         UpperCamelCase_      =	prepare_image_inputs(self.image_processor_tester		,	equal_resolution=_lowercase		,	torchify=_lowercase  )
         for image in image_inputs:
             self.assertIsInstance(_lowercase		,	torch.Tensor  )

         # Test not batched input
         UpperCamelCase_      =	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
         UpperCamelCase_      =	image_processing(_lowercase		,	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  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	float:



    """simple docstring"""






    return -num if num < 0 else num




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	int:



    """simple docstring"""






    if len(SCREAMING_SNAKE_CASE_ ) == 0:
        raise ValueError("abs_min() arg is an empty sequence" )
    UpperCamelCase_      =	x[0]
    for i in x:
        if abs_val(SCREAMING_SNAKE_CASE_ ) < abs_val(SCREAMING_SNAKE_CASE_ ):
            UpperCamelCase_      =	i
    return j




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	int:



    """simple docstring"""






    if len(SCREAMING_SNAKE_CASE_ ) == 0:
        raise ValueError("abs_max() arg is an empty sequence" )
    UpperCamelCase_      =	x[0]
    for i in x:
        if abs(SCREAMING_SNAKE_CASE_ ) > abs(SCREAMING_SNAKE_CASE_ ):
            UpperCamelCase_      =	i
    return j




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	int:



    """simple docstring"""






    if len(SCREAMING_SNAKE_CASE_ ) == 0:
        raise ValueError("abs_max_sort() arg is an empty sequence" )
    return sorted(SCREAMING_SNAKE_CASE_ ,	key=SCREAMING_SNAKE_CASE_ )[-1]




def  lowerCAmelCase(			)->	Any:



    """simple docstring"""






    assert abs_val(0 ) == 0
    assert abs_val(3_4 ) == 3_4
    assert abs_val(-1_0_0_0_0_0_0_0_0_0_0_0 ) == 1_0_0_0_0_0_0_0_0_0_0_0

    UpperCamelCase_      =	[-3, -1, 2, -1_1]
    assert abs_max(SCREAMING_SNAKE_CASE_ ) == -1_1
    assert abs_max_sort(SCREAMING_SNAKE_CASE_ ) == -1_1
    assert abs_min(SCREAMING_SNAKE_CASE_ ) == -1


if __name__ == "__main__":
 import doctest

 doctest.testmod()

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