



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


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ChineseCLIPImageProcessor



class    UpperCAmelCase__(				unittest.TestCase						):

   '''simple docstring'''




   def __init__(    self	:      Union[str, Any]      ,					lowerCAmelCase	:      List[Any]      ,					lowerCAmelCase	:      List[str]=7      ,					lowerCAmelCase	:      Optional[Any]=3      ,					lowerCAmelCase	:      Optional[Any]=18      ,					lowerCAmelCase	:      List[Any]=30      ,					lowerCAmelCase	:      List[str]=4_00      ,					lowerCAmelCase	:      int=True      ,					lowerCAmelCase	:      Union[str, Any]=None      ,					lowerCAmelCase	:      List[Any]=True      ,					lowerCAmelCase	:      int=None      ,					lowerCAmelCase	:      List[str]=True      ,					lowerCAmelCase	:      Tuple=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]      ,					lowerCAmelCase	:      Tuple=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]      ,					lowerCAmelCase	:      List[Any]=True      ,					)						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	size if size is not None else {'height': 2_24, 'width': 2_24}
         lowercase__  =	crop_size if crop_size is not None else {'height': 18, 'width': 18}
         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_convert_rgb


   def       UpperCAmelCase		(    self	:      str)						->					int:




         """simple docstring"""

         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_convert_rgb": self.do_convert_rgb,
         }



   def       UpperCAmelCase		(    self	:      Any      ,					lowerCAmelCase	:      Dict=False      ,					lowerCAmelCase	:      Dict=False      ,					lowerCAmelCase	:      List[Any]=False)						->					Optional[Any]:




         """simple docstring"""

         assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

         if equal_resolution:
               lowercase__  =	[]
               for i in range(self.batch_size):
                     image_inputs.append(
                         np.random.randint(
                             2_55      ,					size=(self.num_channels, self.max_resolution, self.max_resolution)      ,					dtype=np.uinta))
         else:
               lowercase__  =	[]
               for i in range(self.batch_size):
                     lowercase__,						lowercase__  =	np.random.choice(np.arange(self.min_resolution      ,					self.max_resolution)      ,					2)
                     image_inputs.append(np.random.randint(2_55      ,					size=(self.num_channels, width, height)      ,					dtype=np.uinta))

         if not numpify and not torchify:
               # PIL expects the channel dimension as last dimension
               lowercase__  =	[Image.fromarray(np.moveaxis(lowercase_      ,					0      ,					-1)) for x in image_inputs]

         if torchify:
               lowercase__  =	[torch.from_numpy(lowercase_) for x in image_inputs]

         return image_inputs



@require_torch
@require_vision
class    UpperCAmelCase__(				UpperCamelCase__  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	Tuple												=     ChineseCLIPImageProcessor if is_vision_available() else None


   def       UpperCAmelCase		(    self	:      Optional[int])						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	ChineseCLIPImageProcessingTester(self      ,					do_center_crop=lowercase_)


   @property
   def       UpperCAmelCase		(    self	:      Optional[int])						->					List[Any]:




         """simple docstring"""

         return self.image_processor_tester.prepare_image_processor_dict()


   def       UpperCAmelCase		(    self	:      Tuple)						->					Optional[Any]:




         """simple docstring"""

         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'))
         self.assertTrue(hasattr(lowercase_      ,					'do_convert_rgb'))


   def       UpperCAmelCase		(    self	:      str)						->					Tuple:




         """simple docstring"""

         lowercase__  =	self.image_processing_class.from_dict(self.image_processor_dict)
         self.assertEqual(image_processor.size      ,					{'height': 2_24, 'width': 2_24})
         self.assertEqual(image_processor.crop_size      ,					{'height': 18, 'width': 18})

         lowercase__  =	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	:      Dict)						->					Dict:




         """simple docstring"""

         pass


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




         """simple docstring"""

         lowercase__  =	self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         lowercase__  =	self.image_processor_tester.prepare_inputs(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       UpperCAmelCase		(    self	:      int)						->					Tuple:




         """simple docstring"""

         lowercase__  =	self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         lowercase__  =	self.image_processor_tester.prepare_inputs(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       UpperCAmelCase		(    self	:      List[str])						->					Optional[int]:




         """simple docstring"""

         lowercase__  =	self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         lowercase__  =	self.image_processor_tester.prepare_inputs(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'],
             )      ,					)



@require_torch
@require_vision
class    UpperCAmelCase__(				UpperCamelCase__  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	int												=     ChineseCLIPImageProcessor if is_vision_available() else None


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




         """simple docstring"""

         lowercase__  =	ChineseCLIPImageProcessingTester(self      ,					num_channels=4      ,					do_center_crop=lowercase_)
         lowercase__  =	3


   @property
   def       UpperCAmelCase		(    self	:      List[str])						->					Union[str, Any]:




         """simple docstring"""

         return self.image_processor_tester.prepare_image_processor_dict()


   def       UpperCAmelCase		(    self	:      List[str])						->					str:




         """simple docstring"""

         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'))
         self.assertTrue(hasattr(lowercase_      ,					'do_convert_rgb'))


   def       UpperCAmelCase		(    self	:      List[str])						->					Optional[Any]:




         """simple docstring"""

         pass



   def       UpperCAmelCase		(    self	:      str)						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         lowercase__  =	self.image_processor_tester.prepare_inputs(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.expected_encoded_image_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.expected_encoded_image_num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             )      ,					)







'''simple docstring'''

from __future__ import annotations





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

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


if __name__ == "__main__":
  import doctest

  doctest.testmod()



