


'''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 MobileNetVaImageProcessor






class 		lowerCAmelCase__	(	unittest.TestCase		):




			def __init__(						self     :   Any		,     lowerCamelCase__     :   Union[str, Any]		,     lowerCamelCase__     :   Tuple=7		,     lowerCamelCase__     :   Tuple=3		,     lowerCamelCase__     :   Optional[int]=18		,     lowerCamelCase__     :   int=30		,     lowerCamelCase__     :   Any=4_00		,     lowerCamelCase__     :   Optional[Any]=True		,     lowerCamelCase__     :   List[Any]=None		,     lowerCamelCase__     :   Union[str, Any]=True		,     lowerCamelCase__     :   Any=None		,     )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					size if size is not None else {"shortest_edge": 20}
					_UpperCAmelCase						:				Optional[Any]	    =					crop_size if crop_size is not None else {"height": 18, "width": 18}
					_UpperCAmelCase						:				int	    =					parent
					_UpperCAmelCase						:				Optional[int]	    =					batch_size
					_UpperCAmelCase						:				List[str]	    =					num_channels
					_UpperCAmelCase						:				List[Any]	    =					image_size
					_UpperCAmelCase						:				Union[str, Any]	    =					min_resolution
					_UpperCAmelCase						:				int	    =					max_resolution
					_UpperCAmelCase						:				Dict	    =					do_resize
					_UpperCAmelCase						:				Union[str, Any]	    =					size
					_UpperCAmelCase						:				List[str]	    =					do_center_crop
					_UpperCAmelCase						:				Any	    =					crop_size






			def 							lowerCAmelCase__			(						self     :   Tuple       )      ->Any:



					'''simple docstring'''







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






@require_torch
@require_vision
class 		lowerCAmelCase__	(	__lowerCamelCase			,				unittest.TestCase		):
			lowerCAmelCase       :		str				      =   MobileNetVaImageProcessor if is_vision_available() else None




			def 							lowerCAmelCase__			(						self     :   str       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					MobileNetVaImageProcessingTester(self       )




			@property
			def 							lowerCAmelCase__			(						self     :   int       )      ->Dict:



					'''simple docstring'''







					return self.image_processor_tester.prepare_image_processor_dict()




			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					self.image_processing_class(**self.image_processor_dict       )
					self.assertTrue(hasattr(__A		,     "do_resize"       )       )
					self.assertTrue(hasattr(__A		,     "size"       )       )
					self.assertTrue(hasattr(__A		,     "do_center_crop"       )       )
					self.assertTrue(hasattr(__A		,     "crop_size"       )       )




			def 							lowerCAmelCase__			(						self     :   Dict       )      ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					self.image_processing_class.from_dict(self.image_processor_dict       )
					self.assertEqual(image_processor.size		,     {"shortest_edge": 20}       )
					self.assertEqual(image_processor.crop_size		,     {"height": 18, "width": 18}       )

					_UpperCAmelCase						:				Union[str, Any]	    =					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 							lowerCAmelCase__			(						self     :   List[str]       )      ->Optional[int]:



					'''simple docstring'''







					pass




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]       )      ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				str	    =					self.image_processing_class(**self.image_processor_dict       )
					# create random PIL images
					_UpperCAmelCase						:				List[str]	    =					prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__A       )
					for image in image_inputs:
							self.assertIsInstance(__A		,     Image.Image       )

					# Test not batched input
					_UpperCAmelCase						:				Union[str, 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
					_UpperCAmelCase						:				List[str]	    =					image_processing(__A		,     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 							lowerCAmelCase__			(						self     :   Union[str, Any]       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				str	    =					self.image_processing_class(**self.image_processor_dict       )
					# create random numpy tensors
					_UpperCAmelCase						:				Tuple	    =					prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__A		,     numpify=__A       )
					for image in image_inputs:
							self.assertIsInstance(__A		,     np.ndarray       )

					# Test not batched input
					_UpperCAmelCase						:				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
					_UpperCAmelCase						:				Any	    =					image_processing(__A		,     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 							lowerCAmelCase__			(						self     :   Union[str, Any]       )      ->Any:



					'''simple docstring'''







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

					# Test not batched input
					_UpperCAmelCase						:				Tuple	    =					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
					_UpperCAmelCase						:				int	    =					image_processing(__A		,     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'''
import random
def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  Optional[Any]     ) ->      tuple:



       """simple docstring"""






       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  [], [], []
       for element in data:
              if element < pivot:
                     less.append(__lowercase     )
              elif element > pivot:
                     greater.append(__lowercase     )
              else:
                     equal.append(__lowercase     )
       return less, equal, greater


def 				lowercase__   (   __lowercase						:  list				,  __lowercase						:  int     ) ->      Dict:



       """simple docstring"""






       if index >= len(__lowercase     ) or index < 0:
              return None

       __UpperCamelCase       =  items[random.randint(0				,  len(__lowercase     ) - 1     )]
       __UpperCamelCase       =  0
       __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  _partition(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  len(__lowercase     )
       __UpperCamelCase       =  len(__lowercase     )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(__lowercase				,  __lowercase     )
       # must be in larger
       else:
              return quick_select(__lowercase				,  index - (m + count)     )
