"""simple docstring"""





_UpperCamelCase     :		str											=					"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"





def        a_  (   _lowerCAmelCase						:				bytes  ):





       '''simple docstring'''

       if not isinstance(_lowerCAmelCase     ,				_lowerCAmelCase  ):
              lowercase__    :	Optional[int]     					=			f"""a bytes-like object is required, not '{data.__class__.__name__}'"""
              raise TypeError(_lowerCAmelCase  )

       lowercase__    :	str     					=			''.join(bin(_lowerCAmelCase  )[2:].zfill(8  ) for byte in data  )

       lowercase__    :	str     					=			len(_lowerCAmelCase  ) % 6 != 0

       if padding_needed:
              # The padding that will be added later
              lowercase__    :	Dict     					=			B'=' * ((6 - len(_lowerCAmelCase  ) % 6) // 2)

              # Append binary_stream with arbitrary binary digits (0's by default) to make its
              # length a multiple of 6.
              binary_stream += "0" * (6 - len(_lowerCAmelCase  ) % 6)
       else:
              lowercase__    :	str     					=			B''

       # Encode every 6 binary digits to their corresponding Base64 character
       return (
           "".join(
               B64_CHARSET[int(binary_stream[index : index + 6]     ,				2  )]
               for index in range(0     ,				len(_lowerCAmelCase  )     ,				6  )  ).encode()
           + padding
       )





def        a_  (   _lowerCAmelCase						:				str  ):





       '''simple docstring'''

       if not isinstance(_lowerCAmelCase     ,				_lowerCAmelCase  ) and not isinstance(_lowerCAmelCase     ,				_lowerCAmelCase  ):
              lowercase__    :	str     					=			(
                  'argument should be a bytes-like object or ASCII string, '
                  f"""not '{encoded_data.__class__.__name__}'"""
              )
              raise TypeError(_lowerCAmelCase  )

       # In case encoded_data is a bytes-like object, make sure it contains only
       # ASCII characters so we convert it to a string object
       if isinstance(_lowerCAmelCase     ,				_lowerCAmelCase  ):
              try:
                     lowercase__    :	int     					=			encoded_data.decode('utf-8'  )
              except UnicodeDecodeError:
                     raise ValueError('base64 encoded data should only contain ASCII characters'  )

       lowercase__    :	List[Any]     					=			encoded_data.count('='  )

       # Check if the encoded string contains non base64 characters
       if padding:
              assert all(
                  char in B64_CHARSET for char in encoded_data[:-padding]  ), "Invalid base64 character(s) found."
       else:
              assert all(
                  char in B64_CHARSET for char in encoded_data  ), "Invalid base64 character(s) found."

       # Check the padding
       assert len(_lowerCAmelCase  ) % 4 == 0 and padding < 3, "Incorrect padding"

       if padding:
              # Remove padding if there is one
              lowercase__    :	Tuple     					=			encoded_data[:-padding]

              lowercase__    :	Optional[int]     					=			''.join(
                  bin(B64_CHARSET.index(_lowerCAmelCase  )  )[2:].zfill(6  ) for char in encoded_data  )[: -padding * 2]
       else:
              lowercase__    :	List[str]     					=			''.join(
                  bin(B64_CHARSET.index(_lowerCAmelCase  )  )[2:].zfill(6  ) for char in encoded_data  )

       lowercase__    :	Any     					=			[
           int(binary_stream[index : index + 8]     ,				2  )
           for index in range(0     ,				len(_lowerCAmelCase  )     ,				8  )
       ]

       return bytes(_lowerCAmelCase  )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


"""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 MobileViTImageProcessor


class   UpperCAmelCase_			(					unittest.TestCase):


      def __init__(     self	,    a	,    a=7	,    a=3	,    a=1_8	,    a=3_0	,    a=4_0_0	,    a=True	,    a=None	,    a=True	,    a=None	,    a=True	,    ) ->      List[str]:
             lowercase__    :	Tuple     					=			size if size is not None else {'shortest_edge': 2_0}
             lowercase__    :	Union[str, Any]     					=			crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
             lowercase__    :	Optional[int]     					=			parent
             lowercase__    :	Optional[int]     					=			batch_size
             lowercase__    :	str     					=			num_channels
             lowercase__    :	Any     					=			image_size
             lowercase__    :	Optional[Any]     					=			min_resolution
             lowercase__    :	int     					=			max_resolution
             lowercase__    :	List[Any]     					=			do_resize
             lowercase__    :	List[str]     					=			size
             lowercase__    :	str     					=			do_center_crop
             lowercase__    :	List[Any]     					=			crop_size
             lowercase__    :	Union[str, Any]     					=			do_flip_channel_order


      def 	_UpperCAmelCase (     self							) ->      int:
             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   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Optional[Any]										=						MobileViTImageProcessor if is_vision_available() else None


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Tuple     					=			MobileViTImageProcessingTester(self							)


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


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	List[str]     					=			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 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Optional[int]     					=			self.image_processing_class.from_dict(self.image_processor_dict							)
             self.assertEqual(image_processor.size	,    {'shortest_edge': 2_0}							)
             self.assertEqual(image_processor.crop_size	,    {'height': 1_8, 'width': 1_8}							)

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


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             pass


      def 	_UpperCAmelCase (     self							) ->      str:
             # Initialize image_processing
             lowercase__    :	Any     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PIL images
             lowercase__    :	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
             lowercase__    :	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
             lowercase__    :	List[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 	_UpperCAmelCase (     self							) ->      Tuple:
             # Initialize image_processing
             lowercase__    :	Dict     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random numpy tensors
             lowercase__    :	Tuple     					=			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
             lowercase__    :	List[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
             lowercase__    :	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 	_UpperCAmelCase (     self							) ->      Dict:
             # Initialize image_processing
             lowercase__    :	str     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PyTorch tensors
             lowercase__    :	Optional[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
             lowercase__    :	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
             lowercase__    :	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'],
                 )	,    )

