
'''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 ViTImageProcessor


class  UpperCamelCase__  (    unittest.TestCase):






 def __init__(			self  :Dict	,				_A  :int	,				_A  :Optional[Any]=7	,				_A  :int=3	,				_A  :Optional[Any]=18	,				_A  :Optional[int]=30	,				_A  :Any=400	,				_A  :Optional[Any]=True	,				_A  :Optional[int]=None	,				_A  :Any=True	,				_A  :Optional[Any]=[0.5, 0.5, 0.5]	,				_A  :int=[0.5, 0.5, 0.5]	,				)				->			Dict:







       '''simple docstring'''



       __A       	=			size if size is not None else {'height': 18, 'width': 18}
       __A       	=			parent
       __A       	=			batch_size
       __A       	=			num_channels
       __A       	=			image_size
       __A       	=			min_resolution
       __A       	=			max_resolution
       __A       	=			do_resize
       __A       	=			size
       __A       	=			do_normalize
       __A       	=			image_mean
       __A       	=			image_std






 def 	lowercase_						(			self  :Tuple			)				->			Tuple:







       '''simple docstring'''



       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }


@require_torch
@require_vision
class  UpperCamelCase__  (    SCREAMING_SNAKE_CASE      ,     unittest.TestCase):
 UpperCAmelCase__    :    str										=					ViTImageProcessor if is_vision_available() else None






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







       '''simple docstring'''



       __A       	=			ViTImageProcessingTester(self			)






 @property
 def 	lowercase_						(			self  :List[str]			)				->			str:







       '''simple docstring'''



       return self.image_processor_tester.prepare_image_processor_dict()






 def 	lowercase_						(			self  :Tuple			)				->			Optional[int]:







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       self.assertTrue(hasattr(_A	,				'image_mean'			)			)
       self.assertTrue(hasattr(_A	,				'image_std'			)			)
       self.assertTrue(hasattr(_A	,				'do_normalize'			)			)
       self.assertTrue(hasattr(_A	,				'do_resize'			)			)
       self.assertTrue(hasattr(_A	,				'size'			)			)






 def 	lowercase_						(			self  :Any			)				->			Dict:







       '''simple docstring'''



       __A       	=			self.image_processing_class.from_dict(self.image_processor_dict			)
       self.assertEqual(image_processor.size	,				{'height': 18, 'width': 18}			)

       __A       	=			self.image_processing_class.from_dict(self.image_processor_dict	,				size=42			)
       self.assertEqual(image_processor.size	,				{'height': 42, 'width': 42}			)






 def 	lowercase_						(			self  :Tuple			)				->			int:







       '''simple docstring'''



       pass






 def 	lowercase_						(			self  :List[str]			)				->			Dict:







       '''simple docstring'''



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

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

       # Test batched
       __A       	=			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.size['height'],
               self.image_processor_tester.size['width'],
           )	,				)






 def 	lowercase_						(			self  :str			)				->			str:







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       # create random numpy tensors
       __A       	=			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
       __A       	=			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.size['height'],
               self.image_processor_tester.size['width'],
           )	,				)

       # Test batched
       __A       	=			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.size['height'],
               self.image_processor_tester.size['width'],
           )	,				)






 def 	lowercase_						(			self  :List[str]			)				->			Dict:







       '''simple docstring'''



       __A       	=			self.image_processing_class(**self.image_processor_dict			)
       # create random PyTorch tensors
       __A       	=			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
       __A       	=			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.size['height'],
               self.image_processor_tester.size['width'],
           )	,				)

       # Test batched
       __A       	=			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.size['height'],
               self.image_processor_tester.size['width'],
           )	,				)








'''simple docstring'''





from .stack import Stack





def        snake_case							(		UpperCAmelCase	)->			bool:







      """simple docstring"""
      __A       	=			Stack()
      __A       	=			{'(': ')', '[': ']', '{': '}'}
      for bracket in parentheses:
            if bracket in bracket_pairs:
                  stack.push(UpperCAmelCase	)
            elif bracket in (")", "]", "}"):
                  if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                        return False
      return stack.is_empty()


if __name__ == "__main__":
 from doctest import testmod

 testmod()

 a__		:   int            =       ["((()))", "((())", "(()))"]
 print("Balanced parentheses demonstration:\n")
 for example in examples:
  a__		:   int            =       "" if balanced_parentheses(example) else "not "
  print(f'''{example} is {not_str}balanced''')






