

'''simple docstring'''






import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor






class 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  def __init__(      self   :     Dict					,lowercase__   :     Any					,lowercase__   :     int=7					,lowercase__   :     Dict=3					,lowercase__   :     Tuple=1_8					,lowercase__   :     List[Any]=3_0					,lowercase__   :     str=4_0_0					,lowercase__   :     List[str]=True					,lowercase__   :     Union[str, Any]=None					,lowercase__   :     Dict=True					,lowercase__   :     Any=None					,lowercase__   :     int=True					,lowercase__   :     Any=[0.5, 0.5, 0.5]					,lowercase__   :     int=[0.5, 0.5, 0.5]					,lowercase__   :     List[str]=False					,):
   __lowercase							  = size if size is not None else {'''height''': 2_0, '''width''': 2_0}
   __lowercase							  = crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
   __lowercase							  = parent
   __lowercase							  = batch_size
   __lowercase							  = num_channels
   __lowercase							  = image_size
   __lowercase							  = min_resolution
   __lowercase							  = max_resolution
   __lowercase							  = do_resize
   __lowercase							  = size
   __lowercase							  = do_center_crop
   __lowercase							  = crop_size
   __lowercase							  = do_normalize
   __lowercase							  = image_mean
   __lowercase							  = image_std
   __lowercase							  = do_reduce_labels






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   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_reduce_labels": self.do_reduce_labels,
   }


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = load_dataset('''hf-internal-testing/fixtures_ade20k'''     ,				split='''test'''      )

 __lowercase							  = Image.open(dataset[0]['''file''']      )
 __lowercase							  = Image.open(dataset[1]['''file''']      )

 return image, map


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = load_dataset('''hf-internal-testing/fixtures_ade20k'''     ,				split='''test'''      )

 __lowercase							  = Image.open(ds[0]['''file''']      )
 __lowercase							  = Image.open(ds[1]['''file''']      )
 __lowercase							  = Image.open(ds[2]['''file''']      )
 __lowercase							  = Image.open(ds[3]['''file''']      )

 return [imagea, imagea], [mapa, mapa]






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




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  int							=			BeitImageProcessor if is_vision_available() else None






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   __lowercase							  = BeitImageProcessingTester(self			)






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   return self.image_processor_tester.prepare_image_processor_dict()






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = self.image_processing_class(**self.image_processor_dict			)
   self.assertTrue(hasattr(lowercase__					,'''do_resize'''			)			)
   self.assertTrue(hasattr(lowercase__					,'''size'''			)			)
   self.assertTrue(hasattr(lowercase__					,'''do_center_crop'''			)			)
   self.assertTrue(hasattr(lowercase__					,'''center_crop'''			)			)
   self.assertTrue(hasattr(lowercase__					,'''do_normalize'''			)			)
   self.assertTrue(hasattr(lowercase__					,'''image_mean'''			)			)
   self.assertTrue(hasattr(lowercase__					,'''image_std'''			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = self.image_processing_class.from_dict(self.image_processor_dict			)
   self.assertEqual(image_processor.size					,{'''height''': 2_0, '''width''': 2_0}			)
   self.assertEqual(image_processor.crop_size					,{'''height''': 1_8, '''width''': 1_8}			)
   self.assertEqual(image_processor.do_reduce_labels					,lowercase__			)

   __lowercase							  = self.image_processing_class.from_dict(
       self.image_processor_dict					,size=4_2					,crop_size=8_4					,reduce_labels=lowercase__			)
   self.assertEqual(image_processor.size					,{'''height''': 4_2, '''width''': 4_2}			)
   self.assertEqual(image_processor.crop_size					,{'''height''': 8_4, '''width''': 8_4}			)
   self.assertEqual(image_processor.do_reduce_labels					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   pass






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   # Initialize image_processing
   __lowercase							  = self.image_processing_class(**self.image_processor_dict			)
   # create random PIL images
   __lowercase							  = prepare_image_inputs(self.image_processor_tester					,equal_resolution=lowercase__			)
   for image in image_inputs:
    self.assertIsInstance(lowercase__					,Image.Image			)

   # Test not batched input
   __lowercase							  = 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
   __lowercase							  = 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.crop_size['''height'''],
           self.image_processor_tester.crop_size['''width'''],
       )					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   # Initialize image_processing
   __lowercase							  = self.image_processing_class(**self.image_processor_dict			)
   # create random numpy tensors
   __lowercase							  = 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
   __lowercase							  = 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
   __lowercase							  = 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.crop_size['''height'''],
           self.image_processor_tester.crop_size['''width'''],
       )					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   # Initialize image_processing
   __lowercase							  = self.image_processing_class(**self.image_processor_dict			)
   # create random PyTorch tensors
   __lowercase							  = 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
   __lowercase							  = 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
   __lowercase							  = 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.crop_size['''height'''],
           self.image_processor_tester.crop_size['''width'''],
       )					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   # Initialize image_processing
   __lowercase							  = self.image_processing_class(**self.image_processor_dict			)
   # create random PyTorch tensors
   __lowercase							  = prepare_image_inputs(self.image_processor_tester					,equal_resolution=lowercase__					,torchify=lowercase__			)
   __lowercase							  = []
   for image in image_inputs:
    self.assertIsInstance(lowercase__					,torch.Tensor			)
    maps.append(torch.zeros(image.shape[-2:]			).long()			)

   # Test not batched input
   __lowercase							  = image_processing(image_inputs[0]					,maps[0]					,return_tensors='''pt'''			)
   self.assertEqual(
       encoding['''pixel_values'''].shape					,(
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size['''height'''],
           self.image_processor_tester.crop_size['''width'''],
       )					,)
   self.assertEqual(
       encoding['''labels'''].shape					,(
           1,
           self.image_processor_tester.crop_size['''height'''],
           self.image_processor_tester.crop_size['''width'''],
       )					,)
   self.assertEqual(encoding['''labels'''].dtype					,torch.long			)
   self.assertTrue(encoding['''labels'''].min().item() >= 0			)
   self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5			)

   # Test batched
   __lowercase							  = image_processing(lowercase__					,lowercase__					,return_tensors='''pt'''			)
   self.assertEqual(
       encoding['''pixel_values'''].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'''],
       )					,)
   self.assertEqual(
       encoding['''labels'''].shape					,(
           self.image_processor_tester.batch_size,
           self.image_processor_tester.crop_size['''height'''],
           self.image_processor_tester.crop_size['''width'''],
       )					,)
   self.assertEqual(encoding['''labels'''].dtype					,torch.long			)
   self.assertTrue(encoding['''labels'''].min().item() >= 0			)
   self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5			)

   # Test not batched input (PIL images)
   __lowercase  ,	__lowercase							  = prepare_semantic_single_inputs()

   __lowercase							  = image_processing(lowercase__					,lowercase__					,return_tensors='''pt'''			)
   self.assertEqual(
       encoding['''pixel_values'''].shape					,(
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size['''height'''],
           self.image_processor_tester.crop_size['''width'''],
       )					,)
   self.assertEqual(
       encoding['''labels'''].shape					,(
           1,
           self.image_processor_tester.crop_size['''height'''],
           self.image_processor_tester.crop_size['''width'''],
       )					,)
   self.assertEqual(encoding['''labels'''].dtype					,torch.long			)
   self.assertTrue(encoding['''labels'''].min().item() >= 0			)
   self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5			)

   # Test batched input (PIL images)
   __lowercase  ,	__lowercase							  = prepare_semantic_batch_inputs()

   __lowercase							  = image_processing(lowercase__					,lowercase__					,return_tensors='''pt'''			)
   self.assertEqual(
       encoding['''pixel_values'''].shape					,(
           2,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size['''height'''],
           self.image_processor_tester.crop_size['''width'''],
       )					,)
   self.assertEqual(
       encoding['''labels'''].shape					,(
           2,
           self.image_processor_tester.crop_size['''height'''],
           self.image_processor_tester.crop_size['''width'''],
       )					,)
   self.assertEqual(encoding['''labels'''].dtype					,torch.long			)
   self.assertTrue(encoding['''labels'''].min().item() >= 0			)
   self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   # Initialize image_processing
   __lowercase							  = self.image_processing_class(**self.image_processor_dict			)

   # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
   __lowercase  ,	__lowercase							  = prepare_semantic_single_inputs()
   __lowercase							  = image_processing(lowercase__					,lowercase__					,return_tensors='''pt'''			)
   self.assertTrue(encoding['''labels'''].min().item() >= 0			)
   self.assertTrue(encoding['''labels'''].max().item() <= 1_5_0			)

   __lowercase							  = True
   __lowercase							  = image_processing(lowercase__					,lowercase__					,return_tensors='''pt'''			)
   self.assertTrue(encoding['''labels'''].min().item() >= 0			)
   self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5			)








'''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)     )
