



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 							_a							(					unittest.TestCase ):

			def __init__(self,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_=7,					SCREAMING_SNAKE_CASE_=3,					SCREAMING_SNAKE_CASE_=18,					SCREAMING_SNAKE_CASE_=30,					SCREAMING_SNAKE_CASE_=400,					SCREAMING_SNAKE_CASE_=True,					SCREAMING_SNAKE_CASE_=None,					SCREAMING_SNAKE_CASE_=True,					SCREAMING_SNAKE_CASE_=None,					SCREAMING_SNAKE_CASE_=True,					)    ->					List[str]:
						UpperCAmelCase_:						List[Any]					=			size if size is not None else {"""shortest_edge""": 20}
						UpperCAmelCase_:						Union[str, Any]					=			crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
						UpperCAmelCase_:						Optional[int]					=			parent
						UpperCAmelCase_:						Optional[int]					=			batch_size
						UpperCAmelCase_:						List[str]					=			num_channels
						UpperCAmelCase_:						Tuple					=			image_size
						UpperCAmelCase_:						List[str]					=			min_resolution
						UpperCAmelCase_:						List[str]					=			max_resolution
						UpperCAmelCase_:						List[str]					=			do_resize
						UpperCAmelCase_:						Optional[int]					=			size
						UpperCAmelCase_:						Tuple					=			do_center_crop
						UpperCAmelCase_:						List[str]					=			crop_size
						UpperCAmelCase_:						int					=			do_flip_channel_order

			def 			__snake_case    (self )    ->					str:
						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 							_a							(					_UpperCAmelCase      ,						unittest.TestCase ):
			A							=     MobileViTImageProcessor if is_vision_available() else None

			def 			__snake_case    (self )    ->					List[Any]:
						UpperCAmelCase_:						Any					=			MobileViTImageProcessingTester(self )

			@property
			def 			__snake_case    (self )    ->					int:
						return self.image_processor_tester.prepare_image_processor_dict()

			def 			__snake_case    (self )    ->					str:
						UpperCAmelCase_:						Dict					=			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__,					"""center_crop""" ) )
						self.assertTrue(hasattr(a__,					"""do_flip_channel_order""" ) )

			def 			__snake_case    (self )    ->					List[str]:
						UpperCAmelCase_:						List[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_:						Tuple					=			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 			__snake_case    (self )    ->					Dict:
						pass

			def 			__snake_case    (self )    ->					Optional[int]:
						UpperCAmelCase_:						List[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_:						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_:						Tuple					=			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 			__snake_case    (self )    ->					Any:
						UpperCAmelCase_:						Optional[int]					=			self.image_processing_class(**self.image_processor_dict )
						# create random numpy tensors
						UpperCAmelCase_:						Union[str, Any]					=			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_:						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_:						Union[str, 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 			__snake_case    (self )    ->					Dict:
						UpperCAmelCase_:						Tuple					=			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"""],
						    ),					)













from __future__ import annotations






def lowerCamelCase__    (							snake_case_	:  list[int]       )      ->				list[int]:  # This function is recursive
   __snake_case							       =   len(snake_case_       )
   # If the array contains only one element, we return it (it's the stop condition of
   # recursion)
   if array_length <= 1:
      return array
      # Else
   __snake_case							       =   array[0]
   __snake_case							       =   False
   __snake_case							       =   1
   __snake_case							       =   []
   while not is_found and i < array_length:
      if array[i] < pivot:
         __snake_case							       =   True
         __snake_case							       =   [element for element in array[i:] if element >= array[i]]
         __snake_case							       =   longest_subsequence(snake_case_       )
         if len(snake_case_       ) > len(snake_case_       ):
            __snake_case							       =   temp_array
      else:
         i += 1

   __snake_case							       =   [element for element in array[1:] if element >= pivot]
   __snake_case							       =   [pivot, *longest_subsequence(snake_case_       )]
   if len(snake_case_       ) > len(snake_case_       ):
      return temp_array
   else:
      return longest_subseq


if __name__ == "__main__":
    import doctest

    doctest.testmod()

