



'''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 DeiTImageProcessor



class       snake_case__		(  unittest.TestCase							):
 def __init__(					self		:				Tuple	,			__a		:				Union[str, Any]	,			__a		:				Optional[Any]=7	,			__a		:				Optional[Any]=3	,			__a		:				List[str]=18	,			__a		:				Optional[Any]=30	,			__a		:				List[Any]=400	,			__a		:				Optional[int]=True	,			__a		:				Union[str, Any]=None	,			__a		:				Any=True	,			__a		:				str=None	,			__a		:				List[Any]=True	,			__a		:				str=[0.5, 0.5, 0.5]	,			__a		:				Union[str, Any]=[0.5, 0.5, 0.5]	,			)	->					List[str]:







       '''simple docstring'''
       __snake_case      :					str						=  size if size is not None else {'''height''': 20, '''width''': 20}
       __snake_case      :					Any						=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

       __snake_case      :					Dict						=  parent
       __snake_case      :					Optional[Any]						=  batch_size
       __snake_case      :					Tuple						=  num_channels
       __snake_case      :					str						=  image_size
       __snake_case      :					int						=  min_resolution
       __snake_case      :					int						=  max_resolution
       __snake_case      :					Optional[int]						=  do_resize
       __snake_case      :					Optional[Any]						=  size
       __snake_case      :					Union[str, Any]						=  do_center_crop
       __snake_case      :					List[str]						=  crop_size
       __snake_case      :					Optional[int]						=  do_normalize
       __snake_case      :					Tuple						=  image_mean
       __snake_case      :					Any						=  image_std





 def A_					(					self		:				Union[str, Any]   )	->					List[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_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }



@require_torch
@require_vision
class       snake_case__		(  _lowerCAmelCase   ,			unittest.TestCase							):
 A__							=      DeiTImageProcessor if is_vision_available() else None
 A__							=      True
 def A_					(					self		:				Dict   )	->					Tuple:







       '''simple docstring'''
       __snake_case      :					Any						=  DeiTImageProcessingTester(self   )
 @property
 def A_					(					self		:				List[str]   )	->					List[str]:







       '''simple docstring'''
       return self.image_processor_tester.prepare_image_processor_dict()
 def A_					(					self		:				Any   )	->					Union[str, Any]:







       '''simple docstring'''
       __snake_case      :					Tuple						=  self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(lowerCAmelCase__	,			'do_resize'   )   )
       self.assertTrue(hasattr(lowerCAmelCase__	,			'size'   )   )
       self.assertTrue(hasattr(lowerCAmelCase__	,			'do_center_crop'   )   )
       self.assertTrue(hasattr(lowerCAmelCase__	,			'center_crop'   )   )
       self.assertTrue(hasattr(lowerCAmelCase__	,			'do_normalize'   )   )
       self.assertTrue(hasattr(lowerCAmelCase__	,			'image_mean'   )   )
       self.assertTrue(hasattr(lowerCAmelCase__	,			'image_std'   )   )
 def A_					(					self		:				Dict   )	->					List[Any]:







       '''simple docstring'''
       __snake_case      :					Any						=  self.image_processing_class.from_dict(self.image_processor_dict   )
       self.assertEqual(image_processor.size	,			{'height': 20, 'width': 20}   )
       self.assertEqual(image_processor.crop_size	,			{'height': 18, 'width': 18}   )

       __snake_case      :					Dict						=  self.image_processing_class.from_dict(self.image_processor_dict	,			size=42	,			crop_size=84   )
       self.assertEqual(image_processor.size	,			{'height': 42, 'width': 42}   )
       self.assertEqual(image_processor.crop_size	,			{'height': 84, 'width': 84}   )
 def A_					(					self		:				int   )	->					Tuple:







       '''simple docstring'''
       pass
 def A_					(					self		:				List[str]   )	->					Dict:







       '''simple docstring'''
       __snake_case      :					List[Any]						=  self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       __snake_case      :					Optional[int]						=  prepare_image_inputs(self.image_processor_tester	,			equal_resolution=lowerCAmelCase__   )
       for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase__	,			Image.Image   )

       # Test not batched input
       __snake_case      :					Optional[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
       __snake_case      :					List[str]						=  image_processing(lowerCAmelCase__	,			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 A_					(					self		:				List[Any]   )	->					Tuple:







       '''simple docstring'''
       __snake_case      :					int						=  self.image_processing_class(**self.image_processor_dict   )
       # create random numpy tensors
       __snake_case      :					Tuple						=  prepare_image_inputs(self.image_processor_tester	,			equal_resolution=lowerCAmelCase__	,			numpify=lowerCAmelCase__   )
       for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase__	,			np.ndarray   )

       # Test not batched input
       __snake_case      :					Dict						=  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
       __snake_case      :					Optional[Any]						=  image_processing(lowerCAmelCase__	,			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 A_					(					self		:				Optional[Any]   )	->					Tuple:







       '''simple docstring'''
       __snake_case      :					Optional[int]						=  self.image_processing_class(**self.image_processor_dict   )
       # create random PyTorch tensors
       __snake_case      :					Union[str, Any]						=  prepare_image_inputs(self.image_processor_tester	,			equal_resolution=lowerCAmelCase__	,			torchify=lowerCAmelCase__   )
       for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase__	,			torch.Tensor   )

       # Test not batched input
       __snake_case      :					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
       __snake_case      :					int						=  image_processing(lowerCAmelCase__	,			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'],
           )	,			)










"""simple docstring"""




from __future__ import annotations





def        a_				(							_lowercase   ):
						assert (
						    isinstance(_lowercase       ,						_lowercase   ) and mask > 0
						), F"""mask needs to be positive integer, your input {mask}"""
						_UpperCamelCase			:					Union[str, Any]     =    []
						_UpperCamelCase			:					str     =    mask

						while submask:
												all_submasks.append(_lowercase   )
												_UpperCamelCase			:					int     =    (submask - 1) & mask

						return all_submasks


if __name__ == "__main__":
						import doctest

						doctest.testmod()



