





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 MobileViTImageProcessor






class        snake_case_    (		unittest.TestCase						):






  def __init__(							self			:					Any       ,  _snake_case			:					Optional[int]       ,  _snake_case			:					Any=7       ,  _snake_case			:					Dict=3       ,  _snake_case			:					str=18       ,  _snake_case			:					Any=30       ,  _snake_case			:					str=400       ,  _snake_case			:					List[str]=True       ,  _snake_case			:					Optional[Any]=None       ,  _snake_case			:					str=True       ,  _snake_case			:					Optional[int]=None       ,  _snake_case			:					Union[str, Any]=True       ,  )->Union[str, Any]:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					Dict      )->Any:






   '''simple docstring'''

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






@require_torch
@require_vision
class        snake_case_    (		__lowercase					,unittest.TestCase						):
  A_      							=   MobileViTImageProcessor if is_vision_available() else None






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






   '''simple docstring'''

   __lowerCAmelCase					:	Any      =       MobileViTImageProcessingTester(self      )






  @property
  def 				UpperCAmelCase__						(							self			:					List[Any]      )->Dict:






   '''simple docstring'''

   return self.image_processor_tester.prepare_image_processor_dict()






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






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       self.image_processing_class(**self.image_processor_dict      )
   self.assertTrue(hasattr(_snake_case       ,  """do_resize"""      )      )
   self.assertTrue(hasattr(_snake_case       ,  """size"""      )      )
   self.assertTrue(hasattr(_snake_case       ,  """do_center_crop"""      )      )
   self.assertTrue(hasattr(_snake_case       ,  """center_crop"""      )      )
   self.assertTrue(hasattr(_snake_case       ,  """do_flip_channel_order"""      )      )






  def 				UpperCAmelCase__						(							self			:					int      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       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}      )

   __lowerCAmelCase					:	Optional[int]      =       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 				UpperCAmelCase__						(							self			:					str      )->str:






   '''simple docstring'''

   pass






  def 				UpperCAmelCase__						(							self			:					Optional[int]      )->int:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       self.image_processing_class(**self.image_processor_dict      )
   # create random PIL images
   __lowerCAmelCase					:	Optional[int]      =       prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_snake_case      )
   for image in image_inputs:
    self.assertIsInstance(_snake_case       ,  Image.Image      )

   # Test not batched input
   __lowerCAmelCase					:	int      =       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					:	str      =       image_processing(_snake_case       ,  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 				UpperCAmelCase__						(							self			:					Any      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       self.image_processing_class(**self.image_processor_dict      )
   # create random numpy tensors
   __lowerCAmelCase					:	Dict      =       prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_snake_case       ,  numpify=_snake_case      )
   for image in image_inputs:
    self.assertIsInstance(_snake_case       ,  np.ndarray      )

   # Test not batched input
   __lowerCAmelCase					:	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
   __lowerCAmelCase					:	List[str]      =       image_processing(_snake_case       ,  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 				UpperCAmelCase__						(							self			:					Union[str, Any]      )->Any:






   '''simple docstring'''

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

   # Test not batched input
   __lowerCAmelCase					:	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
   __lowerCAmelCase					:	Optional[Any]      =       image_processing(_snake_case       ,  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"""],
       )       ,  )






import random






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:Dict   )      ->   tuple:
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	List[str]      =       [], [], []
 for element in data:
  if element < pivot:
   less.append(SCREAMING_SNAKE_CASE   )
  elif element > pivot:
   greater.append(SCREAMING_SNAKE_CASE   )
  else:
   equal.append(SCREAMING_SNAKE_CASE   )
 return less, equal, greater







def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:int   )      ->   Dict:
 # index = len(items) // 2 when trying to find the median
 #   (value of index when items is sorted)

 # invalid input
 if index >= len(SCREAMING_SNAKE_CASE   ) or index < 0:
  return None

 __lowerCAmelCase					:	Union[str, Any]      =       items[random.randint(0   ,   len(SCREAMING_SNAKE_CASE   ) - 1   )]
 __lowerCAmelCase					:	int      =       0
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	Tuple      =       _partition(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[str]      =       len(SCREAMING_SNAKE_CASE   )

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